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Gelişmekte Olan Avrupa Ekonomileri Para Birimlerinin Ağ Bağlanmışlığı
Abstract
Bu çalışma, Avro ve gelişmekte olan yedi Doğu, Orta ve Güneydoğu Avrupa ülkesi para birimleri arasındaki oynaklık bağlanmışlığını Diebold-Yılmaz bağlanmışlık endeksi çerçevesinde incelemektedir. 2006-2024 arasında günlük veri kullanılarak yapılan analiz sonucunda avronun diğer yedi para birimine doğru güçlü bir bağlanmışlık kaynağı olduğu bulunmuştur. Gelişen ekonomileri ile paralel olarak Polonya zlotisi ve Çek korunası avroyu takip eden diğer önemli bağlanmışlık kaynaklarındandır. Türkiye ve Rusya, Avrupa Birliği üyesi olmasalar da, büyük yerel şoklardan etkilendikleri dönemlerde Türk lirası ve Rus rublesi kanalıyla bağlanmışlık kaynakları olarak davranır. Macar forinti, Romen leyi ve Bulgar levası görece düşük bağlanmışlık etkilerine sahiptir. İncelenen para birimlerinin şok yayma kapasiteleri de benzer sıralamayı izler. Çek korunası, görece güçlü bir şok yayıcısı olmasının yanı sıra şok yayma değerleri en düşük standart sapma değerine sahip para birimidir.
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We analyze the connectedness between the real and the financial sectors of the U.S. economy. Using the weekly ADS index of the Philadelphia Fed (the widely used business conditions indicator) to represent the real side, we find that during times of financial distress and business cycle turning points, the direction of connectedness runs from the real sector to financial markets. The ADS index is derived from a model containing a measure of term structure along with real variables. Therefore, it might not be the best representative of the real activity used in the connectedness analysis. As an alternative, we derive a real activity index (RAI) from a dynamic factor model of the real sector variables only. The behavior of RAI over time is quite similar to that of the ADS index. When we include RAI to represent the real side, connectedness from the real side to financial markets weakens substantially, while the connectedness from financial markets to the real side becomes more pronounced.
Abstract We study macro-financial linkages and their importance within the Swiss economy from a network perspective. First, we investigate the real-financial connectedness in the Swiss economy, using the KOF economic barometer, obtained from real and financial variables, and, the real activity index (RAI), we distilled from a small set of real variables, as two alternative proxies for the real side. Whereas the KOF-barometer-based analysis shows that both sides transmit sizeable shocks to each other without one dominating the other, the RAI-based analysis shows that in the aggregate, the financial side turns out to be the net shock transmitter to the real sector. In the second part, we focus on the relative importance of financial markets as shock propagators using a network centrality measure. We find that 2008–2009 recession in Switzerland and the Swiss National Bank’s (SNB) exchange rate policy changes in 2011 and 2015 have significantly altered the way the shocks are transmitted across the two sides of the economy. During 2009–2011, stock, bond, and foreign exchange (FX) markets, in descending order, played important roles as shock propagators. Following the SNB’s 2015 policy decision to discontinue the lower bound for the EUR/CHF exchange rate, FX market has become equally important as the stock market but more important than the bond market as a shock propagator.
The degree of connectedness of equity markets on a given day can be assessed by decomposing the forecast error variance resulting from a vector autoregressive model, applied to daily returns on stock indices. This well-known procedure leads, for each day, to a spillover matrix which can be readily interpreted as a network structure and summarized into the so-called spillover index, to which much recent research work has been devoted. Taking a sequence of spillover matrices as starting point, we show how the scope of this concept can be broadened in several ways. Firstly, we develop a concept to quantify a market's potential to spread information, which is related to the eigenvector structure of spillover matrices. Secondly, a Markov chain approach allows the definition of relative market entropy, quantifying the amount of information gained from day to day. A further entropy concept can be related to the speed of shock digestion and network stability. As an empirical example, we analyze a system of five markets represented by stock indices Dow Jones Industrial Average (USA), FTSE (UK), Euro Stoxx 50 (euro area), Nikkei 225 (Japan), and SSE Composite (China). It is demonstrated that increasing trends in the spillover index as well as in speed of information digestion are an empirical fact but no logical necessity theoretical examples show that there can be opposite trends in these series.
The goal of the this paper is to investigate the shock spillover characteristics of the Russian stock market during different rounds of sanctions imposed as a reaction to Russia’s alleged role in the Ukrainian crisis. We consider six stock markets, represented by their respective stock indices, namely the US (DJIA), the UK (FTSE), the euro area (Euro Stoxx 50), Japan (Nikkei 225), China (SSE Composite) and Russia (RTS). Linking these markets together in a network on the basis of vector autoregressive processes, we can measure, among other things: (i) direct daily return and volatility spillovers from RTS to other market indices, (ii) daily propagation values quantifying the relative importance of the Russian stock market as a return or volatility shock propagator, and (iii) the amount of network repercussions after a shock. The last two are methodological innovations in this context. It turns out that distinct spillover patterns exist in different rounds of sanctions. Largescale sanctions, beginning in July 2014, rendered the consequences of shocks from Russia less predictable. While these sanctions reduced the importance of the Russian stock market as a propagator of return shocks, they also increased its importance as a propagator of volatility shocks, thus making the network more vulnerable with respect to volatility shocks from the Russian stock market. This is a form of backlash that the sanctioning economies have been facing.
We propose several connectedness measures built from pieces of variance decom- positions, and we argue that they provide natural and insightful measures of connectedness among nancial asset returns and volatilities. We also show that variance decompositions de- ne weighted, directed networks, so that our connectedness measures are intimately-related to key measures of connectedness used in the network literature. Building on these in- sights, we track both average and daily time-varying connectedness of major U.S. nancial institutions' stock return volatilities in recent years, including during the nancial crisis of 2007-2008.
We present a set of stylized empirical facts emerging from the statistical analysis of price variations in various types of financial markets. We first discuss some general issues common to all statistical studies of financial time series. Various statistical properties of asset returns are then described: distributional properties, tail properties and extreme fluctuations, pathwise regularity, linear and nonlinear dependence of returns in time and across stocks. Our description emphasizes properties common to a wide variety of markets and instruments. We then show how these statistical properties invalidate many of the common statistical approaches used to study financial data sets and examine some of the statistical problems encountered in each case.
Using a generalized vector autoregressive framework in which forecast-error variance decompositions are invariant to variable ordering, we propose measures of both total and directional volatility spillovers. We use our methods to characterize daily volatility spillovers across U.S. stock, bond, foreign exchange and commodities markets, from January 1999 through January 2010. We show that despite significant volatility fluctuations in all four markets during the sample, cross-market volatility spillovers were quite limited until the global financial crisis that began in 2007. As the crisis intensified so too did the volatility spillovers, with particularly important spillovers from the stock market to other markets taking place after the collapse of Lehman Brothers in September 2008.
This article examines the extent of contagion and interdependence across the East Asian equity markets since early 1990s and compares the ongoing crisis with earlier episodes. Using the forecast error variance decomposition from a vector autoregression, we derive return and volatility spillover indices over the rolling sub-sample windows. We show that there is substantial difference between the behavior of the East Asian return and volatility spillover indices over time. While the return spillover index reveals increased integration among the East Asian equity markets, the volatility spillover index experiences significant bursts during major market crises, including the East Asian crisis. The fact that both return and volatility spillover indices reached their respective peaks during the current global financial crisis attests to the severity of the current episode.
We analyze time-varying exchange rate co-movements, hedging ratios, and volatility spillovers on the new EU forex markets during 1999M1-2018M5. We document significant differences in the extent of currency comovements during various periods of market distress that are related to real economic and financial events. These imply favorable diversification benefits: the hedge-ratio calculations show all three currencies bring hedging benefits during crisis periods, but at different costs. During calm periods, most of the volatilities are due to each currency's own history. During the distress periods, volatility spillovers among currencies increase substantially and the Hungarian currency assumes a leading role.
Cycles in the behavior of stock markets have been widely documented. There is an increasing body of literature on whether stock markets anticipate business cycles or its turning points. Several recent studies assert that financial integration impacts positively on business cycle comovements of economies.
We consider three western equity markets, represented by their respective stock indices: DJIA (USA), FTSE 100 (UK), and Euro Stoxx 50 (euro area). Connecting these three markets together via vector autoregressive processes in index returns, we construct “propagation values” to measure and trace, on a daily basis, the relative importance of a market as a volatility creator within the network, where volatility is due to a return shock in a market.
A cross-wavelet analysis reveals the joint frequency structure of pairs of the propagation value series, in particular whether or not two series tend to move in the same direction at a given frequency. Our main findings are: (i) From 2001 onwards, the daily propagation values of markets have been fluctuating much less than before, and high frequencies have become less pronounced; (ii) the European markets are in phase at business cycle frequency, while the US market is not in phase with either European market; (iii) in 2008, the euro area has taken over the leading role. This approach not only provides new insight into the time-dependent interplay of equity markets, but it can also replicate certain findings of traditional business cycle research, and it has the advantage of using only readily available stock market data.
Offers a new approach to the study of financial and macroeconomic connectedness using simple time series techniques to obtain static and dynamic measures of financial and macroeconomic connectedness
Proposes a new framework that can be used to uncover and analyze underlying network formation by using daily data on asset returns and their volatilities to study how shocks spread among a group of assets and markets in time
Examines the measurement of connectedness among financial markets and/or financial institutions on a global scale, which has been a concern of policy-makers since the global financial crisis in 2008
2In an influential paper, Freeman (1979) identified three aspects of centrality: betweenness, nearness, and degree. Perhaps because they are designed to apply to networks in which relations are binary valued (they exist or they do not), these types of centrality have not been used in interlocking directorate research, which has almost exclusively used formula (2) below to compute centrality. Conceptually, this measure, of which c(ot, 3) is a generalization, is closest to being a nearness measure when 3 is positive. In any case, there is no discrepancy between the measures for the four networks whose analysis forms the heart of this paper. The rank orderings by the
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