nep-fmk New Economics Papers
on Financial Markets
Issue of 2021‒01‒18
ten papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Bull and Bear Markets During the COVID-19 Pandemic By Maheu, John M; McCurdy, Thomas H; Song, Yong
  2. Credit Risk in a Pandemic By Byström, Hans
  3. Revising the Impact of Global Commodity Prices and Global Stock Market Volatility Shocks: Effects across Countries* By Wensheng Kang; Ronald Ratti Bd; Joaquin Vespignani
  4. Should Stock Returns Predictability be hooked on Long Horizon Regressions? By Theologos Dergiades; Panos K. Pouliasis
  5. On the origin of systemic risk By Montagna, Mattia; Torri, Gabriele; Covi, Giovanni
  6. Forecasting Daily Volatility of Stock Price Index Using Daily Returns and Realized Volatility By Takahashi, Makoto; Watanabe, Toshiaki; Omori, Yasuhiro
  7. Portfolio Construction Using Stratified Models By Jonathan Tuck; Shane Barratt; Stephen Boyd
  8. Modeling asset allocation strategies and a new portfolio performance score By Apostolos Chalkis; Ioannis Z. Emiris
  9. Market Pricing of Fundamentals at the Shanghai Stock Exchange: Evidence from a Dividend Discount Model with Adaptive Expectations By Mingyang Li; Linlin Niu; Andrew Pua
  10. OTC discount By de Roure, Calebe; Mönch, Emanuel; Pelizzon, Loriana; Schneider, Michael

  1. By: Maheu, John M; McCurdy, Thomas H; Song, Yong
    Abstract: The COVID-19 pandemic has caused severe disruption to economic and financial activity worldwide. We assess what happened to the aggregate U.S. stock market during this period, including implications for both short and long-horizon investors. Using the model of Maheu, McCurdy and Song (2012), we provide smoothed estimates and out-of-sample forecasts associated with stock market dynamics during the pandemic. We identify bull and bear market regimes including their bull correction and bear rally components, demonstrate the model's performance in capturing periods of significant regime change, and provide forecasts that improve risk management and investment decisions. The paper concludes with out-of-sample forecasts of market states one year ahead.
    Keywords: predictive density, long-horizon returns, Markov switching
    JEL: C1 C11 C22 G1 G11 G17
    Date: 2020–11
  2. By: Byström, Hans (Department of Economics, Lund University)
    Abstract: Using different measures of how the Covid-19 pandemic progresses we find that the level of credit risk among US blue chip companies increases in tandem with the Covid-19 virus spreading. The credit risk increases dramatically during the pandemic, but we find it to be short of the levels seen during the 2008–2009 financial crisis. Furthermore, we find weekly ups and downs in credit risk and virus impact to be significantly positively correlated throughout the pandemic. Finally, Basel II capital requirements increase drastically when the pandemic strikes but, again, not to the levels seen during the financial crisis.
    Keywords: credit risk; Covid-19; equity market; debt market; CDS; Merton model; Basel II
    JEL: G10 G33 I18
    Date: 2021–01–04
  3. By: Wensheng Kang; Ronald Ratti Bd; Joaquin Vespignani (UTAS - University of Tasmania [Hobart, Australia])
    Abstract: We investigate the time-varying dynamics of global stock market volatility, commodity prices, domestic output and consumer prices. We find (i) stock market volatility and commodity price shocks impact each other and the economy in a gradual and endogenous adjustment process, (ii) impact of commodity price shock on global stock market volatility is significant during global financial crises, (iii) effects of global stock market volatility on the US output are amplified by endogenous commodity price responses, (iv) effects of global stock market volatility shocks on the economy are heterogeneous across nations and relatively larger in twelve developed countries, (v) four developing/small economies are more vulnerable to commodity price shocks.
    Keywords: Global commodity prices,Global stock market volatility,Output,Heterogeneity JEL Codes: D80,E44,E66,F62,G10
    Date: 2020–12–16
  4. By: Theologos Dergiades (Department of International and European Studies, University of Macedonia); Panos K. Pouliasis (Cass Business School)
    Abstract: This paper re-examines stock returns predictability over the business cycle using price-dividend and price-earnings valuation ratios as predictors. Unlike prior studies that habitually implement long-horizon/predictive regressions, we conduct a testing framework in the frequency domain. Predictive regressions support no predictability; in contrast, our results in the frequency domain verify significant predictability at medium and long horizons. To robustify predictability patterns, the analysis is executed repetitively for fixed-length rolling samples of various sizes. Overall, stock returns are predictable for wavelengths higher than five years. This finding is robust and independent of time, window size and predictor.
    Keywords: Stock Returns; Long-Horizon Predictability, Frequency Domain.
    JEL: C10 G17 G32
    Date: 2021–02
  5. By: Montagna, Mattia; Torri, Gabriele; Covi, Giovanni
    Abstract: Systemic risk in the banking sector is usually associated with long periods of economic downturn and very large social costs. On one hand, shocks coming from correlated exposures towards the real economy may induce correlation in banks' default probabilities thereby increasing the likelihood for systemic-tail events like the 2008 Great Financial Crisis. On the other hand, financial contagion also plays an important role in generating large-scale market failures, amplifying the initial shocks coming from the real economy. To study the sources of these rare phenomena, we propose a new definition of systemic risk (i.e. the probability of a large number of banks going into distress simultaneously) and thus we develop a multilayer microstructural model to study empirically the determinants of systemic risk. The model is then calibrated on the most comprehensive granular dataset for the euro area banking sector, capturing roughly 96% or EUR 23.2 trillion of euro area banks' total assets over the period 2014-2018. The output of the model decompose and quantify the sources of systemic risk showing that correlated economic shocks, financial contagion mechanisms, and their interaction are the main sources of systemic events. The results obtained with the simulation engine resemble common market-based systemic risk indicators and empirically corroborate findings from existing literature. This framework gives regulators and central bankers a tool to study systemic risk and its developments, pointing out that systemic events and banks’ idiosyncratic defaults have different drivers, hence implying different policy responses. JEL Classification: D85, G17, G33, L14
    Keywords: financial contagion, microstructural models, systemic risk
    Date: 2020–12
  6. By: Takahashi, Makoto; Watanabe, Toshiaki; Omori, Yasuhiro
    Abstract: This paper compares the volatility predictive abilities of some time-varying volatility models such as thestochastic volatility (SV) and exponential GARCH (EGARCH) models using daily returns, the heterogeneous au-toregressive (HAR) model using daily realized volatility (RV) and the realized SV (RSV) and realized EGARCH(REGARCH) models using the both. The data are the daily return and RV of Dow Jones Industrial Aver-age (DJIA) in US and Nikkei 225 (N225) in Japan. All models are extended to accommodate the well-knownphenomenon in stock markets of a negative correlation between today's return and tomorrow's volatility. Weestimate the HAR model by the ordinary least squares (OLS) and the EGARCH and REGARCH models bythe quasi-maximum likelihood (QML) method. Since it is not straightforward to evaluate the likelihood of theSV and RSV models, we apply a Bayesian estimation via Markov chain Monte Carlo (MCMC) to them. Byconducting predictive ability tests and analyses based on model confidence sets, we confirm that the models us-ing RV outperform the models without RV, that is, the RV provides useful information on forecasting volatility.Moreover, we find that the realized SV model performs best and the HAR model can compete with it. Thecumulative loss analysis suggests that the differences of the predictive abilities among the models are partlycaused by the rise of volatility.
    Keywords: Exponential GARCH (EGARCH) model, Heterogeneous autoregressive (HAR) model, Markov chain Monte Carlo (MCMC), Realized volatility, Stochastic volatility, Volatility forecasting
    JEL: C11 C22 C53 C58 G17
    Date: 2021–01
  7. By: Jonathan Tuck; Shane Barratt; Stephen Boyd
    Abstract: In this paper we develop models of asset return mean and covariance that depend on some observable market conditions, and use these to construct a trading policy that depends on these conditions, and the current portfolio holdings. After discretizing the market conditions, we fit Laplacian regularized stratified models for the return mean and covariance. These models have a different mean and covariance for each market condition, but are regularized so that nearby market conditions have similar models. This technique allows us to fit models for market conditions that have not occurred in the training data, by borrowing strength from nearby market conditions for which we do have data. These models are combined with a Markowitz-inspired optimization method to yield a trading policy that is based on market conditions. We illustrate our method on a small universe of 18 ETFs, using four well known and publicly available market variables to construct 10000 market conditions, and show that it performs well out of sample. The method, however, is general, and scales to much larger problems, that presumably would use proprietary data sources and forecasts along with publicly available data.
    Date: 2021–01
  8. By: Apostolos Chalkis; Ioannis Z. Emiris
    Abstract: We discuss a powerful, geometric representation of financial portfolios and stock markets, which identifies the space of portfolios with the points lying in a simplex convex polytope. The ambient space has dimension equal to the number of stocks, or assets. Although our statistical tools are quite general, in this paper we focus on the problem of portfolio scoring. Our contribution is to introduce an original computational framework to model portfolio allocation strategies, which is of independent interest for computational finance. To model asset allocation strategies, we employ log-concave distributions centered on portfolio benchmarks. Our approach addresses the crucial question of evaluating portfolio management, and is relevant to the individual private investors as well as financial organizations. We evaluate portfolio performance, in a certain time period, by providing a new portfolio score, based on the aforementioned framework and concepts. In particular, it relies on the expected proportion of allocations that the portfolio outperforms when a certain set of strategies take place in that time period. We also discuss how this set of strategies -- and the knowledge one may have about them -- could vary in our framework, and we provide additional versions of our score in order to obtain a more complete picture of its performance. In all cases, we show that the score computations can be performed efficiently. Last but not least, we expect this framework to be useful in portfolio optimization and in automatically identifying extreme phenomena in a stock market.
    Date: 2020–12
  9. By: Mingyang Li; Linlin Niu; Andrew Pua
    Abstract: We study market pricing of fundamentals at the Shanghai Stock Exchange, incorporating possible irrational pricing behavior with adaptive expectation. Using panel data of listed stocks to overcome the limited information in aggregate time series data, we estimated key parameters of the price elasticity of dividends and the expectation adjustment based on a linear dynamic panel data model. We use a major subset of stocks with stationary real prices and cash flows and apply methods that correct for incidental parameter bias. The resulting price elasticity of dividends is about 0.46 (0.35) based on annual (quarterly) data, which is sizable given high PD (PE) ratios in the market. Our results imply that slow expectation adjustment contributes to “bubble-like” price patterns. We also show prices significantly react to macro information related to the discount rate, but these effects are very sensitive to the information set used.
    Keywords: Stock price determination, Adaptive expectation, Time-varying discount rate, Incidental parameter bias
    JEL: G12 C33 C58
    Date: 2020–12–30
  10. By: de Roure, Calebe; Mönch, Emanuel; Pelizzon, Loriana; Schneider, Michael
    Abstract: We document a sizable OTC discount in the interdealer market for German sovereign bonds where exchange and over-the-counter trading coexist: the vast majority of OTC prices are favorable with respect to exchange quotes. This is a challenge for theories of OTC markets centered around search frictions but consistent with models of hybrid markets based on information frictions. We show empirically that proxies for both frictions determine variation in the discount, which is largely passed on to customers. Dealers trade on the exchange for immediacy and via brokers for opacity and anonymity, highlighting the complementary roles played by the different protocols.
    Keywords: Market Microstructure,Hybrid Markets,Venue Choice,Interdealer Brokerage,Fixed-Income,OTC Markets,Search Frictions,Information Frictions
    JEL: D4 D47 G1 G14 G24
    Date: 2020

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