nep-fmk New Economics Papers
on Financial Markets
Issue of 2020‒12‒21
eleven papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Forecasting Realized Stock-Market Volatility: Do Industry Returns have Predictive Value? By Riza Demirer; Rangan Gupta; Christian Pierdzioch
  2. Volatility Expectations and Returns By Lars A. Lochstoer; Tyler Muir
  3. The Collateral Link between Volatility and Risk Sharing By Sebastian Infante; Guillermo Ordoñez
  4. Signaling through Timing of Stock Splits By Maria Chiara Iannino; Sergey Zhuky
  5. Modeling Turning Points In Global Equity Market By Daniel Felix Ahelegbey; Monica Billio; Roberto Casarin
  6. Exchange Rates, Stock Prices, and Stock Market Uncertainty By Fatemeh Salimi
  7. Explainable AI for Interpretable Credit Scoring By Lara Marie Demajo; Vince Vella; Alexiei Dingli
  8. Treasury Market Functioning During the COVID-19 Outbreak: Evidence from Collateral Re-use By Sebastian Infante; Zack Saravay
  9. Information network modeling for U.S. banking systemic risk By Nicola, Giancarlo; Cerchiello, Paola; Aste, Tomaso
  10. Liquidity Risk at Large U.S. Banks By Laurence M. Ball
  11. Private Equity Returns: Empirical Evidence from the Business Credit Card Securitization Market By Matthias Fleckenstein; Francis A. Longstaff

  1. By: Riza Demirer (Department of Economics & Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026-1102); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany)
    Abstract: Yes, they do. Utilizing a machine-learning technique known as random forests to compute forecasts of realized (good and bad) stock market volatility, we show that incorporating the information in lagged industry returns can help improve out-of sample forecasts of aggregate stock market volatility. While the predictive contribution of industry level returns is not constant over time, industrials and materials play a dominant predictive role during the aftermath of the 2008 global financial crisis, highlighting the informational value of real economic activity on stock market volatility dynamics. Finally, we show that incorporating lagged industry returns in aggregate level volatility forecasts benefits forecasters who are particularly concerned about under-predicting market volatility, yielding greater economic benefits for forecasters as the degree of risk aversion increases.
    Keywords: Stock market; Realized volatility; Industry returns, Market efficiency and information
    JEL: G17 Q02 Q47
    Date: 2020–12
  2. By: Lars A. Lochstoer; Tyler Muir
    Abstract: We provide evidence that agents have slow-moving beliefs about stock market volatility that lead to initial underreaction to volatility shocks followed by delayed overreaction. These dynamics are mirrored in the VIX and variance risk premiums which reflect investor expectations about volatility and are also supported in surveys and in firm-level option prices. We embed these expectations into an asset pricing model and find that the model can account for a number of stylized facts about market returns and return volatility which are difficult to reconcile, including a weak, or even negative, risk-return tradeoff.
    JEL: G0 G12 G4
    Date: 2020–11
  3. By: Sebastian Infante; Guillermo Ordoñez
    Abstract: We show that aggregate volatility affects the extent to which agents can share idiosyncratic risks through the valuation of collateral. Both private and public assets are used in insurance markets as collateral, but their exposure to volatility differs. While aggregate volatility decreases the value of private assets—they are exposed to more variation—it increases the value of public assets—they become more valuable to smooth consumption intertemporally. Hence, a more volatile economy tends to damage risk sharing when the composition of collateral is biased toward private assets. As we show that a stable economy is more propitious to the creation of private collateral, stability makes risk sharing increasingly fragile to volatility shocks. We find empirical evidence that the higher use of private assets in the U.S. has affected the sensitivity of risk sharing to aggregate volatility as predicted by our model.
    JEL: E44 G12 G18
    Date: 2020–11
  4. By: Maria Chiara Iannino (University of St Andrews); Sergey Zhuky (Russian Academy of National Economy and Public Administration)
    Abstract: We develop a dynamic structural model of stock splits, in which managers can signal their private information through the timing of the split decisions. Our approach is consistent with the empirical evidence that shows that the majority of stock splits have 2:1 ratio but are announced at various pre-split price levels. The model allows us to estimate the nominal share price preferences of investors and to decompose the split announcement return into the value of new information and the signalling cost. This signalling cost could reach 0.5% of a company’s value for the lowest pre-split prices in our sample.
    Date: 2020–12–07
  5. By: Daniel Felix Ahelegbey (University of Pavia); Monica Billio (University of Venice); Roberto Casarin (University of Venice)
    Abstract: Turning points in financial markets are often characterized by changes in the direction and/or magnitude of market movements with short-to-long term impacts on investors’ decisions. This paper develops a Bayesian technique to turning point detection in financial equity markets. We derive the interconnectedness among stock market returns from a piece-wise network vector autoregressive model. The empirical application examines turning points in global equity market over the past two decades. We also compare the Covid-19 induced interconnectedness with that of the global financial crisis in 2008 to identify similarities and the most central market for spillover propagation
    Keywords: Bayesian inference, Dynamic Programming, Turning points, Networks, VAR.
    JEL: C11 C15 C51 C52 C55 C58 G01
    Date: 2020–11
  6. By: Fatemeh Salimi (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique - AMU - Aix Marseille Université)
    Abstract: While the reference framework for international portfolio choice emphasizes a mean-variance framework, uncovered parity conditions only involve mean stock or bond returns. We propose to augment the empirical specification by using the relative stock market uncertainty of two countries as an extra determinant of their bilateral exchange rate returns. A rise in the relative uncertainty of one stock market will lead capital to flow to the other stock market and generate an appreciation in the currency of the latter. By focusing on the JPY/USD exchange rate returns during the most recent decade (2009-2019) and relying on a nonlinear framework, we provide evidence that the Japanese-US differential stock market uncertainty affects the JPY/USD returns both contemporaneously and with weekly lags. This finding is robust when we control for the stock returns differential and the differential changes in Japanese and US unconventional monetary policy measures.
    Keywords: exchange rate determination,implied volatility,UEP,flight to safety,flight to quality
    Date: 2020–11
  7. By: Lara Marie Demajo; Vince Vella; Alexiei Dingli
    Abstract: With the ever-growing achievements in Artificial Intelligence (AI) and the recent boosted enthusiasm in Financial Technology (FinTech), applications such as credit scoring have gained substantial academic interest. Credit scoring helps financial experts make better decisions regarding whether or not to accept a loan application, such that loans with a high probability of default are not accepted. Apart from the noisy and highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the `right to explanation' introduced by the General Data Protection Regulation (GDPR) and the Equal Credit Opportunity Act (ECOA) have added the need for model interpretability to ensure that algorithmic decisions are understandable and coherent. An interesting concept that has been recently introduced is eXplainable AI (XAI), which focuses on making black-box models more interpretable. In this work, we present a credit scoring model that is both accurate and interpretable. For classification, state-of-the-art performance on the Home Equity Line of Credit (HELOC) and Lending Club (LC) Datasets is achieved using the Extreme Gradient Boosting (XGBoost) model. The model is then further enhanced with a 360-degree explanation framework, which provides different explanations (i.e. global, local feature-based and local instance-based) that are required by different people in different situations. Evaluation through the use of functionallygrounded, application-grounded and human-grounded analysis show that the explanations provided are simple, consistent as well as satisfy the six predetermined hypotheses testing for correctness, effectiveness, easy understanding, detail sufficiency and trustworthiness.
    Date: 2020–12
  8. By: Sebastian Infante; Zack Saravay
    Abstract: In March 2020, uncertainty over the COVID-19 pandemic caused severe stress in U.S. financial markets. Specifically, Fleming and Ruela (2020) document a severe impairment of Treasury market functioning, as indicated by a sharp increase in bid/ask spreads, a decline in market depth, and an increase in price impact measures.
    Date: 2020–12–04
  9. By: Nicola, Giancarlo; Cerchiello, Paola; Aste, Tomaso
    Abstract: In this work we investigate whether information theory measures like mutual information and transfer entropy, extracted from a bank network, Granger cause financial stress indexes like LIBOR-OIS (London Interbank Offered Rate-Overnight Index Swap) spread, STLFSI (St. Louis Fed Financial Stress Index) and USD/CHF (USA Dollar/Swiss Franc) exchange rate. The information theory measures are extracted from a Gaussian Graphical Model constructed from daily stock time series of the top 74 listed US banks. The graphical model is calculated with a recently developed algorithm (LoGo) which provides very fast inference model that allows us to update the graphical model each market day. We therefore can generate daily time series of mutual information and transfer entropy for each bank of the network. The Granger causality between the bank related measures and the financial stress indexes is investigated with both standard Granger-causality and Partial Granger-causality conditioned on control measures representative of the general economy conditions.
    Keywords: financial stress; granger causality; graphical models
    JEL: F3 G3
    Date: 2020–11–23
  10. By: Laurence M. Ball
    Abstract: This paper studies liquidity risk at the six largest U.S. banks. The starting point is the stress tests performed under the Liquidity Coverage Ratio (LCR) regulation, which compare a bank’s liquid assets to its loss of cash in a stress scenario that regulators say is based on the 2008 financial crisis. These tests find that all of the large banks could endure a liquidity crisis for 30 days without running out of cash. This paper argues, however, that some of the assumptions in the LCR stress scenario are not pessimistic enough to capture what could happen in a crisis like 2008. The paper then proposes changes in the dubious assumptions and performs revised stress tests. For 2019 Q4, the revised tests suggest it is unlikely that any of the six banks would survive a liquidity crisis for 30 days. This negative finding is most clear-cut for Goldman Sachs and Morgan Stanley.
    JEL: G21 G24 G28
    Date: 2020–11
  11. By: Matthias Fleckenstein; Francis A. Longstaff
    Abstract: We present a new approach for estimating private equity returns using secondary market prices for entrepreneurial business credit card securitizations. We show that the market requires a significantly higher premium for entrepreneurial credit risk than for household credit risk. Entrepreneurial risk is systematic in nature and has much in common with risks in corporate bond and real-estate-backed lending markets. The expected return on private equity is on the order of 14 percent and the volatility of private equity returns is comparable to that of the smallest quintile of publicly traded firms.
    JEL: G12 G5
    Date: 2020–11

This nep-fmk issue is ©2020 by Kwang Soo Cheong. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.