nep-fmk New Economics Papers
on Financial Markets
Issue of 2022‒06‒13
thirteen papers chosen by

  1. Mean-variance portfolio selection with dynamic attention behavior in a hidden Markov model By Y. Zhang; Z. Jin; J. Wei; G. Yin
  2. Portfolio Diversification Revisited By Charles Shaw
  3. Dynamics of Subjective Risk Premia By Stefan Nagel; Zhengyang Xu
  4. Climate Regulatory Risk and Corporate Bonds By Lee H. Seltzer; Laura Starks; Qifei Zhu
  5. Cryptocurrencies and Decentralized Finance (DeFi) By Igor Makarov; Antoinette Schoar
  6. Quantile return and volatility connectedness among Non-Fungible Tokens (NFTs) and (un)conventional assets By Urom, Christian; Ndubuisi, Gideon; Guesmi, Khaled
  7. Hot off the press: News-implied sovereign default risk By Dim, Chukwuma; Koerner, Kevin; Wolski, Marcin; Zwart, Sanne
  8. Expectations Data in Asset Pricing By Klaus Adam; Stefan Nagel
  9. Asset pricing models with measurement error problems: A new framework with Compact Genetic Algorithms By Erkin Diyarbakirlioglu; Marc Desban; Souad Lajili Jarjir
  10. Bitcoin Prices and the Realized Volatility of US Sectoral Stock Returns By Elie Bouri; Afees A. Salisu; Rangan Gupta
  11. Price Effects After One-Day Abnormal Returns and Crises in the Stock Markets By Alex Plastun; Xolani Sibande; Rangan Gupta; Chien-Chiang Lee
  12. Liquidity Provision to Leveraged ETFs and Equity Options Rebalancing Flows: Evidence from End-of-Day Stock Prices By Andrea Barbon; Heiner Beckmeyer; Andrea Buraschi; Mathis Moerke
  13. Failure of Gold, Bitcoin and Ethereum as safe havens during the Ukraine-Russia war By Alhonita Yatie

  1. By: Y. Zhang; Z. Jin; J. Wei; G. Yin
    Abstract: In this paper, we study closed-loop equilibrium strategies for mean-variance portfolio selection problem in a hidden Markov model with dynamic attention behavior. In addition to the investment strategy, the investor's attention to news is introduced as a control of the accuracy of the news signal process. The objective is to find equilibrium strategies by numerically solving an extended HJB equation by using Markov chain approximation method. An iterative algorithm is constructed and its convergence is established. Numerical examples are also provided to illustrate the results.
    Date: 2022–05
  2. By: Charles Shaw
    Abstract: We relax a number of assumptions in Alexeev and Tapon (2012) in order to account for non-normally distributed, skewed, multi-regime, and leptokurtic asset return distributions. We calibrate a Markov-modulated Levy process model to equity market data to demonstrate the merits of our approach, and show that the calibrated models do a good job of matching the empirical moments. Finally, we argue that much of the related literature on portfolio diversification relies on assumptions that are in tension with certain observable regularities and which, if ignored, may lead to underestimation of risk.
    Date: 2022–04
  3. By: Stefan Nagel; Zhengyang Xu
    Abstract: We examine subjective risk premia implied by return expectations of individual investors and professionals for aggregate portfolios of stocks, bonds, currencies, and commodity futures. While in-sample predictive regressions with realized excess returns suggest that objective risk premia vary countercyclically with business cycle variables and aggregate asset valuation measures, subjective risk premia extracted from survey data do not comove much with these variables. This lack of cyclicality of subjective risk premia is a pervasive property that holds in expectations of different groups of market participants and in different asset classes. A similar lack of cyclicality appears in out-of-sample forecasts of excess returns, which suggests that investors’ learning of forecasting relationships in real time may explain much of the cyclicality gap. These findings cast doubt on models that explain time-varying objective risk premia inferred from in-sample regressions with countercyclical variation in perceived risk or risk aversion. We further find a link between subjective perceptions of risk and subjective risk premia, which points toward a positive risk-return tradeoff in subjective beliefs.
    Date: 2022
  4. By: Lee H. Seltzer; Laura Starks; Qifei Zhu
    Abstract: Investor concerns about climate and other environmental regulatory risks suggest that these risks should affect corporate bond risk assessment and pricing. We test this hypothesis and find that firms with poor environmental profiles or high carbon footprints tend to have lower credit ratings and higher yield spreads, particularly when their facilities are located in states with stricter regulatory enforcement. Using the Paris Agreement as a shock to expected climate risk regulations, we provide evidence that climate regulatory risks causally affect bond credit ratings and yield spreads. Accordingly, the composition of institutional ownership also changes after the Agreement.
    JEL: G12 G14 G23 G28
    Date: 2022–04
  5. By: Igor Makarov; Antoinette Schoar
    Abstract: The paper provides an overview of cryptocurrencies and decentralized finance. The discussion lays out potential benefits and challenges of the new system and presents a comparison to the traditional system of financial intermediation. Our analysis highlights that while the DeFi architecture might have the potential to reduce transaction costs, similar to the traditional financial system, there are several layers where rents can accumulate due to endogenous constraints to competition. We show that the permissionless and pseudonymous design of DeFi generates challenges for enforcing tax compliance, anti-money laundering laws, and preventing financial malfeasance. We highlight ways to regulate the DeFi system which would preserve a majority of benefits of the underlying blockchain architecture but support accountability and regulatory compliance.
    JEL: G1 G2 G20 G21 G23 G3
    Date: 2022–04
  6. By: Urom, Christian (Paris School of Business, Paris); Ndubuisi, Gideon (UNU-MERIT, Maastricht University, and German Development Institute, Bonn); Guesmi, Khaled (Paris School of Business, Paris)
    Abstract: This paper uses the Quantile Vector-Autoregressive (Q-VAR) connectedness technique to examine the return and volatility connectedness among NFTs and (un)conventional assets including cryptocurrency, energy, technology, equity, precious metals, and fixed income financial assets across three quantiles corresponding to the normal, bearish, and bullish market conditions. It also explores the predictive powers of major macroeconomic and geopolitical indicators on the return and volatility connectedness across these three market conditions using a linear regression model. The main findings are as follows. First, the return and volatility connectedness vary across the market conditions, with the levels during the bearish and bullish market conditions being higher. Second, except under the bullish market condition, the total return connectedness is higher than those of total volatility connectedness. Third, NFTs are, at best, decoupled from (un)conventional assets during the normal market condition. Fourth, NFTs is a net return shock receivers except under the bullish market condition where it is a net transmitters. However, it is a net volatility shock receiver irrespective of the market condition. Fifth, during periods of economic crisis the total return and volatility connectedness rise (decreases) under the normal and bearish (bullish) market conditions. Finally, geopolitical risks, business environment conditions, and market and economic policy uncertainty are important predictors of return and volatility connectedness, although the predictive strength and direction vary across market conditions. We discuss the implications of our findings.
    Keywords: Non-Fungible Tokens, Green energy, Grey energy, Spillovers, Quantile connectedness
    JEL: G12 G14 G40 C58 G11
    Date: 2022–05–03
  7. By: Dim, Chukwuma; Koerner, Kevin; Wolski, Marcin; Zwart, Sanne
    Abstract: We develop a sovereign default risk index using natural language processing techniques and 10 million news articles covering over 100 countries. The index is a highfrequency measure of countries' default risk, particularly for those lacking marketbased measures: it correlates with sovereign CDS spreads, predicts rating downgrades, and reflects default risk information not fully captured by CDS spreads. We assess the influence of sovereign default concerns on equity markets and find that spikes in the index are negatively associated with same-week market returns, which reverses over the next week, indicating that investors might overreact to default concerns. Equity markets' reaction to default concerns is more pronounced and persistent for countries with tight fiscal constraints. The response to global, compared to country-specific, default concerns is much stronger, underlining the relevance of global "push" factors for local asset prices.
    Keywords: Sovereign default,Credit risk,Equity returns,Machine learning,Naturallanguage processing,Early warning indicators
    JEL: F30 G12 G15
    Date: 2022
  8. By: Klaus Adam; Stefan Nagel
    Abstract: Asset prices reflect investors' subjective beliefs about future cash flows and prices. In this chapter, we review recent research on the formation of these beliefs and their role in asset pricing. Return expectations of individual and professional investors in surveys differ markedly from those implied by rational expectations models. Variation in subjective expectations of future cash flows and price levels appear to account for much of aggregate stock market volatility. Mapping the survey evidence into agent expectations in asset pricing models is complicated by measurement errors and belief heterogeneity. Recent efforts to build asset pricing models that match the survey evidence on subjective belief dynamics include various forms of learning about payout or price dynamics, extrapolative expectations, and diagnostic expectations. Challenges for future research include the exploration of subjective risk perceptions, aggregation of measured beliefs, and links between asset market expectations and the macroeconomy.
    JEL: E71 G12 G41
    Date: 2022–04
  9. By: Erkin Diyarbakirlioglu (IRG - Institut de Recherche en Gestion - UPEC UP12 - Université Paris-Est Créteil Val-de-Marne - Paris 12 - Université Gustave Eiffel); Marc Desban (IRG - Institut de Recherche en Gestion - UPEC UP12 - Université Paris-Est Créteil Val-de-Marne - Paris 12 - Université Gustave Eiffel); Souad Lajili Jarjir (IRG - Institut de Recherche en Gestion - UPEC UP12 - Université Paris-Est Créteil Val-de-Marne - Paris 12 - Université Gustave Eiffel)
    Abstract: We implement a new framework to mitigate the errors-in-variables (EIV) problem in the estimation of asset pricing models. Considering an international data of portfolio stock returns from 1990 to 2021 widely used in empirical studies, we highlight the importance of the estimation method in time-series regressions. We compare the traditional Ordinary-Least Squares (OLS) method to an alternative estimator based on a Compact Genetic Algorithm (CGA) in the case of the CAPM, three-, and five-factor models. Based on intercepts, betas, adjusted R2 , and the Gibbons, Ross and Shanken (1989) test, we find that the CGA-based method outperforms overall the OLS for the three asset pricing models. In particular, we obtain less statistically significant intercepts, smoother R2 across different portfolios and lower GRS test statistics. Specifically, in line with Roll's critique (1977) on the unobservability of the market portfolio, we reduce the attenuation bias in market risk premium estimates. Moreover, our results are robust to alternative methods such as Instrumental Variables estimated with Generalized-Method of Moments (GMM). Our findings have several empirical and managerial implications related to the estimation of asset pricing models as well as their interpretation as a popular tool in terms of corporate financial decision-making.
    Keywords: Asset pricing,CAPM,Fama-French three-and five-factor models,Market Portfolio,Time-series regressions,Ordinary-Least Squares (OLS),Errors-in-variables (EIV),GMM with Instrumental Variables,Compact Genetic Algorithms (CGA)
    Date: 2022
  10. By: Elie Bouri (Adnan Kassar School of Business, Lebanese American University, Beirut, Lebanon); Afees A. Salisu (Centre for Econometrics & Applied Research, Ibadan, Nigeria; Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)
    Abstract: Recent research suggests stronger ties between Bitcoin and US stock markets. In this paper, we examine the predictive power of Bitcoin prices for the realized volatility of the US stock market index and its various sectoral indices. Using data over the period 22 November 2017 and 30 December 2021, we conduct in-sample and out-of-sample analyses over multiple forecast horizons and evidence that Bitcoin prices contain significant predictive power for the volatility of US stocks. Specifically, an inverse relationship exists between Bitcoin prices and the realized volatility of US stock sector indices. The model that includes Bitcoin prices consistent outperforms the benchmark historical average model, irrespective of the various stock sectors and multiple of forecast horizons. The use of Bitcoin prices as a predictor yields higher economic gains. These findings highlight the power and utility of observing Bitcoin prices when forecasting the realized volatility of US stock sectors, which matter to practitioners, and academics, and policymakers.
    Keywords: Bitcoin prices, S&P 500 index, US stock sector indices, realized volatility prediction, economic gains
    Date: 2022–05
  11. By: Alex Plastun (Faculty of Economics and Management Sumy State University, Sumy, Ukraine); Xolani Sibande (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Chien-Chiang Lee (School of Economics and Management, Nanchang University, Nanchang, China)
    Abstract: We investigate price effects after one-day abnormal returns during crises in US, Japanese, Chinese, Russian and Brazilian stock markets, using the ANOVA, Mann-Whitney, t-tests, the modified cumulative abnormal return approach, regression analysis with dummy variables, and the trading simulation approach. The results suggest that the momentum effect is the most typical case of price behaviour after the days with positive abnormal returns, especially in emerging markets in pre and post crisis periods. Interestingly the momentum effect in developed markets changes into contrarian during crisis periods. However, in emerging markets the momentum effect prevails even in crisis periods. However, the power of the detected effects is weak. These effects do not provide opportunities to beat the market and might result from prevailing positive returns in these stock markets.
    Keywords: Momentum Effect, Contrarian Effect, Abnormal Returns, Stock Market, Crisis
    JEL: G12 C63
    Date: 2022–05
  12. By: Andrea Barbon (University of St. Gallen); Heiner Beckmeyer (University of Muenster - Finance Center Muenster); Andrea Buraschi (Imperial College Business School; Centre for Economic Policy Research (CEPR)); Mathis Moerke (University of St. Gallen - Swiss Institute of Banking and Finance)
    Abstract: Rebalancing of leveraged ETFs (LETFs) and delta-hedging of equity options by intermediaries are two distinct and economically significant sources of liquidity demands. We show that they induce end-of-day momentum and mean-reversion in returns. While gamma effects are persistent throughout our sample, LETFs effects have decreased over time. We empirically study these effects and their potential drivers. We find that LETF flows attract more liquidity provision and their effects on prices are shorter-lived. Intermediaries can strategically decide the timing of their delta-hedging, resulting in less predictable flows. This shows the benefits of information disclosure on market liquidity and price distortion.
    Keywords: Liquidity Provision, Gamma Exposure, Option Delta-Hedging, Leveraged ETF, End-of-Day Momentum
    JEL: G12 G13 G14 G23
    Date: 2022–05
  13. By: Alhonita Yatie (BSE - Bordeaux Sciences Economiques - UB - Université de Bordeaux - CNRS - Centre National de la Recherche Scientifique)
    Abstract: This paper studies the impact of fear, uncertainty and market volatility caused by the Ukraine-Russia war on crypto-assets returns (Bitcoin and Ethereum) and Gold returns. We use the searches on Wikipedia trends as proxies of uncertainty and fear and two volatility indices: S&P500 VIX and the Russian VIX (RVIX). The results show that Bitcoin, Ethereum and Gold failed as safe havens during this war.
    Keywords: H56,Safe haven,Gold,crypto-assets,Russia,Ukraine,G15 War,G12,G32
    Date: 2022–03–23

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