nep-fmk New Economics Papers
on Financial Markets
Issue of 2021‒09‒27
eleven papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Stock Market Responses to COVID-19: Mean Reversion, Dependence and Persistence Behaviours By Coskun, Yener; Akinsomi, Omokolade; Gil-Alana, Luis A.; Yaya, OlaOIuwa S.
  2. The Covid-19 Pandemic, Policy Responses and Stock Markets in the G20 By Guglielmo Maria Caporale; Woo-Young Kang; Fabio Spagnolo; Nicola Spagnolo
  3. The effect of Eurosystem asset purchase programmes on euro area sovereign bond yields during the COVID-19 pandemic By George Hondroyiannis; Dimitrios Papaoikonomou
  4. Financial Turbulence, Systemic Risk and the Predictability of Stock Market Volatility By Afees A. Salisu; Riza Demirer; Rangan Gupta
  5. Evaluation of Dynamic Cointegration-Based Pairs Trading Strategy in the Cryptocurrency Market By Masood Tadi; Irina Kortchmeski
  6. Dynamic effects of network exposure on equity markets By Kangogo, Moses; Volkov, Vladimir
  7. The Role of Social Connectedness: Evidence from Mergers and Acquisitions By Giang Nguyen; Hannah Nguyen; Hung Pham
  8. When Two Worlds Collide: Using Particle Physics Tools to Visualize the Limit Order Book By Marjolein E. Verhulst; Philippe Debie; Stephan Hageboeck; Joost M. E. Pennings; Cornelis Gardebroek; Axel Naumann; Paul van Leeuwen; Andres A. Trujillo-Barrera; Lorenzo Moneta
  9. Bank balance sheet constraints and bond liquidity By Breckenfelder, Johannes; Ivashina, Victoria
  10. Preferred habitat investors in the UK government bond market By Giese, Julia; Joyce, Michael; Meaning, Jack; Worlidge, Jack
  11. Market Efficiency of Asian Stocks: Evidence based on Narayan-Liu-Westerlund GARCH-based Unit root test By Yaya, OlaOluwa S.; Vo, Xuan Vinh; Adekoya, Oluwasegun B.

  1. By: Coskun, Yener; Akinsomi, Omokolade; Gil-Alana, Luis A.; Yaya, OlaOIuwa S.
    Abstract: We examine stock market responses during the COVID-19 pandemic period using fractional integration techniques by considering the data spanning from August 2nd 2019 to July 9th 2020. The evidence suggests that stock markets generally follow a synchronized movement before and during the stages of the pandemic’s shocks. We find that, while mean reversion significantly declines, the degree of persistence and dependence has been increased in the majority of the stock market indices- in the full sample analysis. This outcome implies increasing integration and possibly declining benefits of diversification for the global stock portfolio management.
    Keywords: Coronavirus; stock markets; fractional integration; long memory; mean reversion
    JEL: C12 C22 F31
    Date: 2021–09–09
  2. By: Guglielmo Maria Caporale; Woo-Young Kang; Fabio Spagnolo; Nicola Spagnolo
    Abstract: This paper analyses the impact of the Covid-19 pandemic on stock market returns and their volatility in the case of the G20 countries. In contrast to the existing empirical literature, which typically focuses only on either Covid-19 deaths or lockdown policies, our analysis is based on a comprehensive dynamic panel model accounting for the effects of both the epidemiological situation and restrictive measures as well as of fiscal and monetary responses; moreover, instead of Covid-19 deaths it uses a far more sophisticated Covid-19 index based on a Balanced Worth (BW) methodology, and it also takes into account heterogeneity by providing additional estimates for the G7 and the remaining countries (non-G7) separately. We find that the stock markets of the G7 are affected negatively by government restrictions more than the Covid-19 pandemic itself. By contrast, in the non-G7 countries both variables have a negative impact. Further, lockdowns during periods with particularly severe Covid-19 conditions decrease returns in the non-G7 countries whilst increase volatility in the G7 ones. Fiscal and monetary policy (the latter measured by the shadow short rate) have positive and negative effects, respectively, on the stock markets of the G7 countries but not of non-G7 ones. In brief, our evidence suggests that restrictions and other policy measures play a more important role in the G7 countries whilst the Covid-19 pandemic itself is a key determinant in the case the non-G7 stock markets.
    Keywords: Covid-19 pandemic, stringency index, Covid-19 index, fiscal policy, shadow rates, stock markets
    JEL: C33 G15 E52 E62
    Date: 2021
  3. By: George Hondroyiannis (Bank of Greece and Harokopio University); Dimitrios Papaoikonomou (Bank of Greece)
    Abstract: We investigate the effect of Eurosystem Asset Purchase Programmes (APP) on the monthly yields of 10-year sovereign bonds for 11 euro area sovereigns during January-December 2020. The analysis is based on time-varying coefficient methods applied to monthly panel data covering the period 2004m09 to 2020m12. During 2020 APP contributed to an average decline in yields estimated in the range of 58-76 bps. In December 2020 the effect per EUR trillion ranged between 34 bps in Germany and 159 bps in Greece. Stronger effects generally display diminishing returns. Our findings suggest that a sharp decline in the size of the APP in the aftermath of the COVID-19 crisis could lead to very sharp increases in bond yields, particularly in peripheral countries. The analysis additionally reveals a differential response to global risks between core and peripheral countries, with the former enjoying safe-haven benefits. Markets’ perceptions of risk are found to be significantly affected by credit ratings, which is in line with recent evidence based on constant parameter methods.
    Keywords: Euro area;asset purchase programmes; sovereign bond yields; time-varying parameters.
    JEL: C33 E44 E52 E58 F34 G15
    Date: 2021–07
  4. By: Afees A. Salisu (Centre for Econometric & Allied Research, University of Ibadan, Ibadan, Nigeria; Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Riza Demirer (Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026-1102, USA); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)
    Abstract: This paper adds a novel perspective to the literature by exploring the predictive performance of two relatively unexplored indicators of financial conditions, i.e. financial turbulence and systemic risk, over stock market volatility in a sample of seven emerging and advanced economies. The two financial indicators that we utilize in our predictive setting provide a unique perspective to market conditions as they directly relate to portfolio performance metrics from both a volatility and co-movement perspective and, unlike other macro-financial indicators of uncertainty or risk, can be integrated into diversification models within a forecasting and portfolio design setting. Since the two predictors are available at weekly frequency, and we want to provide forecast at the daily level, we use the generalized autoregressive conditional heteroskedasticity-mixed data sampling (GARCH-MIDAS) approach. The results suggest that incorporating the two financial indicators (singly and jointly) indeed improves the out-of-sample predictive performance of stock market volatility models across both the short and long horizons. We observe that the financial turbulence indicator that captures asset price deviations from historical patterns does a better job when it comes to the out-of-sample prediction of future returns compared to the measure of systemic risk, captured by the absorption ratio. The outperformance of the financial turbulence indicator implies that unusual deviations in not only asset returns, but also correlation patterns clearly play a role in the persistence of return volatility. Overall, the findings provide an interesting opening for portfolio design purposes in that financial indicators that are directly associated with portfolio diversification performance metrics can also be utilized for forecasting purposes with significant implications for dynamic portfolio allocation strategies.
    Keywords: Systemic risk, Financial turbulence, Stock market, MIDAS models
    JEL: C32 D8 E32 G15
    Date: 2021–09
  5. By: Masood Tadi; Irina Kortchmeski
    Abstract: This research aims to demonstrate a dynamic cointegration-based pairs trading strategy, including an optimal look-back window framework in the cryptocurrency market, and evaluate its return and risk by applying three different scenarios. We employ the Engle-Granger methodology, the Kapetanios-Snell-Shin (KSS) test, and the Johansen test as cointegration tests in different scenarios. We calibrate the mean-reversion speed of the Ornstein-Uhlenbeck process to obtain the half-life used for the asset selection phase and look-back window estimation. By considering the main limitations in the market microstructure, our strategy exceeds the naive buy-and-hold approach in the Bitmex exchange. Another significant finding is that we implement a numerous collection of cryptocurrency coins to formulate the model's spread, which improves the risk-adjusted profitability of the pairs trading strategy. Besides, the strategy's maximum drawdown level is reasonably low, which makes it useful to be deployed. The results also indicate that a class of coins has better potential arbitrage opportunities than others. This research has some noticeable advantages, making it stand out from similar studies in the cryptocurrency market. First is the accuracy of data in which minute-binned data create the signals in the formation period. Besides, to backtest the strategy during the trading period, we simulate the trading signals using best bid/ask quotes and market trades. We exclusively take the order execution into account when the asset size is already available at its quoted price (with one or more period gaps after signal generation). This action makes the backtesting much more realistic.
    Date: 2021–09
  6. By: Kangogo, Moses (Tasmanian School of Business & Economics, University of Tasmania); Volkov, Vladimir (Tasmanian School of Business & Economics, University of Tasmania)
    Abstract: Until recently, there has been a growing research focusing on how to predict systemic risks to minimise the recurrence of financial crises, while the importance of understanding how network exposure contributes to the spread of financial distress in the financial system has been largely underestimated. This paper investigates whether network exposure contributes to both shock transmission and absorption. We utilise data from 45 economies and our findings show that both network intensity and interconnectedness in the financial system have impact on increasing network exposure. We also demonstrate how to estimate network intensity in the financial system. Our results indicate that an increased network intensity parameter is associated to period when the financial system is under stress.
    Keywords: Financial markets, financial networks, financial stability
    JEL: G15 G10 G01 C21
    Date: 2021
  7. By: Giang Nguyen (Faculty of Political Science and Economics, Waseda University 1-6-1 Nishi-Waseda, Shinjuku, Tokyo 169-8050, Japan); Hannah Nguyen (Department of Banking and Finance, Monash University Caulfield East, Victoria 3145, Australia); Hung Pham (Department of Banking and Finance, Monash University Caulfield East, Victoria 3145, Australia)
    Abstract: Using a comprehensive dataset of social network ties between U.S. counties, we document higher announcement returns for acquirers that are more socially proximate to their targets. Our findings are robust to the inclusion of geographical proximity and withstand endogeneity concerns. Consistent with the information asymmetry hypothesis, we show that the effect of social connectedness is more pronounced when targets have high information opacity, as proxied by target status, analyst coverage, bid–ask spreads, R&D, and high-tech classifications. In addition, social connectedness lowers advisory fees, reduces deal premiums, and yields better acquirer long-term performance.
    Keywords: Social connectedness; merger and acquisition; information asymmetry
    Date: 2021–09
  8. By: Marjolein E. Verhulst; Philippe Debie; Stephan Hageboeck; Joost M. E. Pennings; Cornelis Gardebroek; Axel Naumann; Paul van Leeuwen; Andres A. Trujillo-Barrera; Lorenzo Moneta
    Abstract: We introduce a methodology to visualize the limit order book (LOB) using a particle physics lens. Open-source data-analysis tool ROOT, developed by CERN, is used to reconstruct and visualize futures markets. Message-based data is used, rather than snapshots, as it offers numerous visualization advantages. The visualization method can include multiple variables and markets simultaneously and is not necessarily time dependent. Stakeholders can use it to visualize high-velocity data to gain a better understanding of markets or effectively monitor markets. In addition, the method is easily adjustable to user specifications to examine various LOB research topics, thereby complementing existing methods.
    Date: 2021–09
  9. By: Breckenfelder, Johannes; Ivashina, Victoria
    Abstract: We explore the ties between bonds and individual dealers formed through home advantage and the persistence of previous underwriting relationships. Building on these connections, we show that the introduction of the leverage ratio for the European banks had a large impact on exposed bonds’ liquidity. Moreover, based on these ties, we show that bond mutual fund panic following the 2020 pandemic outbreak affected substantially more mutual funds with the larger exposures to dealer banks’ balance sheet constraints. JEL Classification: G12, G18, G21
    Keywords: Bond liquidity, capital requirements, COVID-19, leverage ratio, market-making, mutual funds
    Date: 2021–09
  10. By: Giese, Julia (Bank of England); Joyce, Michael (Bank of England); Meaning, Jack (Bank of England); Worlidge, Jack (Bank of England)
    Abstract: Most tests of preferred habitat theory are indirect; they infer the existence of preferred habitat behaviour in financial markets by examining the behaviour of asset prices. We instead identify preferred habitat behaviour directly from whether investors show a preference towards a particular duration habitat. We do so by making use of a newly available and highly granular data set on the UK government bond (gilt) market, which allows us to examine investors’ gilt transactions and their daily stock of gilt holdings during 2016 and 2017. Using cluster analysis, we find that investors can be classified into distinct groups, some of which more closely display the behavioural properties that theory associates with preferred habitat investors. We find that these groups of investors are less sensitive to price movements than other investor groups and include institutional investors, like life insurers and pension funds, which are typically associated with preferred habitat behaviour. Evidence from the Bank of England’s QE4 purchase programme during August 2016 to March 2017 suggests that these investor groups sold relatively more of their gilt holdings to the Bank than other groups of investors.
    Keywords: Preferred habitat; gilt market; yield curve; cluster analysis
    JEL: E43 E52 G11 G12
    Date: 2021–09–10
  11. By: Yaya, OlaOluwa S.; Vo, Xuan Vinh; Adekoya, Oluwasegun B.
    Abstract: This study uses the recently developed Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model-based unit root test of Narayan et al. (2016) to examine the stock market efficiency of 19 Asian countries, using daily prices. The model flexibly accounts for heteroskedasticity and two structural breaks, the presence of which can lead to inaccurate results if neglected. Our results disclose the stock markets of 14 countries as inefficient following the rejection of the unit root null hypothesis. However, the stock markets of China, Hong Kong, Japan and the Korea Republic are adjudged efficient. We further extend the model to accommodate a maximum of five breaks to check the robustness of our results to higher breaks. We observe that the results are largely consistent except for Lebanon and Singapore. For completeness, we compare the results with those of conventional GARCH models that do not account for structural breaks and discover differing results for some countries. Hence, the role of structural breaks is not negligible in assessing market efficiency. Future studies should also incorporate heteroskedasticity and structural breaks in their modelling framework to obtain accurate results.
    Keywords: Stock market efficiency; GARCH; Unit root; Structural breaks; Asia
    JEL: C22 G01 G15
    Date: 2021–09–14

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