nep-fmk New Economics Papers
on Financial Markets
Issue of 2021‒01‒11
six papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Hang in There: Stock Market Reactions to Withdrawals of COVID-19 Stimulus Measures By Jorge A Chan-Lau; Yunhui Zhao
  2. The Cross-Sectional Pricing of Corporate Bonds Using Big Data and Machine Learning By Turan G. Bali; Amit Goyal; Dashan Huang; Fuwei Jiang; Quan Wen
  3. Equity tail risk in the treasury bond market By Mirco Rubin; Dario Ruzzi
  4. Information and Liquidity in the Market for Foreign Currency Denominated Sovereign Bonds By David S. Miller
  5. The Price of Haircuts: Private and Official Default By Silvia Marchesi; Tania Masi; Pietro Bomprezzi
  6. Measuring stock market integration during the Gold Standard By Rebecca Stuart

  1. By: Jorge A Chan-Lau; Yunhui Zhao
    Abstract: The COVID-19 pandemic prompted unprecedented economic stimulus worldwide. We empirically examine the impact of a withdrawal of fiscal stimulus policies on the stock markets. After constructing a database of withdrawal events, we use event study analysis and cross-country regressions to assess the difference between the pre- and post-event stock price returns. We find that markets react negatively to premature withdrawals—defined as withdrawals at a time when the daily COVID cases are high relative to their historical average—likely reflecting concerns about the withdrawal impact on the prospects for economic recovery. The design of a successful exit strategy from COVID-19 policy responses should account for these concerns.
    Date: 2020–12–18
  2. By: Turan G. Bali (Georgetown University - Robert Emmett McDonough School of Business); Amit Goyal (University of Lausanne; Swiss Finance Institute); Dashan Huang (Singapore Management University - Lee Kong Chian School of Business); Fuwei Jiang (Central University of Finance and Economics (CUFE)); Quan Wen (Georgetown University - Department of Finance)
    Abstract: We provide a comprehensive study on the cross-sectional predictability of corporate bond returns using big data and machine learning. We examine whether a large set of equity and bond characteristics drive the expected returns on corporate bonds. Using either set of characteristics, we find that machine learning methods substantially improve the out-of-sample predictive power for bond returns, compared to the traditional linear regression models. While equity characteristics produce significant explanatory power for bond returns, their incremental predictive power relative to bond characteristics is economically and statistically insignificant. Bond characteristics provide as strong forecasting power for future equity returns as using equity characteristics alone. However, bond characteristics do not offer additional predictive power above and beyond equity characteristics when we combine both sets of predictors.
    Keywords: machine learning, big data, corporate bond returns, cross-sectional return predictability
    JEL: G10 G11 C13
    Date: 2020–09
  3. By: Mirco Rubin (EDHEC Business School); Dario Ruzzi (Bank of Italy)
    Abstract: This paper quantifies the effects of equity tail risk on the US government bond market. We estimate equity tail risk as the option-implied stock market volatility that stems from large negative jumps as in Bollerslev, Todorov and Xu (2015), and assess its value in reduced-form predictive regressions for Treasury returns and an affine term structure model for interest rates. We document that the left tail volatility of the stock market significantly predicts one-month-ahead excess returns on Treasuries both in- and out-of-sample. The incremental value of employing equity tail risk as a return forecasting factor can be of economic importance for a mean-variance investor trading bonds. The estimated term structure model shows that equity tail risk is priced in the US government bond market. Consistent with the theory of flight-to-safety, we find that Treasury prices increase and funds flow from equities into bonds when the perception of tail risk is higher. Our results concerning the predictive power and pricing of equity tail risk extend to major government bond markets in Europe.
    Keywords: bond return predictability, equity tail risk, bond risk premium, flight-to-safety, affine term structure model
    JEL: C52 C58 G12 E43
    Date: 2020–12
  4. By: David S. Miller
    Abstract: This note finds a negative, non-linear relationship between bond yield and liquidity using data on Portuguese, Irish, Italian, Greek, and Spanish (PIIGS) sovereign bonds from 2010-2015. This relationship is predicted by the asymmetric information model of bond liquidity by Holmstrom (2015) and Gorton (2017).
    Date: 2020–12–28
  5. By: Silvia Marchesi; Tania Masi; Pietro Bomprezzi
    Abstract: In this paper we examine the link between sovereign defaults and credit risk, by taking into account the depth of a debt restructuring (haircut) and by distinguishing between commercial and official debt. The focus is on debt restructuring events, which take place at the end of a default, or renegotiation spell. Using dyadic data for the relationship between rated countries and agencies, we find that private credit events are more costly than private ones, when it comes to ratings. Moreover, the rating decline is larger for cases with deeper haircuts. Similar results are found when taking bond yield spreads (EMBIG) as measure of a country’s creditworthiness. Results are robust to using the local projection approach (Jordà and Taylor 2016) for the identi…cation of causal effects. Therefore, we find evidence that official and private defaults may have different costs and then induce selective defaults. In the wake of the Covid-19 pandemic, since official lending is likely to increase and official debt sustainability is going to become an important concern, understanding the difference between private and official deals has become even more important.
    Keywords: Sovereign defaults, Haircut, Credit Rating Agencies, bond yield spreads, local projection.
    JEL: F34 G15 G24 H63
    Date: 2021–01
  6. By: Rebecca Stuart
    Abstract: This paper uses a broad geographical sample to investigate stock market integration during the classical Gold Standard. It is novel in estimating 'global components' of stock market returns, using methods proposed by Volosovych (2011), Pukthuanthong and Roll (2009) and Ciccarelli and Mojon (2010). Contrary to the existing literature, all three measures suggest that integration increased during the first decades of the Gold Standard before levelling off thereafter. However, a comparison with more recent data suggests the level of integration was low compared to today. The results are robust to alternative formulations of the global component and alternative measures of returns.
    Keywords: stock returns, principal components analysis, Gold Standard.
    JEL: G1 N2
    Date: 2021–01

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