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12
Do Fintech Activities Aect
Monetary Policy?
Muhammad Zubair Mumtaz,
Zachary A. Smith, and Zafar Mahmood
12.1 Introduction
Over the last decade, the emergence of new financial technologies
(fintech) has posed serious threats and challenges to the structure of
the financial sector. The evolution of electronic money, digital banking,
crowdfunding platforms, and distributed ledger technology raises
various concerns for the banking industry and macroeconomic policy
(Paternoster and Dessimirova 2017). The innovation in fintech has
tremendously changed the accessibility of financial services. For instance,
peer-to-peer lending permits customers to get funds in the absence
of a banking channel, new modes of payments (e.g., mobile software)
allow people to transact through a smartphone, and distributed ledger
technology (e.g., blockchain) facilitates new means of reporting and
executing transactions. These developments have started to influence
the ability of central banks to conduct monetary policy eectively with
a goal of financial stability.
In a Global Fintech Summit, Jun Zhu, director-general of the
International Department at the People’s Bank of China claimed that,
“the growth of fintech will have an impact on the formulation and
implementation of monetary policy” (CBN 2018). He emphasized that
the growth of fintech will generate competition in the financial sector
and cause the market to become more responsive to interest rates.
This implies that technological innovation increases the sphere of
intangible assets that are likely to aect the transmission mechanism of
monetary policy. Zhu also argued that fintech could aect price changes
based on targets set under monetary policy, and this aspect becomes
more prevalent through real-time changes in goods and services via
Do Fintech Activities Aect Monetary Policy?!329
algorithmic technology that may have a significant eect on inflation
(CBN 2018).
On another front, International Monetary Fund Managing Director
Lagarde (2017) claimed that virtual currencies pose little to no threat to
fiat currencies and central banks because they are too volatile, cost too
much to maintain, and are not scalable; however, she envisages a future
in which countries with weak central banks move to this type of digital
currency as the technology evolves. She refers to this as dollarization
2.0, a time in which countries with weak institutions rely on something
other than the dollar or a foreign government’s currency to provide the
necessary functions of money that its citizens demand. She continues to
provide an example of Seychelles from 2006 to 2008 and indicates that
the dollarization of its currency rose from 20% to 60% during this time.
Further, she claims that this movement from current forms of money
could improve payment systems by reducing costs for transfers of funds
for simple transactions.
Tule and Oduh (2017) examined the impact of financial innovations
on the monetary policy in Nigeria during the period from January
2009 to February 2015. They argued that, “the constant substitution
of e-money for cash among other things, will enhance the eciency
of production, strengthen the interest rate channel of monetary policy
transmission and reduce the eects of price on money demand” (p. 472).
Alternatively, it is challenging for the central bank to control the money
supply through its influence on the operating goal, surges in the velocity
of money, interest rate elasticity of the demand for money, and the cost of
monetary policy arising from the trade-o between inflation and output.
The emergence of technological innovations in the financial sector
aected the dynamics of businesses as well as the banking system.
Digital currencies are used as a model of modern technology to transact
business, which may aect the demand for and the supply of money. To
raise this concern, it is important to analyze how markets are reacting
to fintech instruments. The purpose of this study is to examine the
impact of fintech on the monetary policy in developed countries. We
consider 25 developed economies and identify the income velocity
and money multiplier before and after the initiation of fintech. The
results report no change in income velocity and the money multiplier
after the era of fintech. The other objective of the study is to investigate
the money demand using the generalized method of moments (GMM)
estimator. We identify that gross domestic product (GDP), real interest
rates, inflation, and wealth are significant determinates. When fintech
instruments are included, the results indicate that mobile technology,
internet technology, and Bitcoin are the robust predictors of money
330!Macroeconomic Stabilization in the Digital Age
demand. We also examine the product market equation and cost function
and determine that after the start of fintech activities, monetary policy,
as proxied for through interest rates, may have an insignificant eect.
Moreover, we report that Bitcoin, Ethereum, and Ripple are robust
determinants of the output gap. Further, we interact fintech instruments
with interest rates, and report that the transmission mechanism of
monetary policy may influence the mobile and internet technology,
Ethereum, and Ripple. Finally, we investigate the innovations of fintech
instruments and find that GDP, real interest rates, inflation, and financial
development indexes are the significant factors.
The remainder of this chapter is structured as follows. Section
12.2 provides an overview of the channel of money demand and supply
in terms of fintech. Section 12.3 describes the overview of financial
technology. Section 12.4 discusses the research methodology and data,
Section 12.5 examines the empirical results, and Section 12.6 concludes
the study.
12.2 What is Fintech and How Does It Aect
the Monetary Policy?
Fintech is generally referred to as “a portmanteau of financial
technologies,” which is characterized by the financial services of the
21st century. Initially, the innovation process supported and recognized
consumer and trade financial institutions, which eventually turned
into a full-fledged process by creating digital currencies like Bitcoin
and altcoin. Though there are various negative concerns associated
with the cryptocurrencies, fintech products aect the sales of banking
products, and speed up innovation. Prior studies (Bernanke and Blinder
1988; Arize 1990) explored the influence of financial innovation on
the monetary policymaking and the role of central banks. Due to the
emergence of the innovation process, central banks are supposed to
have a pivotal role in conducting monetary policy after the collapse
of the Bretton Woods system (Arize 1990). It seems as though there
is a complicated relationship between the central bank and its ability
to implement monetary policy, which greatly influences the supply of
money or credit throughout the financial system.
12.2.1 The Supply Side
In line with this general idea that central banks had some control
over the money supply, Werner (2016) sought to answer the following
question: do banks lend existing money or create money? This led to
Do Fintech Activities Aect Monetary Policy?!331
a more nuanced understanding of what the central bank’s role is as a
supplier of credit and how much influence it might have over the money
supply. Werner (2016) indicates that over the last century, there have
been three dominant theories about banking’s role in the economy. The
first is that they act as intermediaries and collect deposits, then lend
them out to consumers. The second is that banks on their own are not
able to create money, but do so collectively through the fractional reserve
system and the multiplier eect. The third suggests that banks do not act
collectively, but each individual bank creates credit and money when
they make loans. Based on an empirical test and case studies, Werner
(2016) indicates that the first two theories are rejected and found
empirical evidence supporting the third (similar to Werner 2014).
Traditionally, the suppliers of credit have been banking institutions,
not the central bank. Brunnermeier and Sannikov (2016) provide a good
overall summary of the role that banks play in terms of monetary policy
and the creation of money. According to them, their role is to, “take stakes
in the households’ risky projects, absorbing and diversifying some of the
households’ risk. They are active in maturity and liquidity transformation,
as they issue liquid, at notice redeemable, (inside) money and invest in
illiquid long-term investments.” By creating money, the banks take on
risks associated with the mismatch between assets and liabilities. When
the economy is functioning properly, this process of banks creating inside
money for individuals to allow them to make investments in projects that
they would otherwise be unable to aord is eective; however, when the
economy contracts, banks shrink their balance sheets, the availability
of inside money decreases, and the demand for money increases. This
is when outside money becomes valuable (i.e., provided by a central
authority). They highlight two cases: (i) one in which the banks have
sucient capital and are able to provide households with that capital to
invest in projects; and (ii) one in which the banks do not have sucient
capital (either because they choose not to lend or their access to capital is
somehow restricted) to provide to the markets.
To understand the issues that are created from the supply side, this
chapter uses the convention provided in Braun (2016), which illustrates
the dierences between inside and outside money and indicates that
“legally” only outside money is legal tender. Further, he attempts to
discredit the myth that banks use deposits to create loans and suggests
that banks make loans, which creates a deposit by expanding its balance
sheet through making two osetting entries, i.e., a loan on the asset side
and a deposit on the liability side. Moreover, he contends that banks
loan funds and then borrow the necessary reserves to satisfy the United
States Federal Reserve (the Fed) requirements for making that loan.
So, the creation of inside money is not dependent on the amount of
332!Macroeconomic Stabilization in the Digital Age
outside money available in the system, but it is dependent on the
market’s demand for loanable funds. Similarly, he contends that external
money is not an exogenous variable under the control of the central
authority. Concisely, he explains that “both inside and outside money
are endogenous to the interaction of loan demand and lending behaviour
in the economy.” Finally, he explained that the monetization of “private
loans by making them exchangeable with sovereign promises to pay is
the hallmark of capitalist credit money, which finds its contemporary
expression in central banks’ collateralized open market operations. In
this public-private partnership, demand deposits created through bank
loans make up the largest part of the ‘privately contracted debts’ that
circulate as money.”
Prior to fintech and innovations associated with nontraditional
lenders, the implementation of monetary policy through more traditional
measures (i.e., changing the reserve requirements and increasing or
decreasing the monetary base to influence the Federal Funds Rate) seemed
like adequate measures to take to successfully implement monetary
policy actions. However, as these alternative credit facilities enter the
market and are governed by a separate, laxer, set of regulatory oversight,
the eectiveness of monetary policy actions could be stymied. Lucas and
Nicolini (2015) contend that the relationship between monetary aggregates
(such as M1, M2, and the Monetary Base) and prices and interest rates
deteriorated in the 1980s and have yet to be re-established (this point is
revisited in the section about demand). Therefore, when implementing
monetary policy actions, central bankers rely mainly on interest rates as
a tool to intervene in markets; hence, monetary aggregates seem to be
of little interest to those that implement monetary policy. However, this
transmission channel seems to be less eective in a system that has ample
liquidity, as is evident today. Wolla (2019) contends that, in an environment
where there is ample liquidity flowing through the system, small changes
to the supply of reserves will not aect interest rates, in the case of the
United States (US), the Federal Funds Rate. To overcome this obstacle, in
the US, the Fed has created new rates (i.e., Interest on Excess Reserves,
and Overnight Reverse Repurchase Agreements), which it uses to create
arbitrage opportunities in the marketplace to force banks to either reduce
or expand their borrowing and lending activities. The tools that central
banks have previously used to implement monetary policy initiatives
seem to be changing and the traditional banks are losing market share
to credit- (i.e., money-) creating facilities in the fintech space; these two
taken together seem to have some ramifications for the financial markets
and the implementation of monetary policy.
As was noted in the introduction, from 2007 to 2015, the shadow
banking industry’s market share grew from 14% to 38% in the US
Do Fintech Activities Aect Monetary Policy?!333
(Buchak et al. 2018). Given that the banks and, by association, the
shadow banks, play a pivotal role in creating money and the fact that
these nontraditional banks are exposed to a dierent set of standards
and oversight, the increase in the market share of this sector leads to
questions about how this rise is likely to aect the traditional channels
that central banks use to implement monetary policy. There is already
evidence that some of the more traditional methods are failing
because there is excess liquidity throughout the financial system, i.e.,
manipulation of the Federal Funds Rate, and some of the institutions
issuing credit are not exposed to the same requirements as traditional
banks. In light of these emerging issues, this chapter examines how
fintech and the rise of the shadow banking industry have influenced
the money supply.
12.2.2 The Demand Side
Braun (2016) shows how two primary objectives of money are to
strike a balance between elasticity and the public’s trust in that money.
Hendrickson and Salter (2018) state that a “key factor underlying
the utility of money is imperfect commitment” (p. 23). Further, they
contend that if individuals knew exactly what they were willing to trade
for future actions, then all transactions would be executed on credit;
however, in the face of uncertainty, money is necessary to facilitate
exchange and it expands the individual’s opportunity set. In their
opinion, banks exist because of imperfect commitment and uncertainty,
because if individuals were certain about their future needs, they would
convert a certain portion of their savings to outside money and save the
remaining in interest-bearing deposits; as financial innovation persists,
the boundaries between these two assets seem to be disappearing.
Reiss (2018) claims that the movement toward a cashless society
is neither new nor disruptive, but it has been occurring gradually, with
money already becoming digital. Further, he contends that the monetary
authorities are already aware of this change. In addition, Reiss (2018),
in regards to the demand for money, illustrates that the ratio of cash in
circulation outside of banks to broad money (i.e., M4) has been relatively
stable from 2006 to 2015 for countries across the globe with a few minor
exceptions. The three reasons that they provide for why people rely
on an actual currency are: (i) as a medium of exchange when no other
forms of payment are acceptable; (ii) as a protection against institutional
instability throughout the financial sector; and (iii) for privacy concerns.
Lucas and Nicolini (2015) illustrate a negative relationship between
the demand for currencies and M1 over GDP from 1915 to 1980, but
indicate the breakdown in this relationship from 1980 onward, primarily
334!Macroeconomic Stabilization in the Digital Age
due to the definition of M1 and M2 and the fact that the demand for
“deposits” has shifted to a demand for money market deposit accounts
since the 1980s. After redefining their definition of M1 to include money
market deposit accounts, the negative relationship between the ratio
of M1 to GDP compared to its opportunity cost (measured by interest
rates) remains significant over time. The main purposes of highlighting
this finding is to illustrate, as both Ireland (2015) and Lucas et al. (2015)
relate, that the standard definitions associated with published monetary
aggregates typically fail to illustrate a continued relationship between
the monetary aggregates and short-term interest rates, which would
question the empirical value of the quantity theory of money. However,
as Lucas et al. (2015) illustrate, the relationship between rates and the
demand for money is still apparent in practice, but financial innovations
aect this relationship and how the theory works empirically.
Ireland (2015) indicates that the quantity theory of money links
actual demand to the nominal money supplied by the banks and
government through inflation, which provides its sound macroeconomic
base. Further, he contends that, normally, central bankers refer to and
rely on rates to make adjustments in policy throughout the economy
and ignore discussions of the money supply; however, empirically,
studies that focus explicitly on engaging in monetary policy actions
during times of hyperinflation, central bankers implement policies to
restrict the money supply to curtail inflation. Ireland (2015) questions
whether monetary policy should be “summarized on observations of
interest rates alone” (p. 67), as the New Keynesian models used today
seem to suggest. He indicates how the Lucas et al. (2015) model, which
illustrated that money demand has been relatively stable over time by
creating a new monetary aggregate (similar to Reiss 2018), relates the
velocity of money to short-term interest rates as the quantity theory of
money predicts, and overcomes objections raised by economists that
the association between the monetary aggregate and rates is no longer
intact based on their use of traditional proxies for monetary aggregates
(Goldfeld et al. 1976). This is because the traditional proxies do not
seem to appropriately incorporate alternative forms of money, which
are becoming very close substitutes to money as financial innovation
changes the monetary policy landscape.
To summarize, the definition of money, both in physical terms and
how policy makers measure it, is changing as financial innovations
occur throughout the system. The underlying relationships that have
been established through empirical studies and theoretical debates
pertaining to the quantity theory of money and the relationship between
the demand for money, inflation, and rates are still intact; however, the
definitions that policy makers use to refer to money aggregates have to
Do Fintech Activities Aect Monetary Policy?!335
change as financial innovations occur in the economy. As the definition
of money evolves, the channels that consumers use to access that money
is likely to change as well. Policy makers should be more cognizant of
the underlying changes in the alternative definitions of money and their
potential ramifications for monetary policy as they relate to the quantity
theory of money.
12.3 Overview of Financial Technology
In this study, we consider the instruments of financial technology
(fintech), which include mobile technology, internet technology, Bitcoin,
Ethereum, Litecoin, and Ripple. This section specifically emphasizes
digital currencies—more specifically, digital or virtual currencies
that use cryptographic encryption techniques to generate units of the
currency and verify transactions.
Digital currency is a broad term that can contain anything that
represents value in a digital manner. Digital currency can contain what
we would call electronic money, which is simply a digital representation
of government-issued fiat currency; it can also cover virtual currency
known as electronic currency that is not considered legal tender. Virtual
currencies are controlled and created by their developers, with the value
being appreciated in a specific community.
Bitcoin is an encrypted currency and a payment system. It was
invented by an unidentified programmer or a group of programmers
under the name Satoshi Nakamoto. Bitcoin was introduced into a
cryptography mailing list on 31 October 2008, and was released as open-
source software in 2009. There are various theories and speculations
about Nakamoto’s identity, but none has been confirmed. The system
is peer-to-peer and trades are made directly between users without
intermediaries. These transactions are verified by network nodes and
recorded in a public distributed ledger called blockchain, which uses
Bitcoin as the unit of account. Because the system operates without a
central repository or a single administrator, the US Treasury classified
Bitcoin as a decentralized virtual currency. Bitcoin is often called the
first cryptocurrency, although there were previous systems. It is more
accurately described as the first decentralized digital currency; Bitcoin
is the largest of its kind. The rest of the digital currencies are gaining
popularity.
Figure 12.1 presents the position of closing prices of digital currencies
used in this study. Bitcoin is the older version of the cryptocurrency
where closing prices were at an all-time high from 2013 to 2018.
Litecoin is another digital currency, introduced in 2013; however, its
closing prices are the lowest among other similar digital currencies. In
336!Macroeconomic Stabilization in the Digital Age
January 2015, Ripple was launched and we can see from Figure 12.1 it is
competing with other digital currencies. From our sample, Ethereum is
another popular cryptocurrency that came onto market in August 2015;
still its prices are compatible from the perspective of the market. While
Bitcoin is receiving the attention and interest of investors, other digital
currencies are also competitive, and their flows will further be enhanced
once they are regularized by the concerned authorities.
12.4 Methodology and Data
To examine the eect of financial innovation on the environment of
monetary policy, this study follows the categorization proposed by
Meltzer (1978): (i) financial innovations that overcome the regulatory
and legal restrictions which may occur in the non-existence of
restrictions; (ii) financial innovations that enhance lending and
borrowing possibilities which may influence demand for and supply
of money; and (iii) financial innovations that have instant eect on a
respective kind of institution which may aect the equilibrium of the
entire economy.
In terms of financial innovations, earlier studies (Tule and Oduh
2017; Lenka and Bairwa 2016) used proxies like the number of ATMs,
Figure 12.1: Pricing Pattern of Digital Currencies
Source: Authors.
20
16
12
8
4
0
7/17/10 7/17/11 7/17/12
Bitcoin (000) Ethereum (000) Litecoin (000) Ripple
7/17/13 7/17/14 7/17/15 7/17/16 7/17/17 7/17/18
Do Fintech Activities Aect Monetary Policy?!337
point of sales transactions, and online transactions. Since then, there has
been a substantial change in the context of financial innovation. Over
time, the surge of fintech aects the financial structure, as consumers
are using digital currencies for placing their transactions outside the
scope of the banking industry. These kinds of business transactions
ultimately influence the demand for and supply of money. In this
study, we use fintech as a proxy of digital currencies and mobile and/
or internet banking to examine their role on money demand and output
gap to determine the transmission mechanism of monetary policy. The
ensuing subsections elaborate on the modelling framework, sample,
and data.
12.4.1 Modelling Framework
This study develops a two-tier model. In the first tier, the surge of money
supply (M2) around the world is the result of financial and technological
innovation, and this is investigated by employing: (i) the stability of
income velocity of money and the money multiplier; and (ii) the stability
of demand for money function. The other tier of the model examines
whether fintech activities influence the transmission mechanism of
monetary policy.
The stability of income velocity of money (
υ
)and the money
multiplier (m) are measured through trend (
τ
) and can be expressed as:
() () ()
= ++ (1)
= ++ (2)
()= +ln()+ + ln()+ +++ (3)
() () () () () () ()
()= +()+ +ln ()
+ +++
() () ()
()= +()+ () + ()
+ +()+++
()= +()+
+ +
+ln( )
+ln( )
+()
+ ()
+ ()
+ () + ++
() ()
= ()
=+ + +++ (7)
(1)
() () ()
= ++ (1)
= ++ (2)
()= +ln()+ + ln()+ +++ (3)
() () () () () () ()
()= +()+ +ln ()
+ +++
() () ()
()= +()+ () + ()
+ +()+++
()= +()+
+ +
+ln( )
+ln( )
+()
+ ()
+ ()
+ () + ++
() ()
= ()
=+ + +++ (7)
(2)
Analyzing how fintech aects the money demand function shows
how monetary policy influences inflation and output and how the eect
of the financial system on growth and development can be observed
in the behavior of capital markets and real balances. Previous studies
(e.g., Arrau and De Gregorio 1993; Tule and Oduh 2017) argued that
long-run money demand function is normally distinguished by periods
of “missing money”, unsteady factors, and autocorrelated errors. This
consideration is important from the point of view that countries have
experienced significant variations in economic conditions and financial
markets. In light of these considerations, it is imperative to examine
whether monetary policy is influenced by fintech activities and the
stability of the demand for money function.
To examine the eect of fintech, we develop the money demand
function by considering before and after its initiation. We presume that
338!Macroeconomic Stabilization in the Digital Age
an equilibrium condition in the money market and the conventional
Keynesian real balances, with wealth eect, is written as:
() () ()
= ++ (1)
= ++ (2)
()= +ln()+ + ln()+ +++ (3)
() () () () () () ()
()= +()+ +ln ()
+ +++
() () ()
()= +()+ () + ()
+ +()+++
()= +()+
+ +
+ln( )
+ln( )
+()
+ ()
+ ()
+ () + ++
() ()
= ()
=+ + +++ (7)
() () ()
= ++ (1)
= ++ (2)
()= +ln()+ + ln()+ +++ (3)
() () () () () () ()
()= +()+ +ln ()
+ +++
() () ()
()= +()+ () + ()
+ +()+++
()= +()+
+ +
+ln( )
+ln( )
+()
+ ()
+ ()
+ () + ++
() ()
= ()
=+ + +++ (7)
(3)
where mit refers to the real demand for money that is formed as the
growing function of transactional (y) and uncertainty demand for money
(r), and a decreasing function of price (π). We generally assume that an
increase in income (δ0) raises the transactionary demand for money, an
increase in the opportunity cost of idle fund (γ0) reduces the uncertain
demand for money, whereas wealth eect (ϖ0) has a complex connection
with money demand (Friedman 1988). ξt represents specific time eects
and (υi) represents unobserved country eects. The simplified version
of the model can be expressed as:
() () ()
= ++ (1)
= ++ (2)
()= +ln()+ + ln()+ +++ (3)
() () () () () () ()
()= +()+ +ln ()
+ +++
() () ()
()= +()+ () + ()
+ +()+++
()= +()+
+ +
+ln( )
+ln( )
+()
+ ()
+ ()
+ () + ++
() ()
= ()
=+ + +++ (7)
() () ()
= ++ (1)
= ++ (2)
()= +ln()+ + ln()+ +++ (3)
() () () () () () ()
()= +()+ +ln ()
+ +++
() () ()
()= +()+ () + ()
+ +()+++
()= +()+
+ +
+ln( )
+ln( )
+()
+ ()
+ ()
+ () + ++
() ()
= ()
=+ + +++ (7)
() () ()
= ++ (1)
= ++ (2)
()= +ln()+ + ln()+ +++ (3)
() () () () () () ()
()= +()+ +ln ()
+ +++
() () ()
()= +()+ () + ()
+ +()+++
()= +()+
+ +
+ln( )
+ln( )
+()
+ ()
+ ()
+ () + ++
() ()
= ()
=+ + +++ (7)
(4)
We replace the Keynesian conjecture in Eq. (3) and permit
technological development in the form of financial technology (
η
). As
a result, fintech (
η
) will influence the trade-o between the Keynesian
transactionary (y) and uncertain (r) objectives of the demand for real
balances as:
() () ()
= ++ (1)
= ++ (2)
()= +ln()+ + ln()+ +++ (3)
() () () () () () ()
()= +()+ +ln ()
+ +++
() () ()
()= +()+ () + ()
+ +()+++
()= +()+
+ +
+ln( )
+ln( )
+()
+ ()
+ ()
+ () + ++
() ()
= ()
=+ + +++ (7)
() () ()
= ++ (1)
= ++ (2)
()= +ln()+ + ln()+ +++ (3)
() () () () () () ()
()= +()+ +ln ()
+ +++
() () ()
()= +()+ () + ()
+ +()+++
()= +()+
+ +
+ln( )
+ln( )
+()
+ ()
+ ()
+ () + ++
() ()
= ()
=+ + +++ (7)
() () ()
= ++ (1)
= ++ (2)
()= +ln()+ + ln()+ +++ (3)
() () () () () () ()
()= +()+ +ln ()
+ +++
() () ()
()= +()+ () + ()
+ +()+++
()= +()+
+ +
+ln( )
+ln( )
+()
+ ()
+ ()
+ () + ++
() ()
= ()
=+ + +++ (7)
(5)
We measure (
η
) in terms of mobile and internet subscribers and
digital currencies. Over the past few years, mobile and internet banking
enable customers to transact through the online system and the
cryptocurrencies provide the opportunity to buy and sell goods without
the financial system. The innovation of fintech influences the volume
of transactions, as well as the income velocity of money. The simplified
form of the model is written as:
Do Fintech Activities Aect Monetary Policy?!339
() () ()
= ++ (1)
= ++ (2)
()= +ln()+ + ln()+ +++ (3)
() () () () () () ()
()= +()+ +ln ()
+ +++
() () ()
()= +()+ () + ()
+ +()+++
()= +()+
+
+
+ln( )
+ln( )
+()
+ ()
+ ()
+ () + ++
() ()
= ()
=+ + +++ (7)
(6)
where GDPit is the natural logarithm of the GDP measured in real terms
of country i at time t, and rit refers real interest rate of country i at time t,
Wealthit is estimated as the logarithm of stock market indices,
π
it refers
to price level, and fintech instruments include mobile and internet
subscribers and digital currencies.
The other objective of this study is, thus, to examine the relationship
between fintech and monetary policy from two perspectives. First, we
formulate an association between the equilibrium interest rate to the
output gap. The other factor relates to the output gap that includes
the variables relating to fintech and an interaction term of fintech
with interest rates. The purpose of interacting both these parameters
is to examine the impact of fintech on the credit channel in terms of
transmission of monetary policy mechanisms (short-term interest
rates). The relationship can be examined by assessing two operational
equations: (i) the product market (IS) equation, and (ii) the cost function,
that is, the output gap-inflation expectation equation. The output gap is
defined as the dierence between real and potential output.
The output gap (yg!) is developed through the product market
equation, which is a function of one-period lagged of the output gap
and real interest rates. The output gap associates the relative demand
and supply factors of economic movement and evaluates the extent
of inflationary pressure in the economy, which connects inflation and
the real economy. By employing the output gap, monetary bodies are
presumed to perform in an organized way to overcome uncertainties in
output around the natural rate, while, over a similar time period, being
constrained by a trade-o between output and inflation. According to
Walsh (2002), the central bank is required to stabilize the peripheral
costs and assist in policy reforms. Output gap (yg!) is computed as:
() () ()
= ++ (1)
= ++ (2)
()= +ln()+ + ln()+ +++ (3)
() () () () () () ()
()= +()+ +ln ()
+ +++
() () ()
()= +()+ () + ()
+ +() + ++
()= +()+
+ +
+ln( )
+ln( )
+()
+ ()
+ ()
+ () + ++
() ()
= ()
=+ + +++ (7)
, where
() () ()
= ++ (1)
= ++ (2)
()= +ln()+ + ln()+ +++ (3)
() () () () () () ()
()= +()+ +ln ()
+ +++
() () ()
()= +()+ () + ()
+ +() + ++
()= +()+
+ +
+ln( )
+ln( )
+()
+ ()
+ ()
+ () + ++
() ()
= ()
=+ + +++ (7)
is the GDP in real terms and
() () ()
= ++ (1)
= ++ (2)
()= +ln()+ + ln()+ +++ (3)
() () () () () () ()
()= +()+ +ln ()
+ +++
() () ()
()= +()+ () + ()
+ +()+++
()= +()+
+ +
+ln( )
+ln( )
+()
+ ()
+ ()
+ () + ++
() ()
= ()
=+ + +++ (7)
is the potential output
of the economy at time t. Potential output is defined as the level of GDP
that is consistent with the full utilization of all factors of production and
computed from the Hodrick-Prescott (1997) trend.
340!Macroeconomic Stabilization in the Digital Age
Real interest rates are estimated as the variance between the
interbank call rate and inflation expectations. It can be measured as
() () ()
= ++ (1)
= ++ (2)
()= +ln()+ + ln()+ +++ (3)
() () () () () () ()
()= +()+ +ln ()
+ +++
() () ()
()= +()+ () + ()
+ +() + ++
()= +()+
+ +
+ln( )
+ln( )
+()
+ ()
+ ()
+ () + ++
() ()
= ()
=+ + +++ (7)
, where rt is the real interest rate, which is obtained by
deducting inflation from the nominal interest rate (Rt!). In light of the
primary assumptions, the model implied the equilibrium of the product
market, wherein the short-term interest rate clears the market. Hence,
a change in interest rate aects the output gap and inflation, thereby
impacting the cost of monetary policy.
In a traditional macroeconomic model, the transmission mechanism
of monetary policy in the form of the interest rate is included in the IS
framework. In the wake of the possibility of price stickiness, an increase
in nominal interest rate inflates the cost of capital which restricts
investment spending. The IS model is expressed by incorporating the
real interest rate and output gap:
() () ()
= ++ (1)
= ++ (2)
()= +ln()+ + ln()+ +++ (3)
() () () () () () ()
()= +()+ +ln ()
+ +++
() () ()
()= +()+ () + ()
+ +()+++
()= +()+
+ +
+ln( )
+ln( )
+()
+ ()
+ ()
+ () + ++
() ()
= ()
=+ + +++ (7)
(7)
where ygit is an output gap of country i at time t, and rit refers to the
real interest rate of country i at time t. The eect of fintech on the
transmission of monetary policy mechanism is measured by (a) mobile
and/or internet banking and cryptocurrencies; and (b) the interaction of
fintech with real interest rate. The model is expressed by incorporating
the eect of fintech on the monetary policy credit channel of the
transmission mechanism:
= + + +
+
(
)+
(
)
+ ()+ ()
+ ()+ ()
+ ( )+
( )+ ()
+
(
)+
(
)
+ ()+++
()= + +
+()+ + +
=
(10)
()
=
( + )
= 1, … . . ,
=
(8)
where
π
it is the price level, Mobile Techit refers mobile subscribers,
Internet Techit represents internet subscribers, and digital currencies
include Bitcoin, Ethereum, Litecoin, and Ripple. We interact fintech
instruments with interest rates to determine their impact on the
transmission mechanism of monetary policy. We expect a negative
Do Fintech Activities Aect Monetary Policy?!341
relationship between fintech instruments and output gap as financial
technology provides opportunities to make transactions through the
online system as well as digital currencies which positively influence
GDP, thereby reducing the output gap in an economy.
The other contribution of the study is to examine the parameters
that cause fintech activities. To test this proposition, we formulate the
following model:
= + + +
+ ( )+ ( )
+ ()+ ()
+ ()+ ()
+ ( )+
( )+ ()
+ ()+ ()
+ ()+++
()= + +
+()+ + +
=
(10)
()
=
( + )
= 1, … . .,
=
(9)
where fintech activities are measured through mobile technology,
internet technology, Bitcoin, Ethereum, Litecoin, and Ripple. The
possible factors that cause fintech instruments include GDP, real interest
rates, price level, and stock market indices. FDIit refers to financial
development index, which is a proxy of market openness of the financial
sector and may influence digital currencies in the world.
Barnett (1978, 1980) initially proposed the Divisia monetary
aggregate, which is estimated as the rate of change of the weighted sum
of the rates of change of the individual part of assets. The Divisia index
of money demand is expressed as
= + + +
+ ( )+ ( )
+ ()+ ()
+ ()+ ()
+ ( )+
( )+ ()
+ ()+ ()
+ ()+++
()= + +
+()+ + +
=
(10)
()
=
( + )
= 1, … . .,
=
(10)
where ln represents the natural algorithm of a variable, Dit denotes the
Divisia measure of country i at time t, and Mdit is the money demand
of country i at time period t. The composition of Divisia weights (nit)
is measured as the money demand share average over the change in
two-period
= + + +
+ ( )+ ( )
+ ()+ ()
+ ()+ ()
+ ( )+
( )+ ()
+ ()+ ()
+ ()+++
()= + +
+()+ + +
=
(10)
()
=
( + )
= 1, … . . ,
=
. For i = 1,…..,n where sit is the money
demand share of asset i during time t and measured as:
= + + +
+ ( )+ ( )
+ ()+ ()
+ ()+ ()
+ ( )+
( )+ ()
+ ()+ ()
+ ()+++
()= + +
+()+ + +
=
(10)
()
=
( + )
= 1, … . . ,
=
where pit indicates the user cost of asset i at time t.
12.4.2 Sample and Data
To examine the above linkages, this study considers 25 developed
jurisdictions including Australia, Austria, Belgium, Canada, the People’s
Republic of China, Denmark, Finland, France, Germany, Greece, Iceland,
Ireland, Italy, Japan, Luxembourg, the Netherlands, New Zealand,
Norway, Singapore, the Republic of Korea, Spain, Sweden, Switzerland,
the United Kingdom, and the US. For money demand and the output
gap, this study utilizes quarterly data from 2001Q1 to 2018Q4 by splitting
it into two subperiods, i.e., 2001–2009 (before fintech period) and
342!Macroeconomic Stabilization in the Digital Age
2010–2018 (after fintech period). The digital currencies are a new
concept and availability of data varies with financial instruments. For
Bitcoin, we utilize data from 2010Q1 to 2018Q4, for Litecoin from 2013Q3
to 2018Q4, for Ethereum from 2015Q2 to 2018Q4 and for Ripple from
2015Q1 to 2018Q4. The description of the variables and data sources
used in this study are described in Table 12.1.
12.5 Results
12.5.1 Summary Statistics
Table 12.2 presents the summary statistics of the variables used for
25jurisdictions during the period from 2001Q1 to 2018Q4. The mean
value of the natural logarithm of real GDP is 11.929, with a standard
Table 12.1: Description of Variables and Data Sources
Variable Description Data source
GDPiGross domestic product measured in real terms. DataStream
ygit It refers to the output gap, which is measured by
taking the dierence between real and potential
GDP. Potential GDP is estimated using Hodrick-
Prescott filters.
Authors’
calculations
riReal interest rate DataStream
πiInflation DataStream
M2A proxy of money supply in the country DataStream
Mobile
technology
Mobile telephone subscriptions that provide
access to cellular technology.
World Bank
Internet
technology
Fixed broadband subscriptions that provide
high-speed access to the internet.
World Bank
Bitcoin A proxy of digital currency; Bitcoin is
considered as the most popular form.
Yahoo.finance
Litecoin A proxy of digital currency Yahoo.finance
Ethereum A proxy of digital currency Yahoo.finance
Ripple A proxy of digital currency Yahoo.finance
Wealth A proxy of the stock market index of a country. DataStream
Financial
development
index
A proxy of a country’s financial openness whose
value ranges between 1 and 0. Index close to
1shows a higher value of openness.
IMF
Source: Authors.
Do Fintech Activities Aect Monetary Policy?!343
deviation of 2.010. The output gap is measured by taking the dierence
between real and potential output and its median value is –2.418 with
maximum and minimum values ranges between 11.949 and –12.315,
respectively. On average, the real interest rate of the sample is 1.33%,
with a standard deviation of 2.21%. The maximum and minimum values
comprise 9.50% and –0.50% respectively. The mean value of inflation is
1.83%, with a median value of 1.81%. On average, the mean value of the
logarithm of money supply (M2) is 12.552, with a standard deviation of
2.446. Wealth is a proxy of the stock market index of a respective country
and the mean value of the logarithm of stock market indices is 12.552. The
dierence between maximum value (10.732) and minimum value (3.882)
of ln(Wealth) shows the dispersion among sample countries. Mobile and
internet subscribers and digital currencies are used as proxies of fintech.
The mean value of mobile and internet technology subscribers is 16.809
and 15.467 respectively.
To observe the impact of digital currencies, this study uses the
recognition of cryptocurrencies by a particular country as a dummy
Table 12.2: Summary Statistics
Mean Median Maximum Minimum Std. Dev.
ln(GDP) 11.929 12.298 15.257 4.368 2.010
yg–0.319 –2.418 11.949 –12.315 2.279
Real interest rate 1.331 0.750 9.500 –0.500 2.212
π1.834 1.814 12.694 4.478 1.624
ln(M2)12.552 13.130 17.598 4.905 2.446
ln(Wealth) 7.777 7.752 10.732 3.882 1.328
ln(Mobile technology) 16.809 16.541 21.128 12.740 1.719
ln(Internet
technology)
15.467 15.177 19.792 11.601 1.663
ln(Bitcoin) 23.255 25.148 34.595 4.892 7.472
ln(Ethereum) 22.336 24.655 30.148 10.351 6.828
ln(Litecoin) 19.586 18.525 28.838 3.178 5.697
ln(Ripple) 11.300 11.259 23.654 2.370 8.906
Financial
Development Index
0.721 0.736 1.000 0.376 0.132
GDP = gross domestic product.
Notes: This table presents the summary statistics of the variables for 25 developed economies during the
period from 2001Q1 and 2018Q4. However, the data relating to digital currencies varies.
Source: Authors.
344!Macroeconomic Stabilization in the Digital Age
variable. The mean value is 0.857, which shows that 24 countries have
recognized the activities of digital currencies except for the People’s
Republic of China. Financial development index refers to the openness
of financial markets and its mean value is 0.721 with a standard deviation
of 0.132.
12.5.2 Panel Unit Root Test
We use the Levin, Lin, and Chu (2002) method to test the stationarity
of panel data. Table 12.3 exhibits the results of the panel unit root test
and finds mixed results. Some variables are stationary at level and
others at the first difference. To circumvent this problem, this study
employs GMM.
GMM is considered superior to the alternatives in handling many
econometric problems including endogeneity, heteroscedasticity, serial
correlation, and identification. This technique uses a weighting matrix
to account for serial correlation and heteroscedasticity of unknown
form and for nonlinearities (see Hansen 1982; Newey and West 1987;
Table 12.3: Panel Unit Root Test
Statistics Order of integration
ln(GDP) –17.102*** One
yg–4.557*** Zero
Real interest rate –4.203*** Zero
π–1.307* Zero
ln(M2) –8.039*** One
ln(Wealth) –9.815*** One
ln(Mobile technology) –4.315*** One
ln(Internet technology) –5.306*** Zero
ln(Bitcoin) –15.822*** Zero
ln(Ethereum) –11.116*** Zero
ln(Litecoin) –5.919*** One
ln(Ripple) –3.263*** Zero
Financial Development
Index
–12.292*** One
GDP = gross domestic product.
Note: This table shows the result of the Levin, Lin, and Chu (2002) panel unit root test.
Source: Authors.
Do Fintech Activities Aect Monetary Policy?!345
White 1984). This technique requires moment conditions. A set of
population moment conditions is specified on the regression errors.
These set the expected value of the errors and the expected values of the
products of errors with exogenous instrumental variables equal to zero.
These population moments are then replaced by the sample moments
to derive the parameter estimates. Identification in GMM requires that
there should be at least as many instruments (including the intercept)
and, hence, the moment conditions in each equation as the number
of parameters to be estimated. An equation may be under-identified,
exactly identified, or over-identified depending on whether the number
of instruments and, thus, moment conditions in that particular equation
are respectively less than, equal to, or greater than the number of
parameters to be estimated.
12.5.3 Stability of Income Velocity
and the Money Multiplier
To examine the stability of income velocity and the money multiplier,
first we estimate the Hodrick-Prescott trend. This study considers the
sample period between 2001Q1 and 2018Q4. The concept of digital
currencies was initiated in 2010; thus, it is imperative to analyze the
movements of income velocity and the money multiplier before and after
fintech periods using the feasible generalized least square estimator
(Table 12.4). We divide our sample into two subperiods: (i) 2001Q1 to
2009Q4 (pre-fintech), and (ii) 2010Q1 to 2018Q4 (post-fintech).
The velocity of money is the rate at which it is exchanged in an
economy. It gauges the robustness of an economy by measuring the rate
at which money in circulation is used for purchasing goods and services.
We measure the velocity of money through the nominal GDP scaled by
the average amount of money in circulation. During the period from
2001Q1 to 2018Q4 (Model I), the results show that an increase in trend
of income velocity by 1% increases the velocity of money by 1.003%.
Separating the two periods, we find the same trend in income velocity
of money (i.e., 1.002%) prior to the fintech period. After the initiation
of fintech activities, we report that income velocity of money surged to
1.026% in each quarter from 2010Q1 to 2018Q4. This shows that money
in circulation has not aected the money by the fintech activities.
The money multiplier is also referred to as monetary multiplier,
which measures central bank economic stimulus. If the government’s
goal is to stimulate the economy, they look to the multiplier to help
decide how much should be applied and in what way. For example, the
US government wants to increase the money supply and make it easier
for businesses to access capital and vice versa. The finding suggests that
346!Macroeconomic Stabilization in the Digital Age
an increase in trend by 1% enhances the money multiplier by 0.99%
during the entire sample period. Prior to the fintech period, we find the
same results as of the entire sample.
12.5.4 Monetary Policy and Money Demand
This section examines the factors that aect money demand using 25
jurisdictions, as well as segregating the sample into pre- and post-era
of fintech activities. The purpose of splitting the sample is to analyze
the factors that cause money demand before and after the initiation of
financial technology. To examine the determinants, we estimate three
equations using the GMM technique (Table 12.5). The results of Model
I suggest that, as output increases, the money demand in the country
increases. The coecient of real interest rate is negative and significantly
influences the money demand, which suggests that, as interest rates fall,
this activity generates business opportunities for which investors borrow
funds from financial institutions, thereby increasing money demand.
The coecient of inflation is inversely related to money demand but has
an insignificant eect. ln(Wealth) is used as a proxy of the stock market
indices. In line with the hypothesis on the substitution eect of near
money, it has a positive substitution eect on money demand, indicating
that, in a given investment portfolio, a rise in the stock prices shifts
investors’ preferences towards equity investment. This positive activity
in the stock market increases the money demand in the country.
Table 12.4: Income Velocity of Money and Money Multiplier
Income velocity Money multiplier
I II III I II III
Constant –0.004
(–0.64)
–0.001
(–0.17)
–0.030*
(–1.93)
0.079
(0.91)
0.202
(1.41)
–0.121
(–0.48)
Trend 1.003***
(179.21)
1.002***
(302.13)
1.026***
(72.00)
0.997***
(315.08)
0.993***
(151.57)
1.004***
(109.46)
Wald χ2576.02*** 598.12*** 465.76*** 701.31*** 521.83*** 387.24***
No. of
observations
1800 900 900 1800 900 900
Sample period 2001–18 2001–09 2010–18 2001–18 2001–09 2010–18
Notes: This table presents the results of growth of income velocity and money multiplier of 25 countries
across different time periods. ***, and * show significance at 1%, and 10% level respectively.
Source: Authors.
Do Fintech Activities Aect Monetary Policy?!347
Model II presents the empirical results prior to fintech activities and
reports that an increase in output of the country generates an increase
in money demand, which shows a good omen for the market. With
regard to monetary policy, the results report that the significance level
is deteriorated to 10%, though it negatively influences money demand.
The coecient of inflation is negative, which demonstrates that a higher
price level may inflate the cost of doing business, thereby restricting the
demand for money in the country. The stock market indices demonstrate
a positive relationship with money demand.
Model III estimates the results of post-fintech activities and
postulates that an increase in output and stock market indices results
in increased money demand. With the introduction of fintech, the
possibilities for investing in the stock market have increased. The
results illustrate that if the stock market index increases by 1%, the
money demand increases by 0.36% in each quarter. The real interest
rate is used as a proxy for monetary policy and we find an inverse
relationship between interest rates and money demand. This implies
that people choose to use cash alternatives to obtain better returns.
Table 12.5: Empirical Results: Monetary Policy and Money Demand
I II III
ln(GDP) 0.118***
(16.79)
0.317**
(2.35)
0.522***
(12.61)
Real interest rate –0.099***
(–4.36)
–0.123*
(1.72)
–0.135***
(–8.85)
Inflation –0.161
(–0.81)
–0.534**
(–2.45)
–0.518***
(–6.15)
ln(Wealth) 0.150***
(13.08)
0.318**
(2.48)
0.357***
(4.41)
Sargan (p-value)a0.41 0.11 0.18
AB 2 (p-value)b0.53 0.59 0.40
F-test 83.92*** 3.02** 45.48***
Observations 1800 900 900
Sample period 2001Q1–2018Q4 2001Q1–2009Q4 2010Q1–2018Q4
Notes: This table exhibits the results of the effect of monetary policy on the money demand using the
GMM technique. The dependent variable is the Divisia index of money demanded. We use three models to
estimate the results. ***, ** and * show significance at 1%, 5% and 10% level respectively.
a Sargan test of over-identification.
b Arellano-Bond test that second-order autocorrelation in residuals is zero.
Source: Authors.
348!Macroeconomic Stabilization in the Digital Age
Inflation also had a significantly negative impact on money demand
in our analysis. To summarize, we identify that economic output has
increased in the post-fintech period due to increased money demand,
which is also positively related to stock market activities. However,
real interest rates and inflation are negatively associated with money
demand.
12.5.5 Fintech, Monetary Policy and Money Demand
To investigate the impact of fintech on money demand, we estimate
three models and Table 12.6 reports the empirical findings. In Model I,
we estimate the determinants of money demand by considering mobile
and internet technologies during the period lasting from 2010Q1 to
2018Q4. The results indicate that the output of the economy positively
and significantly influences money demand. Inflation is another
significant factor that negatively aects money demand. The monetary
policy rate inversely aects the money demand, which shows that
firms borrow funds at a lower interest rate and expand their business
activities, thereby increasing the money demand in the country. Mobile
technology and its use could be an indicator of prosperity in a given
country, which would likely increase wealth and the money demand.
We find a direct association between internet technology and money
demand, which suggests that the internet provides an opportunity for
business transactions using online networks, which inflates the demand
for money in a country.
In Model II, we incorporate digital currencies to determine their
impact on money demand and identify that GDP, real interest rates,
and inflation are robust predictors of money demand. Among digital
currencies, Bitcoin is the only determinant that inversely and significantly
influences money demand. This evidence shows that people are using
Bitcoin as a mode of conducting their business transactions, which
restricts the possibilities of transacting in cash and eventually reduces
the money demand in the economy. This finding holds true, as Bitcoin
is being used as the major facilitator of business activities. However, we
did not find any evidence of other digital currencies aecting money
demand.
In Model III, all the fintech instruments are added to find their impact
on money demand. We report that mobile and internet technologies are
significantly influencing money demand, which illustrates that access
creates opportunities for businesses to transact with consumers. Bitcoin
is the sole predictor among digital currencies that enable business
activities using blockchain, thus reducing the money demand.
Do Fintech Activities Aect Monetary Policy?!349
Table 12.6: Empirical Results:
Fintech, Monetary Policy, and Money Demand
I II III
ln(GDP) 0.218***
(1.95)
0.481***
(10.18)
0.605***
(10.94)
Real interest rate –0.143*
(–1.84)
–0.272**
(–2.56)
–0.426***
(–3.46)
Inflation –0.255***
(–3.93)
–0.544***
(–5.08)
–0.568***
(–5.74)
ln(Wealth) 0.462***
(5.12)
0.159
(1.55)
0.200*
(1.68)
ln(Mobile Techt)0.261**
(2.02)
0.525***
(4.70)
ln(Internet Techt)0.425***
(2.03)
0.619***
(4.19)
ln(Bitcoint) –0.286***
(–2.96)
–0.227**
(–2.22)
ln(Ethereumt) –0.024
(–1.38)
–0.029
(–1.42)
ln(Litcoint) –0.005
(–1.02)
–0.008
(–1.23)
ln(Ripplet) –0.007
(–0.15)
–0.019
(–0.47)
Sargan (p-value)a0.31 0.39 0.50
AB 2 (p-value)b0.78 0.93 0.92
F-test 43.92*** 25.88*** 22.17***
Observations 900 600 600
Sample period 2001Q1-2018Q4 2001Q1-2009Q4 2010Q1-2018Q4
Notes: This table exhibits the results of the effect of monetary policy on the output gap using the GMM
technique. The dependent variable is the Divisia of money demanded. ***, ** and * show significance at 1%,
5% and 10% level respectively.
a Sargan test of over-identification.
b Arellano-Bond test that second-order autocorrelation in residuals is zero.
Source: Authors.
12.5.6 Monetary Policy and the Output Gap
To examine the eect of monetary policy on the output gap, we
estimate three models using the GMM technique. Table 12.7 presents
the finding of monetary policy on the output gap. Model I covers the
350!Macroeconomic Stabilization in the Digital Age
period lasting from 2001Q1 to 2018Q4. The output gap measures how far
the economy is from its full employment or potential level. It is a noisy
signal of economic activity as it is based on the potential output, which
is unobservable and depends on estimates of GDP. The result shows
that the lagged output gap positively and significantly aects the output
gap (ygt). We also report that the coecient associated with the real
interest rate negatively and significantly influences the output gap. This
shows that a decrease in real interest rate reduces the cost of borrowing,
thereby increasing the output of the economy.
We divide our sample into pre- and post-fintech emergence periods
to examine the eect of monetary policy on the output gap. Model II
shows that the real interest rate inversely influences the output gap
and Model III shows that the real interest rate has no eect on the
output gap. This illustrates that there is excess liquidity throughout the
financial system and traditional monetary policy tools may no longer be
useful tools to conduct monetary policy interventions.
12.5.7 Fintech, Monetary Policy, and the Output Gap
This section investigates the impact of fintech instruments on the
output gap and their interaction with interest rates through estimating
five equations using GMM (Table 12.8). In Model I, we consider mobile
Table 12.7: Empirical Results: Monetary Policy and the Output Gap
I II III
ygt-10.991***
(616.74)
1.010***
(404.86)
1.022***
(97.93)
Real interest rate –0.386***
(–7.74)
–0.531***
(–16.01)
–0.077
(–1.54)
Sargan (p-value)a0.43 0.33 0.16
AB 2 (p-value)b0.67 0.34 0.18
F-test 901.71*** 919.18*** 546.98***
Observations 1800 900 900
Sample period 2001Q1–2018Q4 2001Q1–2009Q4 2010Q1–2018Q4
Notes: This table exhibits the results of the effect of monetary policy on the output gap using the system
GMM technique. The dependent variable is the output gap (ygt). ygt-1 represents the lagged output gap. ***,
** and * show significance at 1%, 5%, and 10% level respectively.
a Sargan test of over-identification.
b Arellano-Bond test that second-order autocorrelation in residuals is zero.
Source: Authors.
Do Fintech Activities Aect Monetary Policy?!351
Table 12.8: Empirical Results:
Fintech, Monetary Policy, and the Output Gap
I II III IV V
ygt-11.011***
(560.96)
0.919***
(204.94)
1.011***
(468.73)
0.918***
(190.37)
0.914***
(106.01)
Real interest rate –0.452***
(–7.89)
–0.525***
(–4.19)
–0.128***
(–6.15)
–0.073
(–1.05)
–0.177***
(–2.98)
ln(Mobile Techt) –0.187***
(–8.23)
–0.374***
(–11.06)
–0.335***
(–3.59)
ln(Internet Techt) –0.304***
(–7.84)
–0.624***
(–10.82)
–0.005***
(–3.50)
ln(Bitcoint)0.304**
(2.58)
0.423**
(2.10)
0.608**
(2.50)
ln(Ethereumt)0.226***
(4.34)
0.403***
(3.85)
0.253*
(1.89)
ln(Litecoint)0.043
(0.67)
–0.114
(–0.96)
–0.182
(–1.26)
ln(Ripplet) –0.064
(–1.50)
–0.122
(–1.57)
–0.190**
(–2.02)
rt * ln(Mobile Techt) –0.048***
(–7.01)
–0.058**
(–2.59)
rt * ln(Internet Techt) –0.075***
(–6.48)
–0.097***
(–2.81)
rt * ln(Bitcoint) –0.042
(–0.60)
–0.045
(–0.54)
rt * ln(Ethereumt) –0.075*
(–1.92)
–0.172***
(–3.48)
rt * ln(Litcoint) –0.043*
(–1.93)
0.045
(0.85)
rt * ln(Ripplet) –0.000
(0.22)
–0.097***
(–2.81)
Sargan (p-value)a0.23 0.20 0.19 0.29 0.68
AB 2 (p-value)b0.15 0.18 0.14 0.28 0.30
F-test 616.45*** 523.98*** 673.92*** 489.56*** 632.89***
Observations 900 550 900 550 550
Notes: This table presents the empirical findings of fintech and monetary policy using the generalized
method of moments covering 25 developed economies during the period lasting from 2010Q1 to 2018Q4.
t-values are reported in parenthesis. ***, ** and * show significance at 1%, 5%, and 10% level respectively.
a Sargan test of over-identification.
b Arellano-Bond test that second-order autocorrelation in residuals is zero.
Source: Authors.
352!Macroeconomic Stabilization in the Digital Age
and internet technologies as proxies of fintech. The eect of internet
technology on the output gap is indicative of an increase in aggregate
demand, which increases the country’s production capacity. On the
other hand, mobile technology negatively influences the output gap,
which illustrates that the output increases as people are able to make
transactions using it. However, both of these variables have a significant
eect on economic output.
We include Bitcoin, Ethereum, Litecoin, and Ripple to inquire about
their impact on output as well as interaction with interest rates. Model
II suggests that the economy is aected positively by Bitcoin, which
increases the dierence between actual and potential output. The
coecient of Ethereum is positive and significantly aects the output
gap of the economy. However, other digital currencies are insignificantly
contributing to the output of the countries in this study.
The other objective of the study is to determine the eect of fintech
instruments along with the real interest rate. In Model III, we interact
both mobile and internet technologies with real interest rates and
find that they decrease the output gap, showing the importance of the
interest rates. There is a surge in the volume of mobile-based banking
transactions, including borrowing funds, which ultimately aects
monetary policy. We also report a negative eect associated with both
interest rates and internet technology on the output gap that illustrates
that this mode of fintech provides an opportunity for firms to obtain
bank funds and increase their production facilities, which lowers the
output gap of the economy.
In Model IV, we employ other fintech instruments to find their
impact on the real interest rates, showing that only Ethereum and
Litecoin aect the output gap. The final model interacts all fintech
instruments with interest rates, reporting that Bitcoin, Ethereum, and
Ripple are significantly aecting the output gap but their directions are
dierent. These digital currencies may influence the financial system
once they are used for business transactions; however, alternatively
they are aecting the output of the economy. In terms of the interaction
of interest rates, Ethereum and Ripple may influence the transmission
mechanism of monetary policy as well as the output gap. An attempt to
explain these results is as follows: Ripple helps businesses to convert
currencies when engaging in global trade; so, Ripple may reduce the
output gap by creating ecient ways for businesses to accept and
convert global payments and Bitcoin and Ethereum may be used more
as alternatives to money in countries that have questionable governance
regimes.
Do Fintech Activities Aect Monetary Policy?!353
12.5.8 Determinants of Fintech Activities
Considering the importance and development of financial technology
over the past few years, we determine the factors that cause innovation
and development of fintech. In this context, we use GDP, real interest
rate, inflation, financial development index, and wealth as the predictors
that may aect financial technology. Table 12.9 presents the results of
the empirical findings.
Model I shows that the coecient of GDP is positive and
significantly influences the mobile technology variable. This illustrates
that mobile technology is utilized for business transactions, which may
increase production activities in the country. However, the coecient
of other variables is insignificant. In Model II, we identify the factors
that influence internet technology and report that GDP and inflation
Table 12.9: Determinants of Fintech Activities
I
(Mobile
Tech)
II
(Internet
Tech)
III
(Bitcoin)
IV
(Ethereum)
V
(Litecoin)
VI
(Ripple)
lnGDP 0.701***
(4.50)
0.037***
(4.09)
0.599***
(5.59)
0.686***
(5.74)
0.180*
(1.98)
0.326***
(3.25)
Real interest rate 0.003
(0.80)
0.000
(1.01)
–0.454*
(–2.01)
–0.587*
(–1.78)
–0.511**
(–2.39)
–0.528**
(–2.33)
Inflation –0.004
(–1.01)
–0.000
(–0.49)
–0.496*
(–1.92)
0.991***
(3.59)
0.978**
(2.25)
0.145***
(3.75)
FDI 0.229
(0.73)
0.037*
(1.83)
0.274
(0.17)
0.367**
(2.80)
0.100
(0.53)
0.618
(0.34)
ln(Wealth) 0.011
(0.61)
0.387
(0.33)
0.432***
(7.31)
0.179
(1.59)
0.149***
(7.87)
0.117***
(6.72)
Sargan (p-value)a0.16 0.17 0.38 0.41 0.36 0.20
AB 2 (p-value)b0.78 0.23 0.61 0.30 0.28 0.32
F-test 4.51*** 5.23*** 50.59*** 68.17*** 99.33*** 125.67***
Observations 900 900 900 400 550 550
Notes: This table presents the factors that cause fintech instruments considering 25 jurisdictions during the
period from 2010Q1 to 2018Q4. The t-values are reported in parenthesis. ***, ** and * show significance at 1%,
5% and 10% level respectively.
a Sargan test of over-identification.
b Arellano-Bond test that second-order autocorrelation in residuals is zero.
Source: Authors.
354!Macroeconomic Stabilization in the Digital Age
are robust predictors. Presently, it is a general practice that businesses
are interconnected, thereby increasing GDP. We further identify that
higher-level reforms relating to regulations of financial development
facilitate businesses flourishing using internet technology.
We also classify four digital currencies to examine their
determinants (Model III to VI). In Model III, Bitcoin is employed as
an important mode of fintech and the results reflect that it increases
production activities. In the event of lower real interest rates and
inflation, the volume of trade in Bitcoins is higher. To emphasize the
substitution eect of money, firms make transactions, which increase
the use of Bitcoin. An increase in Bitcoin trading volume provides an
opportunity for the investors to get funds and invest in firms listed on
the stock market to get abnormal returns. The other digital currencies
like Ethereum, Litecoin, and Ripple indicate the same determinants as
Bitcoin except inflation. In summary: (i) stronger financial development
regulations promote digital currencies to trade in a rigorous manner;
(ii) fintech instruments generate competition and promote stock
market development; and (iii)the trading possibilities of fintech create
opportunities to increase the output of the country.
12.6. Conclusion
With the innovation associated with financial technology around the
world, the dynamics of the financial system have drastically changed.
Firms use dierent modes of fintech to facilitate business transactions,
which may influence income velocity, money demand, and output
gap for dierent countries. Fintech is a novel concept that may also
influence the transmission mechanism of monetary policy. To address
these concerns, this study empirically investigates the role of fintech in
the transmission mechanism of monetary policy.
First, we report that there is no eect in income velocity and the
money multiplier after the initiation of fintech activities. We also
evaluate the money demand function and identify that GDP, real interest
rates, inflation, and stock market indices are significant factors that
aect money demand. By incorporating the fintech instruments into the
money demand function, we identify that mobile technology, internet
technology, and Bitcoin are influencing the money demand, while the
other digital currencies have no eect.
We also examine the relationship between monetary policy and
output gap and identify that, in the post-fintech era, the former may
have an insignificant eect on the latter. In addition, we included fintech
instruments as well as their interaction with the real interest rates to
explore their relationship with the output gap. This study determines
Do Fintech Activities Aect Monetary Policy?!355
that Bitcoin, Ethereum, and Ripple are significant determinates of the
output gap, whereas the transmission mechanism of monetary policy
may influence mobile and internet technology, Ethereum, and Ripple.
Lastly, we examine the factors aecting fintech instruments and find
that GDP, the real interest rate, inflation, the financial development
index, and stock market indices are the robust factors that influence
fintech instruments.
This study is useful for policy makers and should aid them in
constructing a regulatory framework for digital currencies which may
be implemented in true spirit and with consideration of the income
velocity, money demand, and monetary policy. As these financial
innovations become more commonplace and the historical data
associated with them increase, researchers will be able to formulate a
clearer picture of the relationship between monetary policy and fintech
activities, specifically focusing on the transition mechanism.
356!Macroeconomic Stabilization in the Digital Age
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