Available via license: CC BY
Content may be subject to copyright.
Journal of
Risk and Financial
Management
Article
Crisis and the Role of Money in the Real and Financial
Economies—An Innovative Approach to Monetary Stimulus
Richard Simmons 1, Paolo Dini 2,* , Nigel Culkin 1and Giuseppe Littera 3
Citation: Simmons, Richard, Paolo
Dini, Nigel Culkin, and Giuseppe
Littera. 2021. Crisis and the Role of
Money in the Real and Financial
Economies—An Innovative
Approach to Monetary Stimulus.
Journal of Risk and Financial
Management 14: 129. https://
doi.org/10.3390/jrfm14030129
Academic Editor: Georgina M.
Gomez
Received: 28 February 2021
Accepted: 16 March 2021
Published: 20 March 2021
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1Enterprise & Business Development, University of Hertfordshire, Enterprise Hub, Hatfield,
Hertfordshire AL10 9AB, UK; r.simmons3@herts.ac.uk (R.S.); n.culkin@herts.ac.uk (N.C.)
2Department of Media and Communications, London School of Economics and Political Science,
London WC2A 2AE, UK
3Sardex S.p.A., Viale Sant’Ignazio 16, 09038 Serramanna, Italy; giuseppe.littera@sardex.net
*Correspondence: p.dini@lse.ac.uk
Abstract:
‘Financial crisis’ is sometimes regarded as synonymous with ‘economic crisis’, but this is an
oversimplification and risks missing the feedback loops between the financial and real economies. In
this paper, the role of money is revisited in the context of distinguishing the real economy from the
financial economy. A theoretical framework is developed to explain how endogenous (bank credit)
and central bank exogenous (quantitative easing, QE) money creation feed into the real and financial
economies. It looks at how the velocity of monetary circulation varies between the two economies
and across asset types within the financial economy. Monetary transmission mechanisms are set into a
framework that helps explain how QE stimulus risks combining asset price bubbles with poor growth
in the real economy. The real economy transmission mechanism of ‘helicopter money’ is given context,
enabling an assessment of the efficacy of both the QE and helicopter money policy routes. Finally, we
present a new type of monetary transmission, ‘Smart Helicopter Money’, to deliver monetary stimulus
to innovators, SMEs and high-growth firms via both complementary currencies and a modified form of
QE in order to achieve proportionally greater impact on the real economy.
Keywords:
monetary policy; financial crisis; helicopter money; real economy; financial economy;
quantitative easing; complementary currency; velocity of circulation; innovation; economic growth
1. Introduction
In this paper, we postulate that developed nations have an economic base consisting
of two separate and distinct economies: the real economy of Main Street, focused on ‘value
adding’, and the financial economy of Wall Street, focused upon the wealth management
of existing assets. The narrative in the real economy is one of output, capital investment,
innovation and productivity, whilst in the financial economy it is of increases in existing
asset values and yield. Both real and financial economies depend upon money to function
and grow. The disconnect between the economies, in 2021, is reminiscent of the 1929 one
described by Galbraith (1961) in his seminal work on the Wall Street Crash. Starting from
say 1970, there has been growing divergence largely as a consequence of financial market
deregulation. Starting with the breakdown of the fixed-currency Bretton Woods world,
through to the repeal of the Glass Steagall Act in 1999, financial regulation barriers have
been dismantled and the financial Prometheus allowed to let rip.
Financial deregulation has been associated with the prevailing dominance of liber-
tarian (Friedman 1962;Hayek 1960) thought in conventional economics. This has seen
mainstream monetary theory revert to the classical Quantity Theory in the Fisher (1911)
formulation, supplanting the ‘liquidity preference’ framework of Keynes (1936). By juxtapo-
sition and partly in reaction to this trend, Modern Monetary Theorists
(Juniper et al. 2014)
have presented an alternative framework where they envisage a world of limitless potential
J. Risk Financial Manag. 2021,14, 129. https://doi.org/10.3390/jrfm14030129 https://www.mdpi.com/journal/jrfm
J. Risk Financial Manag. 2021,14, 129 2 of 28
for central banks to ‘monetary-fund’ government deficits. They argue this allows the state
to balance economic activity by acting as the ‘employer of last resort’.1
By contrast, we believe that whilst in the main money is created endogenously, central
bank money creation to fund governments is constrained by the interplay between external
capital flows, exchange rate fluctuations, and overall price growth and financial stability.
Further, following Werner (1997,2012),
2
we argue that the separation between the real and
financial economies results in protracted capital misallocation, as flows of funds and created
credit are focused into purchasing existing assets in the pursuit of stable savings returns
from known predictable cashflows and anticipated capital gains in asset values. Concur-
rently, the real economy requires a higher flow of funds into capital investment to support
entrepreneurial growth in innovators, SMEs, and high-growth firms
(Schumpeter 1934)
. Over-
allocation of capital into the financial economy slows real economy growth rates and misprices
financial economy assets (artificially depressing savings yields). This mismatch, in turn, leads
to financial crises as markets force adjustments between asset prices and the real economy
cashflows that support them.
For us, a financial crisis is a “liquidity freeze” where assets can no longer be turned
into cash at a value aligned to their long-term discounted potential cashflows. The moment
is general rather than specific (Feldstein 1991), so needs to cover more than one investor or
group of investors and more than one company or asset class. The onset of the crisis is a
‘Minsky Moment’ (Minsky 1989).
As asset values fall in the financial economy, there can be an associated economic crisis
as real-economy actors rein back spending and lenders tighten lending criteria. Economic
crises have some similarities to financial crises, as they can start when economic actors
have incurred debt to a level that they cannot service (Minsky 1989). Economic crises are
also synonymous with monetary instability (Friedman 1960). We draw upon, develop,
and interweave these themes to arrive at a 21st Century, high-level understanding of the
relationship between money, financial markets, and the real economy.
More specifically, our view of a crisis is a situation where ‘business as usual’ is
broken. Restrictive assumptions such as ‘rational expectations’ (Lucas 1976;Muth 1961),
representative rational agent modelling, probability theory, and stochastic modelling fail to
support understanding as in a crisis we face the uncertainty described by Frank Knight
(Knight 1921). Arguably, this is one of the key challenges that Keynes (1936) addressed
in his General Theory. Our analysis seeks to understand how changes in the monetary
mass within both the real and financial economies act as a key factor in financial resource
allocation to support innovation and economic growth, with mismatches driving financial
asset bubbles and thereby acting as a crucial driver in the onset of both financial and
economic crises. However, although both real and financial economies are part of one
system, the monetary disconnect between them has profound importance. Specifically,
growth in the monetary mass in the financial economy
does not automatically
pass into
the real economy, so central bank stimulus targeting the financial economy is likely to
have little direct impact on the real economy, although stress and trouble in the financial
economy routinely spells trouble for the real economy.
Whilst money is normally created endogenously, since 2009 there has been an ad-
ditional central bank exogenous ‘quantitative easing’ (QE) creation stream. Our under-
standing of how these monetary mass changes interact with both the real and the financial
economies is enabled by utilizing the quantity equation into our two-economy paradigm.
This simple innovation allows us to shed light on the interplay of money with economic
activity in the real economy whilst providing insights into the bubbles and instabilities in
the financial economy, which in turn can generate real economy instability. Not only does
the quantity of money matter, but how new money is transmitted into the economy is cru-
cial as to whether it will promote real economy activity and growth, or if it will feed asset
1
They argue that this unlimited capability is due to the supremacy of state-issued money, meaning that government debt should be seen as a financial
asset for the private sector, not as a liability.
2This article draws on many of the same concepts and is complementary but not intended as a specific follow-on to Werner’s work.
J. Risk Financial Manag. 2021,14, 129 3 of 28
value increases in the financial economy. This framework helps us understand why money
injected via central banks’ QE has, despite its magnitude since 2009, generally become
stuck in the financial economy and largely failed to reach the real economy. We suggest this
blockage is largely the result of a failure to understand the differing monetary dynamics in
the real and financial economies and the monetary mediation failures between them.
We conclude by noting that in the financial economy exogenous money (QE) stabilizes
(in crisis) by inflating asset market values but lacks appropriate market-facing transmission
mechanisms to carry QE into the real economy. Such real-economy QE support, in the
context of the slow growth rates since 2008 and current Covid-19 pandemic growth losses,
is urgently required to (i) promote crisis recovery; (ii) enhance economic growth rates
by freeing real-economy credit constraints; (iii) support long-term economic/social goals
such as decarbonizing the productive economy and/or promoting regional productive
investment; (iv) a combination of all of the above.
Rather than opting for Friedman’s helicopter money (Friedman 1969), which is focused
at consumers and whose benefit can be reduced through leakages to imports and saving(s)
transferred into the financial economy, we opt for a ‘smart helicopter money’ concept to
target the supply side of the real economy, which existing mechanisms fail to reach. To
realize this we propose three options for transmission mechanisms to directly connect QE
monetary expansion to real-economy growth by addressing those credit-starved innovators
and SMEs that form the bedrock of all developed and developing economies.
The next section presents a qualitative analysis of time-series data from a number of
public and private monetary and economic sources to provide evidence of the disconnect
between the real and financial economies. Section 3develops the main argument of the
paper by generalizing the quantity equation for the two economies separately and detailing
the monetary flows between them. It then analyses the sources and utilization of the
monetary mass in the real and financial economies, providing a framework to explain the
occurrence of financial crashes as a source of economic crises. It ends with an analysis of
why conventional responses to financial and economic crises such as QE and helicopter
money are not effective, and offers three new monetary policy channels to heal the real
economy. Section 4provides a brief clarification of the main concepts used, and Section 5
offers some conclusions.
2. Results
In this section we discuss almost exclusively empirical data to show the divergence
between the real and financial economies since 1970, to give a sense of the asset composition
of the financial economy, and to show that QE is much less effective at reaching the real
economy than we all would wish.
2.1. The Roots of Divergence
Figure 1shows the increase in the size of the financial sector in the United States since
1945. It is striking that assets in the financial sector grew more rapidly following the move
from the gold standard to dollarization in 1971, the liberalization of the 1980s, and the
subsequent repeal of the Glass Steagall Act in 1999.
Figure 2shows how the pure financial sector’s assets rose from 29% of overall US
financial assets in 1946 to 40% in 2019. The real and financial economies are separated by
a black dotted boundary. This growth in financial assets has been enabled by financial
innovation and leverage that have been applied (increasingly from about 1980 to 2000)
to an already increasing US$ monetary mass. The definition of this monetary mass itself
has also evolved as more instruments have tended to adopt “money-like” characteristics.
Of particular importance in the modern financial economy are ‘repo’ and ‘reverse repo’
3
agreements that underpin short-term money market fund arrangements and to a large
extent enable the modern financial system.
3Please see Appendix Afor definitions of technical terms.
J. Risk Financial Manag. 2021,14, 129 4 of 28
0
50,000
100,000
150,000
200,000
250,000
300,000
1946
1948
1950
1952
1954
1956
1958
1960
1962
1964
1966
1968
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
2016
2018
Assets in US$ Billions
US $ Financial Assets (Cumulative)
∑ Assets = C+B+G+F+W where C= Consumers, B= Business G= Government F= Financial and W= World
Hou se hol d Busin ess Government Finan cial Sector Rest of World
Growth in overseas holders reflects “dollarisation”
and US trade deficit spending
Growth in financial sector assets is particularly
clear since market liberalisations
Government
Household
Business
Financial Sector
Rest of the World
Figure 1. Growth in US Dollar financial assets, cumulative view (US Federal Reserve System 2020b).
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
90.0%
100.0%
1946
1949
1952
1955
1958
1961
1964
1967
1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
2006
2009
2012
2015
2018
United States % Shares Financial Assets
∑ Assets = C+B+G+F+W where C= Consumers, B= Business G= Government F= Financial and W= World
Hous e hold Business Government Fin ancial Sector Rest of World
Financial
Economy
Real
Economy
Government
Household
Business
Financial Sector
Rest of the World
Figure 2. Cumulative % share of US financial assets by sector (US Federal Reserve System 2020b).
Understanding the monetary landscape is confused by differences between the defi-
nitions used by different central banks and multilateral institutions (for example the US
Federal Reserve and the IMF). Following the IMF definition of ‘Extended Broad Money’
(see Table 6.2 p. 1996 in Cartas and Harutyunyan (2017)), we estimate US Extended Broad
Money by combining M2 money with institutional zero-demand money (MZM) and gov-
ernment cash, and repos. Debt securities are excluded to avoid double-counting risks as
many of these are already involved in the repo market. Figure 3gives a weekly view of
this growth.
Comparing the growth in the annual money supply to the growth of financial assets in
the household, business, and government sectors, we observe apparently loosely correlated
relationships in Figure 4(US Federal Reserve System 2020a). Although a statistical correla-
tion test gives an
r2
value of only 0.02, qualitatively the curves follow each other for most of
the 1981–2019 period. The major exception is some lagging of broad monetary mass relative
to asset values in the 2000s (this area would benefit from follow-on econometric analysis).
As can be seen in this figure, the repeal of the Glass-Steagall Act in 1999 and significant
J. Risk Financial Manag. 2021,14, 129 5 of 28
leverage utilized by professional traders with access to market trading platforms have
had important systemic implications on the lag structure between the real and monetary
economies. This change has been seen by some as the final “hammer blow” to the post-1929
regulatory system (Crawford 2011).
-2.0%
-1.0%
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
0
5,000
10,000
15,000
20,000
25,000
30,000
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Weekly Change in Broad Money Base %
Broad Money Base Billions US$
United States Weekly Growth In Broad Money
M2 + Government Holding + Zero Notice Financial Money Market
Money&in&US$&billions
%&Change&in&Money&Base
12-period&moving&average
Figure 3. Growth in the United States’ monetary base (M2 +MZM +Government holdings).
-15.0%
-10.0%
-5.0%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
1980 1985 1990 1995 2000 2005 2010 2015 2020
Year -on-Ye ar % C ha ng e
!"#$%&"'()*%+,-#-',#$%#.."(.
%%%/0"#*1"-23%.4)(%#.."(%5#$6".7
8)-"(#*9%:#."%/,-'$62,-;%*"4).7
Monetary)Base%<%8=%>%?"*)1@#(6*,(9%A,-#-'"
%%%%&"'()*%@)-"9%@#*B"(%>%C)5"*-@"-(%'#.D
Real)Sec tor%<%E)-.6@"*FG)6."D)$2%."'()*%>
%%%H6.,-"..%."'()*%>%C)5"*-@"-(
Quantitative Easing
after 2008 Event
Leverage spike after
repeal of Glass-Steagall
Subprime
mortgages “Dash to Cash”
In sync Counter-cyclical
Money lagging Assets
Figure 4. US Monetary and Real Economy Growth 1980–2020 (Sifma 2020;US Federal Reserve System 2020b).
2.2. The Liquidity Continuum
As alluded to above, “formal money” is combined in today’s financial economy with
significant trading platform leverage. This is expressed as the ‘liquidity continuum’ shown in
Figure 5(see also Economist (2021b) for a related discussion). This chart depicts qualitatively
J. Risk Financial Manag. 2021,14, 129 6 of 28
the inverse relationship between fungibility and maturity for some of the main financial asset
classes. The size of the relevant leverage is significant, as shown in Table 1. Small retail
investors access platform liquidity and leverage through brokers (such as Robinhood). These
brokers carry the major platform registrations and provide the necessary margin to attract
the leverage their customers trade under. Generally, financial markets perform within an
overall expectation (many argue a ‘rational expectation’) that sees future growth as being in
aggregate fairly similar to previous growth (for example Figure 6) adjusted for any major
policy announcements, within some noise boundary. Daily movements are generally within
95% confidence limits. As demonstrated in Figure 7, over 90% of the daily changes in the S&P
500 lie within two standard deviations over a 10-year period.
Bank Balances
Multi-Currency
Spot Market
REPOs
Traded Instruments
Futures, Options, CDS
Commodities
Equities
T-Bills
Bonds (IG)
Junk Bonds &
Illiquid Debt
Invoice
Factoring
Plant & Machinery
Sale & Leaseback Real
Estate
Settlement Time
1 Day 3 Days Longer
Asset
Sales/Purchases Leverage Trading Broad Money
MZM M2 M1
Fungibility into
Cash
Figure 5.
The Liquidity Continuum: variation of monetary, financial-leverage, and real-asset fungi-
bility vs. settlement time.
Table 1.
Leverage in the US financial economy at the end of 2019 (US Federal Reserve System 2020b).
US$b
M1 1446
M2 2399
MZM 3130
Broad Money 8068
Broad Money leveraged to:
Futures 22,709
Options 45,417
CDS 3000
Commodities 1000
Equities 1771
Total 73,897
Leverage Factor 916%
J. Risk Financial Manag. 2021,14, 129 7 of 28
Figure 6. 90-Day smoothed change in S&P Index 2010–2020 (NASDQ 2020).
-15.0%
-10.0%
-5.0%
0.0%
5.0%
10.0%
15.0%
11/0 4/201 0
11/0 1/2 011
10/ 31/201 2
10/ 29/201 3
10/ 27/201 4
10/ 23/201 5
10/ 20/201 6
10/ 18/201 7
10/ 16/201 8
10/ 15/201 9
10/ 12/202 0
% Deviation From Arithmetic Mean
Vari ation Plus 1 Std Dev From Mean
Minus 1 Std Dev From Mean Plus 2 Std Dev From Mean
Minus 2 Std Dev From The Mean
+/-$2$standard$deviations$from$mean
Vari at ion
+/-$1$standard$deviation$from$mean
Figure 7. Daily % variation in S&P Index 2010–2020 (NASDQ 2020).
2.3. The Role of Expectations
When divergence between reality and ‘rational expectations’ does occur it can be
highly significant. We select two periods of such divergence to demonstrate this point, the
first being the 2011 USA credit rating downgrade and the second the pandemic-induced
drop in spring 2020. Markets perform differently when they are either under stress (crash-
ing) or recovering, notwithstanding recent comments by some that recovery from the
Covid-19 pandemic could be ‘V-shaped’ (Sharma et al. 2020). By contrast, the multi-period
nature of a steady rise of a market index (usually termed a bull or boom market) is a more
stable and predictable affair.
At market level, the dynamics of rapid change often start with a crash that is then
followed by recovery. In a crash, expectations change rapidly as market participants try to
protect their positions, with the recovery taking somewhat longer. Figure 8shows how a
sharp market adjustment in 2011 unfolded. The original event that led to the adjustment
was a downgrading of the United States’ credit rating, with the downward phase taking
rather longer to recover.
J. Risk Financial Manag. 2021,14, 129 8 of 28
-8.0%
-6.0%
-4.0%
-2.0%
0.0%
2.0%
4.0%
6.0%
2011-07-15
2011-07-22
2011-07-29
2011-08-05
2011-08-12
2011-08-19
2011-08-26
2011-09-02
2011-09-09
2011-09-16
2011-09-23
2011-09-30
2011-10-07
2011-10-14
2011-10-21
2011-10-28
2011-11-04
2011-11-11
2011-11-18
2011-11-25
2011-12-02
2011-12-09
2011-12-16
2011-12-23
2011-12-30
%"Change"in"Market"Index"on"Previous"Day
Crash downward
spikes become less
severe over time
Recovery resumes more normal movements
!"#$%&'()*+,&$)-.$,-
/01&2+#-3
Figure 8. Daily % variation in S&P Index, 2011 Crash (NASDQ 2020).
This experience differs from the Covid-19 pandemic’s impact in 2020. Once it was clear
that this pandemic was going to be truly global and engulf the United States (dramatically
demonstrated by the March 2020 events in New York City), financial markets started to
adjust rapidly, with a ‘dash for cash’ as investors left many asset classes including United
States Treasury Bills (T-Bills). The process of financial market contagion spilled across
many asset classes, as asset holders first dashed for safer and more liquid assets and then
for cash. This contagion drove multiple asset markets to move together, as described in a
report by the Basel-based global Financial Stability Board (FinancialStabilityBoard 2020).
The US Federal Reserve reacted rapidly, buying a wide variety of assets. Figure 9
shows how after the intervention on the 23rd of March 2020 the equity market (and indeed
other financial markets) stabilized, while GDP continued to fall. The cumulative impact of
these changes can be seen in Figure 10, which shows rather dramatically the divergence
between the real and financial economies.
-35.0%
-25.0%
-15.0%
-5.0%
5.0%
15.0%
25.0%
35.0%
-15.0%
-10.0%
-5.0%
0.0%
5.0%
10.0%
15.0%
2020-01-28
2020-02-04
2020-02-11
2020-02-18
2020-02-25
2020-03-03
2020-03-10
2020-03-17
2020-03-24
2020-03-31
2020-04-07
2020-04-14
2020-04-21
2020-04-28
2020-05-05
2020-05-12
2020-05-19
2020-05-26
%"Change"in"Quarterly"GDP
Daily"%"Change"in"Index
!"#
$%#&'()*+
,-)./&0123(4&.2*5.4*
Increasing
divergence
23 March
Fed intervention
Financial markets
stabilisation
Figure 9. Daily % variation in S&P Index during the 2020 pandemic (BEA 2020b;NASDQ 2020).
The puzzle that this chart reveals is the subject of our analysis in the next section, where
we present the outline of a theoretical framework to explain how the US Fed’s interventions
J. Risk Financial Manag. 2021,14, 129 9 of 28
in March 2020 stabilized financial markets but, despite injecting record amounts of new
monetary assets, have failed to restart GDP in the real economy. We close the article by
proposing new monetary transmission channels to directly impact the real economy from
the supply side rather than the more conventionally conceived demand-side ‘helicopter
money’ (Friedman 1969).
-35.0%
-30.0%
-25.0%
-20.0%
-15.0%
-10.0%
-5.0%
0.0%
5.0%
10.0%
2020-01-29
2020-02-05
2020-02-12
2020-02-19
2020-02-26
2020-03-04
2020-03-11
2020-03-18
2020-03-25
2020-04-01
2020-04-08
2020-04-15
2020-04-22
2020-04-29
2020-05-06
2020-05-13
2020-05-20
2020-05-27
2020-06-03
2020-06-10
2020-06-17
2020-06-24
2020-07-01
2020-07-08
2020-07-15
2020-07-22
2020-07-29
2020-08-05
2020-08-12
2020-08-19
2020-08-26
% Change (Cumulative)
!"#
$%#&'()*+
,-)./&0123(4&.2*5.4*
23 March
Fed intervention
Figure 10.
Cumulative % change in S&P Index during the 2020 pandemic (BEA 2020b;NASDQ 2020).
3. Discussion
3.1. The Overall Economy
Conventionally, monetary mass is defined by the domestic currency area it operates
within. Central banks and national authorities measure economic activity and monetary
mass within these areas, often starting from a simplified equation that relates the different
elements of Gross Domestic Product (GDP):
Y=C+I+G+ (Exp −Imp), (1)
where
Y
is the national income,
C
is consumption,
I
investment,
G
government expenditure
(both investment, GI, and services, GS), Exp exports, and I m p imports.
The IMF (2021) and, as implemented in the USA’s System of National Accounts
the Bureau of Economic Analysis (BEA 2020a), define GDP as the sum of Gross Value
Added, which can be broken down into Traded Gross Value Added (GVA), or
J
, where the
relevant service is traded at a known market price, and Non-Traded GVA, or
GNTS
, where
the service is provided outside the market system. Typically,
GNTS
covers government-
supplied services such as defence or education. Therefore, government expenditure can be
broken down as
G=GI+GS=GI+GTS +GNTS
, where ‘TS’ stands for ‘traded services’.
Fundamental to this paper is to recognize that the overall economy is made up of an
additional, third component, the increase in the value of asset wealth,
H
, over the same
defined time-period. This is discussed in more detail later, but at this point we need to
establish the relationship between J,G, and Y, namely:
Y=GDP =J+GNTS. (2)
Setting
(1)
and
(2)
equal to each other and using the more granular definition for
G
,
we get that
J. Risk Financial Manag. 2021,14, 129 10 of 28
J=C+I+GI+GTS , (3)
since in this analysis we are neglecting foreign trade. To simplify our presentation, in this
article we assume that all components of
G
are constant during the chosen time-period. This
assumption allows us to ascribe all the changes in the value added in the real economy to
J
,
thereby simplifying the need to explain the dynamics of
G
at every stage of our reasoning.
We believe a potential future research agenda should be to relax this assumption. Table 2
shows how different kinds of economic activity contribute to the national accounts and to
the increase in asset wealth in the financial economy.
Table 2. Contribution of economic activities to the national accounts (IMF 2017).
Contribution to . . .
Productive
Value Added
Non-Traded
Services
GDP
Calculation
Wealth
Calculation
Sector J G GDP
(
J+GNT S
)
H
Agriculture and Forestry Yes No Yes No
Fishing Yes No Yes No
Mining and Quarrying Yes No Yes No
Manufacturing Yes No Yes No
Electricity, Gas, Steam & Air Conditioning
Yes No Yes No
Water Supply Yes No Yes No
Construction Yes No Yes No
Wholesale and Retail Yes No Yes No
Transportation and Storage Yes No Yes No
Accommodation Yes No Yes No
Food Services Yes No Yes No
Information and Communication Yes No Yes No
Financial and Auxiliary Services Yes No Yes No
Insurance and Pensions Yes No Yes No
Real Estate Services Yes No Yes No
Professional, Scientific and Technical Serv.
Yes No Yes No
Administration and Support Services Yes No Yes No
Public Admin., Defence, Social Security No Yes Yes No
Education No Yes Yes No
Human Health and Social Work (Gov.) No Yes Yes No
Human Health and Social Work (Private) Yes No Yes No
Arts, Entertainment and Recreation Yes No Yes No
Asset Value Changes No No No Yes
3.1.1. Money, Transactions and GDP
We introduce money into this framework using the Quantity Equation (Fisher 1911):
MV =PT (4)
or, in terms of fundamental dimensions,
[Currency]Circul ations
Time = [Currency]No.o f T x
Time
or, simplifying, Currency
Time =Currency
Time .
(5)
Equation
(4)
relates monetary mass to economic output and applies to a specific
time-period
t
, which may be set in advance or may fall out of the analysis, as explained
below. The simplification of the dimensions shown above is due to the fact that cycles (or
J. Risk Financial Manag. 2021,14, 129 11 of 28
circulations) and number of transactions are pure numbers and are therefore dimensionless.
We begin the discussion by assuming a set time-period (1 year, or 1 month, etc). In this
equation, therefore,
M
is the quantity of money or monetary mass (see Section 3.3),
V
is
the velocity of circulation for the given time-period,
P
is the average price level, and
T
is
the total number of transactions in the given time-period. Out of these variables the one
that may be less familiar is circulation, which we now define carefully because it plays an
important role in what follows. ‘Circulation’ refers to the movement of money through
the economy due to economic transactions and, as shown above, is measured in units
of inverse time. More simply put, it is exactly analogous to a frequency, namely to the
frequency of circulation of the monetary mass in a given time-period.4
In some of the economics literature GDP is used in place of
T
, which for dimensional
consistency requires the interpretation of
P
as the dimensionless ‘GDP deflator’. In this
paper, to account for GDP in Equation
(4)
in a dimensionally consistent manner we prefer
to maintain the original meanings of these variables and set both sides of the equation
equal to income or economic output:
x=MV =PT. (6)
Depending on how
x
is interpreted, the meaning of the other variables changes.
Further complexity is caused by the different definitions for monetary mass M. As defini-
tions of monetary aggregates widen, more of the monetary mass is used in the financial
economy. For the sake of clarity and completeness we list the possibilities here without
worrying about the fact that some of these quantities may be challenging to measure from
an econometric point of view (see also Table 3, below):
•
If
x
is total transaction volume (e.g., expressed in US$ transacted per year) we are
dealing with the original Equation
(4)
and the variables are defined as above, but
including also all asset purchases and sales in the financial economy. Specifically,
M=Broad Money.
•
If
x
is GDP, then the relevant monetary mass is defined as a subset of
M
, namely
MY
,
that corresponds to the product and service transactions that actually made use of it;
V
and
P
are unchanged in concept although clearly they can change in value; and
T
refers only to those product and service transactions recorded within GDP. In this
case we set
x=Y
. We note also that in this case
V
is dubbed the ‘income velocity of
circulation’.
•
If
x
is set as traded GVA, or
J
, then our output measure no longer equates to GDP, the
difference being due to non-traded GVA such as
GNTS
. For traded GVA we should be
using
∆P=Pout −Pin
in place of
P
, i.e., the average difference in price between two
steps in the same supply chain, which measures the value added. Adopting this in-
put/output approach allows our value-added measure to be evaluated independently
of a specific stage of production, obtaining
J=γMV =∆PT (7)
or J=MRVR=∆PTR, (8)
where the subscript
R
makes the emphasis on the real economy explicit and
γ
is a
factor that expresses the fraction of the monetary mass that participates in the traded
gross value-adding.
•
Finally,
x
could also be
GNTS
, non-traded GVA, whose monetary mass we call
MNTS
.
In our discussion we treat all components of
G
as constant and changes in
J
as changes
in the real economy.
J
has specific quantification issues as Equation
(4)
is usually applied
for a fixed time-period, often 1 year. Such arbitrary periods generate accounting challenges
as value added in a supply chain occurs within payment transaction sequences that can
4For conceptual clarity, we assume that Mis constant during the time-period used to calculate these variables.
J. Risk Financial Manag. 2021,14, 129 12 of 28
straddle the initial or final time boundaries, and that can therefore lead to significant
implicit stage-of-production assumptions. To address this issue we invert the algebraic
problem: rather than setting the time-period and solving for
V
, we set the value of
V
and
leave the time-period as a dependent variable. More precisely, following Keynes (1930)
and Schumpeter (1934), we set the velocity of circulation equal to 1, which means that the
activity
J
is measured for a single monetary circulation. For the sake of clarity, we reserve
the Greek letter
τ
to denote the time-period that corresponds to 1 circulation, which of
course can vary in different contexts.
A simple example can be provided for the case where
x=GDP
. If Equation
(4)
is
applied to the US economy and a period of 1 year is chosen, the value of
V
is approximately
1.5, meaning that the monetary mass circulates one-and-a-half times in the US economy,
in one year. If, instead, we choose to set
V=
1, then the value of GDP or
Y
and the
corresponding time-period will become dependent variables, they will adjust to whatever
corresponds to one circulation. For the US$ this is a smaller value of
Y
and a period shorter
than 1 year.
Table 3. Monetary mass variables associated with different economic accounts during one circulation.
Account Account Variable Mass Variable Relation to Other Mass Variables
Whole economy Tot. Transac. Vol. M MR+MNTS +MF=MY+MF= Broad Money
GDP Y MYM−MF=MR+MNTS
Traded GVA J MRM−(MNT S +MF) = MY−MN TS =γM
Non-traded Govt. GVA GNT S MNT S M−(MR+MF) = MY−MR
Change in gross wealth H MFM−MY
3.1.2. The Role of Value Added
Focusing upon value added in a circulation, and relating some proportion of the
monetary mass to this quantity, the productivity that value added represents can increase
over time if new technologies are applied so that more output can be derived from the
same level of circulation. In the short term, and ignoring economies of scale that are less
important in today’s largely service-dominated economies, productivity is fixed. Using
J
allows us to focus on the supply side of the economy, on the assumption that an increase in
consumer cash balances will be utilized in purchasing locally produced output or imports,
or in saving.
J
looks exclusively at the first of these, and so focuses the question (to which
we will return) as to how an increase in monetary mass can be used to drive an increase in
value added. In reference to the
∆P
in Equation
(7)
, we define a coefficient of productivity,
α, as the ratio of output to input value within a single circulation:
α=Pout T
Pin T=Pout
Pin
. (9)
It is notable that this increase in value added says nothing about possible changes in
asset values or asset yields in the financial economy where, as shown in Figure 11, changes
seem to be disconnected from movements in GDP.
3.2. The Two Economies
Traded GVA (
J
) results from traded activity in the real economy. Consumers demand
products and services that are provided by economic actors who in turn pay for their
own inputs (raw material, labour, entrepreneurial contribution, and capital). Asset prices,
on the other hand, rise in relation to investor demand. This demand may either be real
(the deployment of savings) or speculative (the creation of new money for the leveraging
of assets). The assets exist at the start of a circulation cycle, so financial flows into and
out of them result in changes in asset prices and consequent values. Overall, rational
investors are expected to optimize yield across assets in accordance with perceived risk
J. Risk Financial Manag. 2021,14, 129 13 of 28
(Markowitz 1952)
, although the opportunity for speculative gain (the casino referred to in
Keynes (1936)) can supersede strict rationality.
0%
1000%
2000%
3000%
4000%
5000%
6000%
196 4 19 6 6 19 6 8 19 7 0 19 7 2 19 74 19 76 197 8 19 8 0 19 8 2 19 8 4 19 86 19 88 199 0 19 9 2 19 9 4 19 96 19 98 2000 2002 2004 2006 2008 2010 201 2 2014 201 6 201 8 2020
Equities
GDP
House-prices
1971 Dollar standard
1973 Dollar float
1980 Depository Institutions,
Deregulation and
Monetary Control Act
1999 Repeal of the
Glass-Steagall Act
Figure 11.
Comparison of percentage change in asset prices (residential house prices and S&P 500 equities vs. GDP,
United States).
These different dynamics suggest that there are two different economies coexisting
within a single monetary space: the
real economy
, defined as the monetary circulation
within which the implicated monetary mass is utilized in value-adding activity and the
building of new real-capital assets, and the
financial economy
, defined as the monetary
circulation within which the implicated monetary mass is utilized to hold and trade existing
financial assets.5
Changes in regulation have also been important. During the tightly regulated
1945–1980
period, the real and financial aspects were kept in lockstep, with saving broadly matching
investment across OECD countries (Feldstein and Horioka 1980). Increasingly, post-1980, a
saving/investment divergence (Khan 2017;Tesar 1991) reflected increasing financializaton
of developed nations’ economies, in turn associated with the post-1970 financial deregula-
tion.
6
Additionally, the introduction of the Basel Capital Accords, especially Basel II, skewed
bank lending away from small firms and innovators, especially in favour of residential real
estate in response to regulatory risk weights and associated bank capital allocations.
The increasing focus on wealth management (accumulation) associated with financial
deregulation has led to the financialized economy becoming both pervasive
(Krippner 2005)
and international (Christophers 2012). We illustrated this financializaton in the US economy
in Figures 1and 2. As financializaton grows, so does the divergence between the rate
of return on capital investment (Keynes’s marginal efficiency of capital (Keynes 1936))
and financial economy interest rates. Expressed more formally, the marginal efficiency of
capital diverges from the natural rate of interest (Wicksell 1898) which, in turn, diverges
from the neutral rate of interest, which balances inflation and financial markets, as the
real and financial economies become less connected in response to deregulation. Figure 12
conceptualizes the two economies and the monetary interdependencies between them.
5Although we do not treat it in this article, as already mentioned the duration of a circulation can differ between the real and financial economies.
6This savings/investment divergence is not associated with trade balance changes (Obstfeld and Taylor 2005).
J. Risk Financial Manag. 2021,14, 129 14 of 28
Real Economy
(comprised of sectors)
Financial Economy
(comprised of asset classes)
Savings & Real Estate
Investment
•!Savings Withdrawals
•!Equity & Bond Proceeds
•!Real Estate Sales
Consumption
including
Government
Entrepreneurship
incl. Production
& Services
Financial
including Utility
Banking
Real Estate
Commercial &
Residential
Equities &
Bonds
Derivatives
incl. Futures, ETFs
& Indexes
Supported by Income:
Salaries, Profit, Pensions, Interest, Dividends
Financial Economy holds
economic actors’ wealth
Increases in economic actor wealth can lead to
withdrawals and loans to support raised consumption/
investment in the real economy. This link is not described;
rather, the effect is assumed to be handled by the
financial sector in the real economy.
Wealth Effect
Figure 12. Structure of the two economies, high-level monetary flow connections, and wealth effect.
3.2.1. Different Kinds of Circulations in the Two Economies
The two kinds of circulation that correspond to the two economies are shown in
Figure 13
. We note that while in the real economy there is a single circulation for a given
nation-state, in the financial economy a different circulation can be identified for each asset
class. Simplistically, entrepreneurs, firms, wage earners, consumers, and utility finance
(banks that hold checking accounts and extend simple loans to real-economy actors) belong
in the real economy. Financiers, asset traders, shadow banking, wealth management,
existing real estate trading, insurance and financial options, indexes, derivatives, and other
complex products belong in the financial “economies”. Krippner (2005) usefully categorizes
these as FIRE (Finance, Insurance and Real Estate) activities.
In the real economy, the output of a single circulation is the value added,
J
. Output in
the financial economy from a single circulation is the change in the value of existing assets.
To calculate the value of existing financial assets at a point in time, denoted
W
, one simply
adds the number of each asset times its price. However, this calculation is complicated by
the fact that assets come in different classes, so each class requires a separate sum. If we use
i
as the index that indicates the asset class, this can be expressed with a double summation
as follows:
W=
n
∑
i=1 mi
∑
j=1
pij !, (10)
where
n
is the number of asset classes,
mi
is the number of assets in asset class
i
, and
pij
is the price of asset
j
in asset class
i
. The output or the ‘change in gross wealth’,
H
, of the
financial economy can now be defined as the difference in
W
at the beginning of successive
circulations as
H=Wt+1−Wt, (11)
where
t
is used loosely as an index to indicate a given circulation rather than a specific
time-period.7
7
As already stated, the implication is that the time duration of different circulations may be different, but this does not present a problem in the
present analysis.
J. Risk Financial Manag. 2021,14, 129 15 of 28
Creating new assets in the real economy drives economic growth, whilst the intricacies
of packaging cashflows from existing financial assets in the financial economy delivers
the returns savers look for. Unsurprisingly, the monetary mass performs differently in the
real and financial economies. Thus, following in the footsteps of Werner (1997,2012), we
split (4) into two components, as follows:
MRVR+MFVF=∆PRTR+∆PFTF, (12)
where the subscript
R
denotes the real economy and
F
the financial economy. The difference
in the nature of the real and financial economies allows us to split (12) into two equations
and set them equal to their corresponding outputs in a single circulation:8
J=MRVR=∆PRTR(13)
H=MFVF=∆PFTF. (14)
Notably, it is only Equation
(13)
that is integrated into the real variables described
in Equation
(1)
. Equation
(14)
for the financial economy is separate from the real activity
variables of Consumption, Investment and Government: its connections are indirect via
specific interactions between the two economies, as shown in Figure 12.
Real Economy
(comprised of sectors)
Financial Economy
(comprised of asset classes)
Consumption
including
Government
Entrepreneurship
incl. Production
& Services
Financial
including Utility
Banking
Real Estate
Commercial &
Residential
Equities &
Bonds
Derivatives
incl. Futures, ETFs
& Indexes
Real Economy Circulation
Each iteration of GVA
= monetary mass
Trading cycle
differs for each
asset class
Circulation for each
asset class
Multiple Financial Economy Circulations
Figure 13. Circulation.
In discussing Equation
(6)
, under our two-economy model it is possible for growth in
the overall monetary mass
M
to exceed the growth in
J
without there being significantly
observed consumer price inflation. This is because part of the growth in the monetary
mass has been effectively ring-fenced into the financial economy, thereby inflating asset
prices that are not included in mainstream price indices. Table 3provides a summary of
the different types of monetary mass for the different economic accounts.
8
This is a major modelling step as it represents a strict additional constraint whose validity will need to be verified ex post by future empirical and
mathematical research. This paper presents only the first tentative steps towards a possible model, modulated by the interactions shown in
Figure 12
and Table 4, which could be expressed as additional equations.
J. Risk Financial Manag. 2021,14, 129 16 of 28
Table 4. Impacts of monetary flows on real and financial economies.
Real Economy Financial Economy
Impact Impact
Flow GDP MRAsset Valuation MFImpact Type Assumptions
Savings - – + + Direct All new savings deployed into
financial assets
Savings withdrawal - + – – Direct All savings withdrawals taken
from assets
Equity investment +
(indirect) + + – Direct Funded by existing, not new
money
Bond issue - + + – Direct Funded by existing, not new
money
Profit and loan interest - – + + Direct
Assumes reinvestment of proceeds
received
Management charges + + – – Direct Assumes funded by reduced
reinvestment of profits
Savings returns + + – – Direct Assumes funded by reduced
reinvestment of profits
Inbound capital flow - - + + Direct All deployed into financial assets
Outbound capital flow - - – – Direct All withdrawn from assets
Wealth effect If >0 it can increase consumption and vice versa Indirect
3.2.2. Linkages Between the Real and Financial Economies
The connections
9
between Equations
(13)
and
(14)
occur through a series of direct and
indirect effects set out in
Table 4
. The same connections were also shown already at a more
conceptual level in
Figure 12
. The financial economy’s contribution to
J
is defined as the
impact of the value added created in the financial sector (bonuses, fees, etc.) and then as
movements in consumption and investment behaviour due to the ‘wealth effect’. Drawing
upon Burgess (2011), we formalize the value-added impact from the financial economy
into the real economy as
Jf=kPfTf+i∆L, (15)
where
k
is the fee percentage associated with every strictly financial transaction within the
monetary base, and
i
is the interest rate on the loan volume
L
in the period. We are using
lower-case
f
to differentiate these variables from those used in Equation
(14)
. Namely,
f
denotes all the financial transactions in the financial economy plus the transactions in
the financial sector of the real economy (see Figure 13). Another subtle difference relative
to
(14)
is the use of
P
rather than
∆P
since fees are levied on gross prices rather than on
price difference or value added.
Activity in the real economy is indirectly impacted also by the wealth effect, where
individual economic actors may change real-economy consumption and investment be-
haviour as a result of changes in their financial wealth, expressed here as an unspecified
functional dependence:
∆Cw=f(H), (16)
where Cwis the consumption associated with wealth effects.
A further impact from the split of the two economies, post-Keynes (1936) and
Hicks (1937)
,
is that macroeconomic adjustment is viewed by many as the result of the saving rate moving to
meet the level of investment, to ensure saving equals investment (Lerner 1938). A decision to
save does not mean a decision to invest in new real-economy assets, since it can be a decision
to deploy cash into financial markets or hold larger cash balances. In other words, a decision
to save represents a resource withdrawal from the real economy that may or may not be
reinvested.
10
In turn, the level of investment in the real economy depends upon (i) the desire
9In mathematics or physics this is called the ‘coupling’.
10 We note that changes in post-Pandemic saving behaviour are not yet clear.
J. Risk Financial Manag. 2021,14, 129 17 of 28
of firms to invest to meet an anticipated market need, (ii) capability to invest from current
cashflows,
11
and (iii) the willingness of investors and banks to provide additional resources in
preference to making potential gains on existing assets in the financial economy. Interactions
in today’s globalized system with a single main reserve currency are further complicated by
both the trade balance and capital flows.12
We should explain at this point that although the mathematical formalization dis-
cussed so far could be used as the starting point for a more in-depth modelling effort,
for example based on extending general equilibrium theory towards dynamic behaviour
(Arrow and Debreu 1954;Day 1994;Debreu 1970;Hahn 1978;Mas-Colell 1985;Samuelson
1948), etc., its role in the present paper is merely to provide greater conceptual clarity in
support of the overall argument. This paper introduces innovations to monetary theory
in the same discursive non-mathematical tradition used by Keynes, Friedman and others
in this area. There is no claim that it is a self-standing mathematical construction of this
aspect of economic theory. We therefore proceed in the same vein, with the analysis of the
next building block of the two-economy model in relation to crisis: the monetary mass and
how it originates.
3.3. Monetary Mass
We define the monetary mass as the quantity of money available to support economic
activity in either the real economy (Equation
(13)
) or the financial economy (Equation
(14)
).
In this analysis the monetary mass is assumed to be independently measured through
empirical means, even if that may be difficult in practice. The monetary mass is made
up of the elements set out in Table 5. We also note the importance of gross capital flows
(Borio and Disyatat 2010) as a source of funds.13
Table 5. Components of the monetary mass.
Name Description Real Economy Financial Economy Source
M1 Physical notes and coins, and money in
current accounts at banks Yes Yes FED (2020)
M2
M1
+
savings, small time deposits, and retail
money market funds Yes Yes FED (2020)
Broad Money M2 +eligible debt securities (paper that can
be repo’d). In the USA this includes MZM. Partial Yes Table 61, p. 182 in IMF
(2016)
Net Broad Money Broad Money −M2 No Yes
MZM (M2 −small term-deposits) +institutional
money market funds No Yes US Federal Reserve
System (2020a)
Trading Leverage Creation of trading credits against margin
deposits No Yes
Whilst both the real and financial economies require bank-mediated credit, certain
forms of monetary mass are only available within the financial economy. In effect, the
financial economy monetary mass consists of (i) open loan balances,
14
(ii) margin deposits,
(iii) trading platform leverage, and (iv) cash that a given investor has on hand and that the
investor has allocated to buy positions without making use of a loan within an asset class
(so this cash is now conceptually held within the asset class). By contrast, the real-economy
monetary mass reflects immediately accessible cash and bank credit balances.
11 In the macroeconomic identity for saving this diversion of funds to investment is seen as saving deployed as investment.
12
In a dynamic setting, to these must be added the import propensity to consume (how much of each $ of national income is spent on imports vs. home
production) and the propensity to save (how much of each additional $ of income is saved).
13 Capital flows have special importance with respect to exchange rate constraints on central bank policy.
14 Open loan balances mean the amount of credit still available to be drawn down by the economic actor.
J. Risk Financial Manag. 2021,14, 129 18 of 28
3.3.1. Real Economy Endogenous Money Demand
Money is created endogenously in both the real and financial economies.
15
Following
Hahn (1924),
16
formal endogenous money creation (BOE 2014) occurs when bank bal-
ances are created from loans
17
within the banking system (Werner 2014).
18
Between 1959
and 2017 a link can be observed between growth in monetary aggregate M1 and GDP
(Deleidi and Levrero 2019)
. Liquidity also matters. For instance, Laidler (2006) highlights
the importance of adequate liquidity (bank deposits and cash), in turn giving central banks
a lender-of-last-resort role for liquidity provision (Minsky 1982).
Expansion of the monetary mass in the real economy is a key enabler of economic
growth (Schumpeter 1934). Repeated studies from as far back as the Macmillan Report
of the 1930s (Stamp 1931) highlight the need for innovators, SMEs, and high-growth
companies to have access to adequate capital. In theory, the ‘K plus’ rule
(Friedman 1960)
,
where the monetary mass should increase at the same percentage rate as the rate of GDP
growth, should enable this expansion and ensure that inflation does not take off. Implicit
to this is the requirement that the money created reaches needy firms in the real economy
and is not frittered away into blowing financial economy bubbles. With the now near-
complete separation between the real and financial economies, the K plus rule needs
updating to reflect sophisticated capital markets, financial deregulation, banks increasingly
focused on real estate lending, bond issues that are only available to large companies and
private-equity companies, and the plethora of complex derivative products.
3.3.2. Financial Economy Endogenous Money Demand
For the financial economy money demand relates to the level of asset trading. Each
type of asset is grouped into an asset class, which is typified by its risk and liquidity
properties (see Figure 5). Financial economy money demand is the monetary mass required
for a single circulation of trading for an individual asset class. The time duration of each
circulation varies according to asset class (for example, real estate transactions take longer
than option transactions). Abstracting and simplifying, we define eight asset classes in the
financial economy:
(i)
Traded derivatives, options and futures
(ii)
Equity
(iii)
Government bonds (T-Bills/Gilts etc)
(iv)
Commercial investment-grade bonds
(v)
Non-investment-grade bonds
(vi)
Residential real estate
(vii)
Commercial real estate
(viii)
Cash
More generally, the financial sector consists of a vector of
n
asset classes (within which
the individual assets are held) with (average) price vector
pF= (p1
,
p2
,
· · ·
,
pn)
. For each
asset class, a specific forward price expectation
ei
applies, the velocity of circulation is set to
1, and the time-period for a circulation is a function of price and expectation,
τi=f(pi
,
ei)
.
Similarly, each asset class has its own monetary mass
(MF)i
. Although we chose to set
V=
1 for greater conceptual clarity in the initial part of our analysis, it is easier to discuss
qualitatively the instabilities in the financial economy from the point of view of a fixed
time-period and a variable velocity of circulation. Specifically, the velocity of circulation
of each asset class is potentially unstable, as it is a function of the price expectations
ei
15
Since the 1970s, money has been created endogenously in both the real and financial economies unless there are ‘emergency’ administrative
constraints (Badarudin et al. 2013), such as the UK 1973 Supplementary Special Deposits Scheme (BOE 1982) where credit creation levels were
specified by the central bank.
16 L Albert Hahn had considerable influence on Josef Schumpeter.
17 Banks and central banks are the only institutions able to create official national currency.
18
The exception to this is when trading platforms (which may or may not be part of banks) create trading liquidity through margin trading prior to
platform netting and settlement of any outstanding liabilities.
J. Risk Financial Manag. 2021,14, 129 19 of 28
for that asset class that are, in turn, impacted by price expectations in other asset classes.
This vector-based approach, therefore, provides an effective formulation to describe the
effective money demand in the financial economy, which in turn drives money creation
and leverage.
In the financial economy, Broad Money’s mass is augmented by the two additional con-
cepts of ‘settlement’ and ‘trading leverage’. Settlement allows financial market traders to pay
for their purchases (after netting their sales) after a typical 1 to 3-day period
(Bech et al. 2020)
.
Trading leverage relates to margin account trading, whereby a trader can leverage their
collateral
19
(Heimer and Simsek 2019). The repo market, where high-quality securities are
sold and repurchased on a short-term basis, gives institutions access to short-term cash and
specific securities required for settlement (ICMA 2020). Trading platform leverage moves pro-
cyclically, so that it rises as asset prices rise and falls as asset prices fall, thereby multiplying
price impacts of changes in trading expectations (Adrian and Shin 2010).
3.3.3. Exogenous Money
Since 2008, central bank money has additionally been created via QE (exogenously
determined), as opposed to being related to endogenous growth. Under QE, the central
bank buys existing assets (for example commercial or government bonds) that are then
held on the central bank balance sheet for an indeterminate period of time. In this QE
environment there is one special case where government expenditure (both consumption
and investment) may be funded indirectly via the issue of government debt that is then
repurchased by the central bank. Such action raises the value of
G
by injecting additional
M1 money and is known as ‘monetary financing’ since there is little prospect of early
repayment of the debt. Raising
G
may or may not deploy additional funds to the value-
adding sector
J
, a point we discuss further below in the context QE transmission channels.
Following QE purchases, asset prices adjust upwards (imperfectly and with differing
lags according to the “stickiness” of the asset class in question) according to demand
from the central bank and as asset market actors re-balance holdings across asset classes.
This rebalancing, depending on investor expectations and propensity to consume, may
or may not release surpluses into the real economy.
20
Mechanically, QE reduces yields,
and percolates via the portfolio balance effect into other asset markets, changing prices
to balance risk-adjusted yields (Joyce et al. 2011) with specific market impact set by each
individual asset class’s dynamics (Goldstein et al. 2018;Thornton 2014). Significantly,
investors look for safety and not to engage riskier debt.
The impact of QE on real GDP is less clear, and limited. Since QE asset purchases
create new M1 deposits, which add to the real economy monetary mass, some expect that
such interventions will help the real economy. However, since the sellers of the assets are
financial economy actors, this provision of additional M1 money in general stays in the
financial economy, raising asset prices rather than increasing traded value added
J
. In the
UK a monetary injection of 14% of GDP was estimated to raise GDP by around 1.5% to
2.0% (Joyce et al. 2011).
3.4. The Velocity of Circulation in the Financial Economy
Effective monetary mass growth can also arise from a rise in the velocity of circulation of
the monetary mass, meaning a shortened time-period
τ
for a single circulation. Empirically,
in the short term, the velocity of circulation can be somewhat unstable, with a variety of
explanations being offered (Bernanke 2006;Friedman and Schwartz 1982;Taylor 1993). Long-
term, changes in the velocity (Anderson et al. 2015) occur due to a variety of structural reasons
(Bordo et al. 1997;Duca 2016) that reflect the increasing sophistication of the financial economy.
However, there can also be significant short-term fluctuations such as during the 1923 German
19 Professional traders in the USA are limited to a 1:50 leverage by the Dodd Frank Act of 2010, whilst commodity traders have a leverage limit of 1:20.
20 Surpluses can be reinvested into financial assets in anticipation of further price rises.
J. Risk Financial Manag. 2021,14, 129 20 of 28
hyperinflation (Laidler and Stadler 1998) and in anticipation of price changes (Cagan 1956;
Hamilton 1989).
Trading volume and associated volatility (Karpoff 1987) are seen as a good proxy for
length of
τi
in each asset class, with volumes especially volatile in market stress.
21
This
creates the ‘effective
τi
’ for each circulation by asset class. Responding to market volatility,
each asset class velocity itself can change rapidly and demonstrate instability in times of
market stress. The overall velocity in the financial economy is itself a weighted average
(based on each asset class’s proportion of
MF
) of different asset class-related velocities
in a specific time-period. In addition, deregulation
22
has decoupled the length of cycles
between the two economies and enabled a return of some of the speculative characteristics
of the late 1920s.
In the face of such speculation, the financial economy is inherently unstable, since each
asset class carries its own volatility (
λ
), fungibilty (
θ
) into other assets including cash, and
unit of trading (
β
). Assets that have a large
β
account like residential property tend to have a
lower fungibility parameter
θ
. Each asset class has its own set of time-phased expectations
(
e
) for each forward time-period
t
(or
τ
), with expectations in turn relating to a function (
w
)
expressing previous volatility in the asset class (normally through an exponentially-weighted
moving average), plus some current expectation of volatility in alternative asset classes. We
can express the volatility of an individual asset class as the following equation, which is
weighted (u) by the monetary mass of this asset in the overall holding:
λ=
n
∑
i=1
uiλiei. (17)
Similarly, the average velocity of circulation of the whole financial economy is given by
VF=
n
∑
i=1
uivi. (18)
Arbitrage occurs across assets within an asset class vector, and then between asset
classes within the vector holding all asset classes according to future price change expecta-
tions for the asset and the asset class, respectively.23 The ultimate safe asset is cash.
3.5. Crisis Onset and the Minsky Moment
Market stress events such as those shown in Figures 7and 8arise when there is a
need to liquidate assets to meet margin calls (Kahraman and Tookes 2020) and avoid future
losses. When asset prices fall, expectations of further price falls trigger more sales in that
asset class, leading to a cycle of declining prices. As asset sell-offs continue, if these pass a
certain expectation threshold, they can generate a vicious cycle of contagion into adjacent
asset classes, leading to an overall price crash. In other words, as volatility rises beyond
the threshold in one asset class, investors move to sell assets in other asset classes that are
now perceived to be vulnerable, thereby reducing the overall leverage and the associated
monetary base in the financial sector. An increase in the financial economy velocity of
circulation is a likely symptom of this process unfolding. This demonstrates how financial
economy liquidity varies pro-cyclically in direct relation to the expected overall growth or
fall in the market (Adrian and Shin 2010) and the type of asset (class) involved. When all
else fails, a dash for cash results. Our financial crisis, the Minsky Moment, has arrived.
In the lead up to the Minsky Moment, financial structures become unstable because
underlying cashflows from the real economy do not support financial-economy asset
21 Short-term τchanges can be later reflected into M2 changes (Tarassow 2019).
22
Onset of the Euro-Dollar market in the 1960s, the dollar standard from 1971, the Depository Institutions Deregulation and Monetary Control Act of
1980, and the 1999 repeal of the Glass-Steagall Act.
23
Each asset carries a future price change expectation, either “inheriting” this from the asset class or having its own expectation. For example, in late
January 2021 GameStop had a different and more extreme price expectation than that of its asset class.
J. Risk Financial Manag. 2021,14, 129 21 of 28
valuations on a systemic basis. In turn, the negative price spiral and financial contagion
described above impact upon real-economy growth through associated wealth loss, loss
of confidence, reductions in equity and bond flows, commercial bank lending restrictions,
and loss of demand. The contagion from financial markets into real markets ceases when
all financial market agents have rebalanced across asset classes to reach their new desired
liquidity preference, but by then real-economy declines in aggregate demand have been
triggered and economic activity is contracting. Our economic crisis has arrived.
Central banks have a mandate to prevent financial instability by intervening to mod-
erate and stop financial contagion (Minsky 1982). In 2020, central banks used QE executed
through asset purchases from the financial economy to achieve this, by providing addi-
tional liquidity and raising asset prices, reducing volatility and healing financial markets
(see Figures 7–9). Notwithstanding the healing delivered to the financial economy, QE had
limited impact in the real economy due to the lack of automated connections between the
real and financial economies (Equations
(13)
and
(14)
), and the indirect nature of the wealth
effect. Figure 10 shows how, despite healing the financial economy, there was an increasing
divergence from the real economy. QE had failed to reach the real economy, generating
concern that inflated asset prices in the financial economy are not supported by underlying
real economy cashflows and therefore represent a bubble.
3.6. Healing the Real Economy
It is by aligning the productivity and cashflows generated from value added in the
real economy to asset values in the financial economy that financial stability is assured and
the real economy healed. Without this alignment, crashes act as a necessary mechanism to
force adjustment. In the long run market mechanisms dictate broad alignment between
asset values and underlying cashflows. Adjustment can be gentle through accommodative
regulation or brutal through crashes as asset price expectations realign to fundamentals
(Culkin and Simmons 2019;French 1988). In spite of central banks’ best intentions, in
the main, QE protects the stability of financial markets without healing the real economy
and/or raising its growth rate, thereby setting up the next financial bubble and sowing the
seeds of the next crash.
Avoiding this cycle of boom and bust requires mechanisms to (i) support and raise
real-economy growth rates and (ii) heal the real economy in the event of a crash. The main-
spring of these mechanisms is to ensure the necessary flow of capital to the entrepreneurs,
innovators, SMEs, and high-growth companies to drive growth and enable cashflows in the
future. Current monetary transmission channels fail to do this, given investor preferences
for (i) known cashflows and (ii) making capital gains on existing assets. Additionally, de-
minimus boundaries prevent smaller firms and innovators accessing capital markets, and
regulatory Basel risk weights penalize certain forms of business lending and trade credit.
Inadequate demand in the face of falling ‘neutral interest rates’ then drives economies into
demand-deficient secular stagnation (Summers 2015). In the face of the failure of QE to
heal the real economy, there are two conventional monetary responses.
First, following Friedman (1969) ‘helicopter money’ drops to consumers are made
to drive activity and capital investment in the real economy. Second, in accordance with
Modern Monetary Theory (MMT) and Neo-Keynesian policy, some argue that monetary-
financed increases in
G
lift output through an increase in demand. Both proposals suffer
from a lack of an automatic connection to raising long-term
J
and
α
(traded GVA and
productivity), because raised consumption does not necessarily lead to higher capital
investment and associated productivity increases in traded GVA. In today’s globalized
world, much consumption spending is on imports that passes increase in demand to third
countries instead of raising domestic value added. Additionally, with ageing populations,
increases in savings can also be a significant consumer response.
Evoking a response to QE in the real economy requires targeted intervention to address
three specific scenarios:
J. Risk Financial Manag. 2021,14, 129 22 of 28
•
a shortage of liquidity (working capital) on the supply side; here targeted intervention
during the same circulation should lead to a rapid upward adjustment of
J
, since
freeing the working capital constraint should lead to a significantly larger number of
supply-chain transactions;
•
a shortage of demand due to a lack of purchasing capability: this is an area that
traditional helicopter money has targeted;
•
a shortage of capacity or lack of productivity on the supply side due to financing
constraints on new capital investment. Investment is needed to raise both productivity
and capacity of the supply side. There will be some increase in
J
during the same
circulation due to expansion activities (building, new machinery, etc), but the increase
in
J
will keep going in subsequent years due to the greater output that the larger
capacity affords. This third intervention increases the value of our coefficient α.
These challenges call for the adoption of ‘smart helicopter money’
(Simmons et al. 2020)
,
which is expected to be more effective in raising domestic economic growth since it directly
targets an increment in
J
. To achieve this we identify two possible frameworks for ‘smart
helicopter QE’, which can deliver monetary expansion directly to needy entrepreneurs by
establishing two new QE transmission channels.
First, drawing upon the experiences of both Italian mutual credit innovator Sardex and
the WIR Bank in Switzerland, locally-issued business-focused community and complementary
currencies provide support to SMEs. Sardex is an electronic, account-based, non-convertible
currency pegged 1:1 to the Euro and integrated into a mutual credit circuit in Sardinia that
supports mainly business-to-business (B2B) transactions. WIR is a complementary currency
started in 1934 only open to SMEs. Both Sardex and the WIR have been shown to make
strong positive counter-cyclical growth contributions by enabling the creation of interest-free
“mutual” credit by cash-starved SMEs who are underserved by the commercial banking
system and modern capital markets (Dini and Kioupkiolis 2019;Littera et al. 2017;Stodder
and Lietaer 2016;Studer 1998). By establishing limited convertibility for the community
tokens into national currency via a QE-funded conversion fund, community credits can be
integrated as an additional monetary transmission channel to target SMEs’ working-capital
needs. An additional implementation of these tokens would be to issue them as a consumer
helicopter drop, that forces consumers to spend the tokens with local SME businesses. We call
these Smart Helicopter Tokens.
Second, QE can feed a state (or not-for-profit) fund, to finance established venture
and risk capital providers with “low-cost” commercial debt funding in return for a small
on-going interest payment
24
and a modest share of future profits related to this finance, on
condition that the funds are invested into innovators, SMEs and high-growth companies.
The VCs identify and invest in such worthy companies with either equity or convertible
debt, based upon firms’ business plans and anticipated enterprise valuations. The venture
capital firms reap rewards through exits, dividends, further funding rounds, loan repay-
ments, etc and they in turn repay the state/not-for-profit fund with ongoing interest, the
principal, and a modest share of the related profits to help the fund build future capital
reserves to support future activity (Culkin and Simmons 2018). This is a hybrid develop-
ment of existing funding initiatives made by both the European Investment Bank and the
British Business Bank. Table 6summarizes the greater impacts that smart helicopter money
is expected to have on increasing the output of the real economy, relative to traditional QE
and other interventions.
For completeness we mention alternative interventionist central banking models visible
in Asia, where ‘Structural Monetary Policy’ is used to proactively support certain sectors via
shadow banks with a specific focus on stimulating demand through mainly real estate, (cen-
trally identified) key sectors, and infrastructure development (Chen et al. 2018;Economist
2021a;Yang et al. 2019). The model has been very effective at maintaining and raising
growth rates (even after 2008), but it can and does lead to overbuilding in some sectors that
24 Say 50 or 100 basis points over the costs of the funds.
J. Risk Financial Manag. 2021,14, 129 23 of 28
is then followed by consolidation. And the complex shadow banking transmission channels
have from time to time needed state support to remain stable.
Table 6. Impacts of different monetary policy interventions.
Impact on: Real Economy Monetary Mass Leakages Other
Intervention Method CTraded
GVA
non-
TGVA MReal
Econ.
Fin.
Econ. Im p Saving α(Prod-
uctivity)
Capital
Gain
Additional working capital Mutual credit +
VC Conv. Debt + + +
Friedman helicopter drop Cash to consumers + + + + + + + ?
Smart helicopter working
capital Convertible tokens + + + + + + ?
Smart helicopter tokens Tokens for
domestic spend + + + + + + ?
Smart helicopter investment
cash VC equity & debt + + + ?+?
Conventional QE Asset purchases + + +
Rise in GMMT ?+ + ?
Structural monetary policy Asian development
model + + + + ? ? ?
4. Materials and Methods
4.1. Economic Analysis
In this paper, we make extensive use of three economic concepts:
•
Circulation. The term circulation is used by both Keynes (1930) and
Schumpeter (1934)
.
In Keynes’s case he uses it to split the real from the speculative economy and as a tool
to understand monetary flows and income distribution. Schumpeter uses it to express
a trading flow through a series of Walrasian-type self-clearing markets.
•
Quantity Equation. We make extensive use of the Fisher (1911) Quantity Equation.
This identity offers many insights when it is combined with understanding the defini-
tional challenges relating to which transactions are implicated in a monetary circula-
tion. Specifically, we believe that transactions associated with GDP only tell part of
the story, which is why there is a need to incorporate the financial economy explicitly
in the analysis.
•
Money Creation. We embrace concepts of both endogenous (Hahn 1924) and central
bank exogenous (QE) (Joyce et al. 2011) money creation as being appropriate in specific
settings. We do not seek or attempt to add to the very extensive literature on money
demand equations.
Although temporal and time-dependent variables are discussed and although we
fully acknowledge the importance of time and uncertainty in a comprehensive dynamic
schema (Knight 1921), we do not address time explicitly in our analysis. This is an area for
further development.
4.2. Data Sources
All our data sources are credited on the charts, and the data sets come from known
credible sources, the links for which are in the bibliography. We have not undertaken
complex statistical analysis, as this article is focused on developing the theory. We would
welcome a follow-on econometric step of our analysis.
5. Conclusions
This paper has sought to establish through theoretical reasoning and qualitative empir-
ical time series analysis that there is a disconnect between the real and financial economies.
This disconnect, reinforced by the failure to measure changes in asset wealth in conven-
tional output measures, has two major impacts. First, it is responsible for the inherent
instability of the financial markets through the misallocation of capital into financial assets
and asset bubbles. Second, it is responsible for the inefficacy of traditional QE interventions
in achieving their ultimate intended goal: to restart the real economy during a crisis. In
other words, the disconnect perpetrates the misallocation of capital away from innovators,
J. Risk Financial Manag. 2021,14, 129 24 of 28
SMEs and high-growth firms, thereby slowing economic growth and further raising risks
of a crisis. We propose new smart helicopter QE transmission to remedy these points.
The analysis shows that our three smart helicopter money transmission channels, shown
in Table 6, complement the efforts and policies to meet the current central bank imperative to
maintain financial market stability, and they do this by providing monetary stimulus directly
to real-economy wealth creators. The methods we advocate are tried-and-tested market
mechanisms that focus support on viable growth prospects and minimize the moral hazard
risks associated with state support. Our approach aligns with suggestions that central bank
targeting needs to be widened from an inflation-only target to include growth and financial
stability. A possible limitation of such a widening and of our approach is the extent to which
institutional frameworks have evolved to integrate monetary and fiscal policy actions into
single targeted interventions. Over time it is firms in the real economy and generating growth
that help reduce financial market bubble pressure, since investors’ return expectations are
then better met by higher cashflows created in the real economy.
Author Contributions:
Conceptualization, R.S., P.D., N.C. and G.L.; methodology: R.S. and P.D.;
formal analysis: P.D.; data curation: R.S. and P.D.; writing–original draft preparation, R.S. and P.D.;
writing–review and editing, R.S., P.D., N.C. and G.L.; visualization: P.D. and R.S. All authors have
read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Acknowledgments:
Publication of this paper was financially supported by Monneta (https://
monneta.org/, accessed on 18 March 2021), a German not-for-profit organization, and the De-
velopment Economics Research Group at the International Institute of Social Sciences of Erasmus
University Rotterdam (https://www.iss.nl/en, accessed on 18 March 2021).
Conflicts of Interest:
The authors declare no conflicts of interest. P.D. worked as a part-time R&D
consultant for Sardex S.p.A. from August 2016 to December 2019.
Appendix A. Definitions of Key Terms
Given the complexity of the discussion and the different interpretations of similar
concepts by different schools of thought, we provide definitions of some of the key terms:
•Collateral is pledged assets available to be sold to repay a debt.
•Financial Economy
is the set of markets where buyers and sellers trade assets for
both yield and capital gain.
•Gross Domestic Product
or GDP is defined by the IMF (2021) and equals Gross Value
Added which in turn equals Trading and Non-Trading Gross Value Added (
J
+
GNTS
).
•Leverage
is the financial multiplier that is applied to a ‘margin’ deposit in a trading
account and is underpinned by collateral security held against the permitted leverage.
•Lifetime Income
, sometimes referred to as the Permanent Income Hypothesis, refers
to the process economic actors use to plan savings, withdrawals and returns on assets
held to allow them to optimize their income over their whole expected life.
•Liquidity describes the free cash available to meet liabilities.
•Liquidity Preference
refers to economic actors’ preference to hold cash even if it
means they miss out on financial returns that could be available from holding other
assets.
•M1 is cash and money held in ‘on-demand’ bank accounts.
•M2
is M1 plus savings deposits, small (under US$100k) time deposits, and retail
money market funds.
•Margin
is the deposit that is made into a trading account that allows the trader to
trade a nominal value of specified assets that is up to some preset multiple of the
deposited margin. The margin is held as immediately liquidatable security by the
market maker (operator of the trading platform) against potential trading losses.
Should losses exceed the required ‘margin’ amount the trader is subject to ‘margin
calls’.
J. Risk Financial Manag. 2021,14, 129 25 of 28
•Margin Call
is a demand for an additional deposit into the trading account to cover
existing or anticipated trading losses.
•Minsky Moment
is when asset markets reset themselves to bring Ponzi asset financial
economy valuations into line with real economy cashflows (Minsky 1989).
•Monetary Mass
is the total value of the components of broad money as defined by
the IMF (2016). This includes monetary definitions that are then implemented by the
US Federal Reserve for M1, M2 and MZM but that differ from the implementations by
the European Central Bank and the Bank of England.
•MZM
is M2 less small-denomination time deposits plus institutional money funds
US Federal Reserve System (2020a).
•Natural Rate of Interest
is the risk-adjusted interest rate that matches the Marginal
Efficiency of Capital (Keynes 1936) meaning that the cost of finance equals the marginal
return on capital (Wicksell 1898).
•Neutral Rate of Interest
is the rate of interest needed to keep financial markets in
balance with inflation as per the central bank target inflation rate (Lavoie 2003).
•Non-Traded Gross Value Added
is the value added in the non-traded goods sector
(principally government-provided services) and is represented by the symbol
GNTS
for ‘non-traded services’.
•Portfolio Balance
refers to the process economic actors use to mix their asset holdings
so as to optimize their yield, risk and access to cash.
•Real Economy
is the set of markets in which consumers, firms, and government
economic actors buy and sell products and services from each other.
•‘Repo’
is a short-term (often under 24 hours) agreement to sell to and (via a reverse
repo) repurchase the previous repo’d security at a pre-agreed price.
•‘Reverse Repo’
is the original repo seller’s commitment to repurchase the security
after the specified time-period.
•Saving
means national income
Y
minus consumption
C
, where consumption includes
consumption by individuals, firms and households. In formal models the definition
requires adjustment for capital flows.
•Securities Settlement
is the period of time specified by a trading platform between
when a transaction is made and when it is netted against any others to establish an
amount to be paid to or received from the agent trading.
•Shadow Banking
denotes a group of financial institutions that mediate credit to
borrowers but who are not subject to banking regulations.
•Solvency
refers to the assets of an economic actor being sufficient to cover their
liabilities.
•Traded Gross Value Added represents the value added between inputs and outputs
in traded goods and services and corresponds to J.
•Velocity of Circulation
is how many times the monetary mass circulates in a given
period (or, more precisely, how many times each unit of account circulates, on average,
in a given period). Normally
V=
‘Income velocity of circulation’ and is calculated as
GDP/Monetary mass, whereas in other situations
V
could be defined as Transaction
volume/Monetary mass.
•Wealth Effect
is the indirect impact on the real economy of changes in the asset wealth
of an economic actor in the financial economy.
References
Adrian, Tobias, and Hyun Shin Shin. 2010. Liquidity and leverage. Journal of Financial Intermediation 19: 418–37.
Anderson, Richard G., Michael Bordo, and John V. Duca. 2015. Money and Velocity during Financial Crises: From the Great Depression
to the Great Recession. Available online: https://www.dallasfed.org/~/media/documents/research/papers/2015/wp1503.pdf
(accessed on 18 July 2020).
Arrow, Kenneth J., and Gérard Debreu. 1954. Existence of an Equilibrium for a Competitive Economy. Econometrica 22: 265–90.
Badarudin, Z. E., Mohamed Ariff, and A. M. Khalid. 2013. Post-Keynesian money endogeneity evidence in G-7 economies. Journal of
International Money and Finance 33: 146–62.
J. Risk Financial Manag. 2021,14, 129 26 of 28
BEA. 2020a. NIPA Handbook: Concepts and Methods of the U.S. National Income and Product Accounts. Available online:
https://www.bea.gov/resources/methodologies/nipa-handbook (accessed on 10 February 2021).
BEA. 2020b. Technical Note—Gross Domestic Product, Third Quarter of 2020 (Advance Estimate). Available online: https:
//www.bea.gov/sites/default/files/2020-07/gdp2q20_adv.pdf (accessed on 2 November 2021).
Bech, Morten L., Jenny Hancock, Rice Tara, and Amber Wadsworth. 2020. On the Future of Securities Settlement. Technical Report,
Bank of International Settlements. BIS Quarterly Review, March. Available online: https://ssrn.com/abstract=3561195 (accessed
on 18 March 2021).
Bernanke, Ben S. 2006. Monetary aggregates and monetary policy at the Federal Reserve: A historical perspective. Paper presented at
Fourth ECB Central Banking Conference, Frankfurt, Germany, November 9–10, vol. 10.
BOE. 1982. Supplementary Special Deposits Scheme. Available online: https://www.bankofengland.co.uk/-/media/boe/files/
quarterly-bulletin/1982/the-supplementary-special- deposits-scheme.pdf (accessed on 16 July 2020).
BOE. 2014. Money Creation in the Modern Economy. Available online: https://www.bankofengland.co.uk/quarterly-bulletin/2014
/q1/money-creation-in-the-modern-economy (accessed on 4 July 2020).
Bordo, Michael D., Lars Jonung, and Pierre L Siklos. 1997. Institutional Change and the Velocity of Money: A Century of Evidence.
Economic Inquiry 35: 710–24.
Borio, Claudio, and Piti Disyatat. 2010. TGlobal imbalances and the financial crisis: Reassessing the role of international finance. Asian
Economic Policy Review 5: 198–216.
Burgess, Stephen. 2011. Measuring financial sector output and its contribution to UK GDP. Bank of England Quarterly Bulletin 2011:
234–46.
Cagan, Phillip. 1956. The Monetary Dynamics of Hyperinflation. In Studies in the Quantity Theory of Money. Edited by Milton Friedman.
Chicago: University of Chicago Press.
Cartas, Jose, and Artak Harutyunyan. 2017. Compilation, Source Data, and Dissemination of Monetary Statistics. In Monetary and
Financial Statistics Manual and Compilation Guide. Washington, DC: International Monetary Fund. doi:10.5089/9781513579191.069.
Chen, Kaiji, Jue Ren, and Tao Zha. 2018. The nexus of monetary policy and shadow banking in China. American Economic Review 108:
3891–936.
Christophers, Brett. 2012. Anaemic geographies of financialisation. New Political Economy 17: 271–91.
Crawford, Corrine. 2011. The repeal of the Glass-Steagall Act and the current financial crisis. Journal of Business & Economics Research
(JBER) 9: 127–34.
Culkin, Nigel, and Richard Simmons. 2018. Mastering Brexits Through the Ages: Entrepreneurial Innovators and Small Firms—The Catalysts
for Success. Bingley: Emerald Group Publishing.
Culkin, Nigel, and Richard Simmons. 2019. Shock Therapy and Entrepreneurial Flare #Brexit. International Journal of Entrepreneurial
Behaviour & Research 25: 338–52.
Day, Richard H. 1994. Complex Economic Dynamics. Cambridge: MIT Press, vol. 1.
Debreu, Gérard. 1970. Economies with a Finite Set of Equilibria. Econometrica 38: 387–92.
Deleidi, Matteo, and Enrico S. Levrero. 2019. The Money Creation Process: A Theoretical and Empirical Analysis for the United States.
Metroeconomica 70: 552–86.
Dini, Paolo, and Alexandros Kioupkiolis. 2019. The alter-politics of complementary currencies: The case of Sardex. Cogent Social
Sciences 5. doi:10.1080/23311886.2019.1646625. Available online: https://www.tandfonline.com/doi/full/10.1080/23311886.201
9.1646625 (accessed on 18 March 2021).
Duca, John V. 2016. How capital regulation and other factors drive the role of shadow banking in funding short-term business credit.
Journal of Banking & Finance 69: S10–S24.
Economist. 2021a. China Leads in Precision-Guided Central Banking. Does It work? Free Exchange, February 13.
Economist. 2021b. Transfer of power. Finance and Economics, February 6.
FED. 2020. Money Stock Measures. H.6 Release Table 1 Footnotes. Available online: https://www.federalreserve.gov/releases/h6
/current/default.htm (accessed on 18 July 2020).
Feldstein, Martin. 1991. Reducing the Risk of Economic Crisis. Technical Report. Cambridge: National Bureau of Economic Research.
Feldstein, Martin S., and Charles Y. Horioka. 1980. Domestic saving and international capital flows. Economic Journal 90: 314–29.
FinancialStabilityBoard. 2020. Holistic Review of the March Market Turmoil. Available online: https://www.fsb.org/wp-content/
uploads/P171120-2.pdf (accessed on 17 November 2020).
Fisher, Irving. 1911. The Purchasing Power of Money: Its Determination and Relation to Credit, Interest and Crises. New York: Macmillan .
French, Kenneth R. 1988. Crash-testing the efficient market hypothesis. NBER Macroeconomics Annual 3: 277–85.
Friedman, Milton. 1960. A Program for Monetary Stability. New York: Fordham University Press.
Friedman, Milton. 1962. Capitalism and Freedom. Chicago: Chicago University Press.
Friedman, Milton. 1969. The Optimum Quantity of Money. In The Optimum Quantity of Money and Other Essays. Chicago: Adline
Publishing Company, pp. 1–50.
Friedman, Milton, and Anna J. Schwartz. 1982. Monetary Trends in the United States and the United Kingdom: Their Relation to Income,
Prices, and Interest Rates, 1867–1975. Chicago: University of Chicago Press (for Nat. Bur. Econ. Res.).
Galbraith, John Kenneth. 1961. The Great Crash, 1929: With a New Introduction by the Author. Boston: Houghton Mifflin.
J. Risk Financial Manag. 2021,14, 129 27 of 28
Goldstein, Itay, Jonathan Witmer, and Jing Yang. 2018. Following the Money: Evidence for the Portfolio Balance Channel of Quantitative
Easing. Technical Report, Paper No. 2018-33. Montreal: Bank of Canada.
Hahn, Frank. 1978. Exercises in Conjectural Equilibria. In Topics in Disequilibrium Economics. Edited by Steinar Strom and Lars Werin.
Hoboken: Blackwell, pp. 64–80.
Hahn, L. Albert. 1924. Geld und Kredit: gesammelte Aufsätze. Tübingen: J C B Mohr.
Hamilton, James D. 1989. The long-run behavior of the velocity of circulation: A review essay. Journal of Monetary Economics 23:
335–44.
Hayek, Friedrich A. 1960. The Constitution of Liberty. Chicago: University of Chicago Press. Available online: https://archive.org/
details/in.ernet.dli.2015.553409/page/n3 (accessed on 14 November 2020).
Heimer, Rawley, and Alp Simsek. 2019. Should retail investors’ leverage be limited? Journal of Financial Economics 132: 1–21.
Hicks, John R. 1937. Mr Keynes and the Classics: a suggested interpretation. Econometrica 5: 147–59.
ICMA. 2020. What Is Special in the Repo Market. Available online: https://www.icmagroup.org/Regulatory-Policy-and-Market-
Practice/repo-and-collateral-markets/icma-ercc-publications/frequently-asked-questions-on-repo/9-what-is- a-special-in-
the-repo-market/ (accessed 10 September 2020).
IMF. 2016. Monetary and Financial Statistics Manual and Compliation Guide. Available online: https://www.imf.org/~/media/
Files/Data/Guides/mfsmcg_merged-web-pdf.ashx (accessed 10 September 2020).
IMF. 2017. Quarterly National Accounts Manual. Available online: https://www.imf.org/external/pubs/ft/qna/pdf/2017
/QNAManual2017text.pdf (accessed 15 February 2021).
IMF. 2021. Gross Domestic Product: An Economy’s All. https://www.imf.org/external/pubs/ft/fandd/basics/gdp.htm (accessed
15 February 2021).
Joyce, Michael, Ana Lasaosa, Ibrahim Stevens, and Matthew Tong. 2011. The Financial Market Impact of Quantitative Easing in the
United Kingdom. International Journal of Central Banking 7: 113–61.
Joyce, Michael, Matthew Tong, and Robert Woods. 2011. The United Kingdom’s Quantitative Easing Policy: Design, Operation and Impact.
Technical Report, Bank of England Quarterly Bulletin. London: Bank of England.
Juniper, James, Timothy P. Sharpe, and Martin J. Watts. 2014. Modern monetary theory: Contributions and critics. Journal of
Post-Keynesian Economics 37: 281–307.
Kahraman, Bige and Heather Tookes. 2020. Margin trading and comovement during crises. Review of Finance 24: 813–46.
Karpoff, Jonathan M. 1987. The Relation between Price Changes and Trading Volume: A Survey. Journal of Financial and Quantitative
Analysis 22: 109–26.
Keynes, John M. 1930. A Treatise On Money Vols I and II. New York: Harcourt Brace.
Keynes, John M. 1936. The General Theory of Employment, Interest, and Money. London: Palgrave Macmillan.
Khan, Saleheen. 2017. The savings and investment relationship: The Feldstein–Horioka puzzle revisited. Journal of Policy Modeling 39:
324–32.
Knight, Frank Hyneman. 1921. Risk, Uncertainty and Profit. Boston: Houghton Mifflin, vol. 31.
Krippner, Greta R. 2005. The financialization of the American economy. Socio-Economic Review 3: 173–208.
Laidler, David. 2006. Woodford and Wicksell on Interest and Prices: The place of the pure credit economy in the theory of monetary
policy. Journal of the History of Economic Thought 28: 151–59.
Laidler, David, and George W. Stadler. 1998. Monetary Explanations of the Weimar Republic’s Hyperinflation: Some Neglected
Contributions in Contemporary German Literature. Journal of Money, Credit and Banking 30: 816–31.
Lavoie, Marc. 2003. Measuring the natural rate of interest. Review of Economics and Statistics 85: 1063–70.
Lerner, Abba P. 1938. Saving equals investment. The Quarterly Journal of Economics 52: 297–309.
Littera, Giuseppe, Laura Sartori, Paolo Dini, and Panayotis Antoniadis. 2017. From an Idea to a Scalable Working Model: Merging
Economic Benefits with Social Value in Sardex. International Journal of Community Currency Research 21: 6–21. Available online:
https://ijccr.files.wordpress.com/2017/02/littera-et-al.pdf (accessed on 18 March 2021).
Lucas, Robert E., Jr. 1976. Econometric Policy Evaluation: A Critique. In The Phillips Curve and Labor Markets. Edited by Karl Brunner
and Alan Meltzer. Carnegie Rochester Conference Series. New York: North Holland, pp. 19–46.
Markowitz, Harry M. 1952. Portfolio selection. Journal of Finance 7: 77–91.
Mas-Colell, Andreu. 1985. The Theory of General Economic Equilibrium: A Differentiable Approach. Cambridge: Cambridge University
Press.
Minsky, Hyman P. 1982. Can “It” Happen Again?: Essays on Instability and Finance. Armonk: M.E. Sharpe.
Minsky, Hyman P. 1989. The Financial Instability Hypothesis: A Clarification. Ph.D. thesis, Bard College, Annandale-On-Hudson, NY,
USA. Available online: http://digitalcommons.bard.edu/hm_archive/145 (accessed on 28 August 2020).
Muth, John F. 1961. Rational Expectations and the Theory of Price Movements. Econometrica 29: 315–35.
NASDQ. 2020. SPX Historical Data. Available online: https://www.nasdaq.com/market-activity/index/spx/historical (accessed on
2 November 2020).
Obstfeld, Maurice, and Alan M. Taylor. 2005. Global Capital Markets: Integration, Crisis, and Growth. Cambridge: Cambridge University Press.
Samuelson, Paul A. 1948. Economics, an Introductory Analysis. New York: McGraw-Hill.
Schumpeter, Joseph. 1934. The Theory of Economic Development. Cambridge: Harvard University Press.
J. Risk Financial Manag. 2021,14, 129 28 of 28
Sharma, Dhruv, Jean-Phillipe Bouchaud, Stanislao Gualdi, Marco Tarzia, and Francesco Zamponi. 2020. “V-, U-, L-or W-shaped
recovery after COVID?” Insights from an Agent-Based Model June 2020 SSRN 3627505. Available online: https://arxiv.org/pdf/
2006.08469.pdf (accessed on 22 November 2020).
Sifma. 2020. US Repo Market Fact Sheet. Available online: https://www.sifma.org/resources/research/us-repo-market-fact-sheet/
(accessed on 10 September 2020).
Simmons, Richard, Giuseppe Littera, Nigel Culkin, Paolo Dini, Luca Fantacci, and Massimo Amato. 2020. Helicopter, Bazooka or
Drone? Economic Policy for the Coronavirus Crisis. Available online: https://www.politico.eu/article/helicopter-bazooka-
drone-economic-policy-coronavirus-crisis-money// (accessed 10 February 2021).
Stamp, Josiah C. 1931. The report of the Macmillan Committee. The Economic Journal 41: 424–35.
Stodder, James and Bernard Lietaer. 2016. The Macro-Stability of Swiss WIR-Bank Credits: Balance, Velocity, and Leverage. Comparative
Economic Studies 58: 570–605.
Studer, Tobias. 1998. IWIR and the Swiss National Economy. Basel: WIR Bank.
Summers, Lawrence H. 2015. Demand side secular stagnation. American Economic Review 105: 60–65.
Tarassow, Artur. 2019. Forecasting US money growth using economic uncertainty measures and regularisation techniques. International
Journal of Forecasting 35: 443–57.
Taylor, John B. 1993. Discretion versus policy rules in practice. In Carnegie-Rochester Conference Series on Public Policy. North-Holland:
Elsevier, vol. 39, pp. 195–214.
Tesar, Linda L. 1991. Savings, investment and international capital flows. Journal of International Economics 31: 55–78.
Thornton, Daniel L. 2014. QE: Is There a Portfolio Balance Effect? Technical Report, Review 96, No. 1. St. Louis: Federal Reserve Bank of
St. Louis.
US Federal Reserve System. 2020a. Institutional Money Funds [WIMFNS], M2 Money Stock [WM2NS], Total Cash Balance [GDTCBW],
General Account Balance at Federal Reserve [GDBFRW], Time and Savings Deposits at Commercial Banks [GDTSDCBM] &
Demand Deposits at Commercial Banks [GDDDCBW] Money Zero Maturity [MZM]. Retrieved from FRED, Federal Reserve
Bank of St. Louis. Available online: https://fred.stlouisfed.org/series/NBCB (accessed on 9 September 2020).
US Federal Reserve System. 2020b. Quarter 2, 2020, Z.1 Financial Accounts of the United States (Sept 21, 2020). Available online:
https://www.federalreserve.gov/apps/fof/FOFTables.aspx (accessed on 1 October 2020).
Werner, Richard. 1997. Towards a New Monetary Paradigm: A Quantity Theorem of Disaggregated Credit, with Evidence from Japan.
Kredit und Kapital 2: 276–309.
Werner, Richard. 2012. Towards a New Research Programme on ‘Banking and the Economy’—Implications of the Quantity Theory of
Credit for the Prevention and Resolution of Banking and Debt Crises. International Review of Financial Analysis 25: 1–17.
Werner, Richard. 2014. How do banks create money, and why can other firms not do the same?An explanation for the coexistence of
lending and deposit-taking. International Review of Financial Analysis 36: 71–77.
Wicksell, Knut. 1898. Interest and Prices. Translated by Richard Kahn. London: Macmillan for the Royal Economic Society.
Yang, Liu, Sweder van Wijnbergen, Xiaotong Qi, and Yuhuan Yi. 2019. Chinese shadow banking, financial regulation and effectiveness
of monetary policy. Pacific-Basin Finance Journal 57: 101169.
