nep-fmk New Economics Papers
on Financial Markets
Issue of 2022‒01‒31
twelve papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. A Survey of Hedge and Safe Havens Assets against G-7 Stock Markets before and during the COVID-19 Pandemic By Ozdemir, Huseyin; Ozdemir, Zeynel Abidin
  2. How did Australian financial markets react to the COVID-19 vaccine rollout? Fresh evidence from quantile copula spectrum analysis By Matsuki, Takashi; Pan, Lei
  3. Optimal Portfolio Choice and Stock Centrality for Tail Risk Events By Christis Katsouris
  4. The credit spread curve. I: Fundamental concepts, fitting, par-adjusted spread, and expected return By Richard J. Martin
  5. Deep Partial Hedging By Songyan Hou; Thomas Krabichler; Marcus Wunsch
  6. ESG features explain one bit of idiosyncratic price returns By J\'er\'emi Assael; Laurent Carlier; Damien Challet
  7. Evolutionary finance for multi-asset investors By Michael Schnetzer; Thorsten Hens
  8. Strategic complementarity and substitutability of investment strategies By Nikolay Doskov; Thorsten Hens; Klaus Reiner Schenk-Hoppé
  9. Modelling the volatility of Bitcoin returns using Nonparametric GARCH models By Mestiri, Sami
  10. Hedging Cryptocurrency Options By Jovanka Lili Matic; Natalie Packham; Wolfgang Karl H\"ardle
  11. A revised comparison between FF five-factor model and three-factor model,based on China's A-share market By Zhijing Zhang; Yue Yu; Qinghua Ma; Haixiang Yao
  12. Forecasting Realized Volatility Using Machine Learning and Mixed-Frequency Data (the Case of the Russian Stock Market) By Vladimir Pyrlik; Pavel Elizarov; Aleksandra Leonova

  1. By: Ozdemir, Huseyin (Gazi University); Ozdemir, Zeynel Abidin (Ankara HBV University)
    Abstract: We propose a new Sharpe ratio index obtained from return and volatility spillover indices to individual assets from the whole financial system. We use our new approach to shed light on a new perspective on a hot topic examining the safe-haven assets after Covid-19. To do that, we compare both hedge and safe-haven properties of gold, Bitcoin, and crude oil against G-7 stock markets by using daily return and volatility data from September 2013 to October 2021. Our empirical findings show that the hedging effectiveness of gold, Bitcoin, and crude oil varies overtime before the Covid-19 pandemic. Furthermore, according to our analysis results, only Bitcoin acts as a safe haven against G-7 stock markets during most of the Covid-19 pandemic time.
    Keywords: sharpe ratio, safe haven, hedge, spillover effect, G-7 countries
    JEL: C58 G10
    Date: 2021–11
  2. By: Matsuki, Takashi; Pan, Lei
    Abstract: We provide the first study of how large and persistent the Australian financial markets reacted to the COVID-19 vaccine rollout. Using the novel quantile copula coherency developed by Baruník and Kley (2019), our study properly detects short- and long-lived significant reactions of the stock price index and foreign exchange returns to the vaccine rate variation.
    Keywords: Australia; Financial markets; COVID-19 vaccine; Quantile copula spectrum; Quantile coherency
    JEL: G1 H51 I18
    Date: 2021–12
  3. By: Christis Katsouris
    Abstract: We propose a novel risk matrix to characterize the optimal portfolio choice of an investor with tail concerns. The diagonal of the matrix contains the Value-at-Risk of each asset in the portfolio and the off-diagonal the pairwise Delta-CoVaR measures reflecting tail connections between assets. First, we derive the conditions under which the associated quadratic risk function has a closed-form solution. Second, we examine the relationship between portfolio risk and eigenvector centrality. Third, we show that portfolio risk is not necessarily increasing with respect to stock centrality. Forth, we demonstrate under certain conditions that asset centrality increases the optimal weight allocation of the asset to the portfolio. Overall, our empirical study indicates that a network topology which exhibits low connectivity is outperformed by high connectivity based on a Sharpe ratio test.
    Date: 2021–12
  4. By: Richard J. Martin
    Abstract: The notion of a credit spread curve is fundamental in fixed income investing, but in practice it is not `given' and needs to be constructed from bond prices either for a particular issuer, or for a sector rating-by-rating. Rather than attempting to fit spreads -- and as we discuss here, the Z-spread is unsuitable -- we fit parametrised survival curves. By deriving a valuation formula for a risky bond, we explain and avoid the problem that bonds with a high dollar price trade at a higher yield or spread than those with low dollar price (at the same maturity point), even though they do not necessarily offer better value. In fact, a concise treatment of this effect is elusive, and much of the academic literature on risky bond pricing, including a well-known paper by Duffie and Singleton (1997), is fundamentally incorrect. We then proceed to show how to calculate carry, rolldown and relative value for bonds/CDS. Also, once curve construction has been programmed and automated we can run it historically and assess the way a curve has moved over time. This provides the necessary grounding for econometric and arbitrage-free models of curve dynamics, which will be pursued in later work, as well as assessing how the perceived relative value of a particular instrument varies over time.
    Date: 2022–01
  5. By: Songyan Hou; Thomas Krabichler; Marcus Wunsch
    Abstract: Using techniques from deep learning (cf. [B\"uh+19]), we show that neural networks can be trained successfully to replicate the modified payoff functions that were first derived in the context of partial hedging by [FL00]. Not only does this approach better accommodate the realistic setting of hedging in discrete time, it also allows for the inclusion of transaction costs as well as general market dynamics.
    Date: 2021–12
  6. By: J\'er\'emi Assael; Laurent Carlier; Damien Challet
    Abstract: We systematically investigate the links between price returns and ESG features. We propose a cross-validation scheme with random company-wise validation to mitigate the relative initial lack of quantity and quality of ESG data, which allows us to use most of the latest and best data to both train and validate our models. Boosted trees successfully explain a single bit of annual price returns not accounted for in the traditional market factor. We check with benchmark features that ESG features do contain significantly more information than basic fundamental features alone. The most relevant sub-ESG feature encodes controversies. Finally, we find opposite effects of better ESG scores on the price returns of small and large capitalization companies: better ESG scores are generally associated with larger price returns for the latter, and reversely for the former.
    Date: 2022–01
  7. By: Michael Schnetzer (Sammelstiftung Vita); Thorsten Hens (University of Zurich - Department of Banking and Finance; Norwegian School of Economics and Business Administration (NHH); Swiss Finance Institute)
    Abstract: Standard strategic asset allocation procedures usually neglect market interaction. However, returns are not generated in a vacuum but are the result of the market's price discovery mechanism which is driven by investors' investment strategies. Evolutionary finance accounts for this and endogenizes asset prices. This paper develops a multi-asset evolutionary finance model. Requiring little more than dividend and interest rate data, it facilitates an interesting glimpse into the inner workings of financial markets and provides a valuable guide to this class of models. While traditional mean/variance optimization is static and concerned with finding the optimal asset allocation, evolutionary portfolio theory is dynamic and its focus is on finding the optimal investment strategy. This paper shows that yield-based strategies generate asset allocations that outperform competing alternatives. Therefore, strategic asset allocation approaches that rely on such an economic foundation are evolutionarily advantageous for multi-asset investors.
    Keywords: Evolutionary finance, strategic asset allocation, multi-asset.
    JEL: G10 G11 G17
    Date: 2022–01
  8. By: Nikolay Doskov (LGT Capital Partners, Pfaffikon); Thorsten Hens (University of Zurich - Department of Banking and Finance; Norwegian School of Economics and Business Administration (NHH); Swiss Finance Institute); Klaus Reiner Schenk-Hoppé (University of Manchester - Department of Economics)
    Abstract: Institutional investors in equities tend to follow well-defined investment strategies, often based on factors such as size, value, momentum, quality, dividend yield and other stock characteristics. This paper explores the impact of capital flows between investment strategies on the cross-section of their performance. We find that the correlation between factor performance and the cyclical nature of risk premia can be explained by capital flows. The CAPM with a non-mean-variance investor supports these results.
    Date: 2022–01
  9. By: Mestiri, Sami
    Abstract: Bitcoin has received a lot of attention from both investors and analysts, as it forms the highest market capitalization in the cryptocurrency market. The use of parametric GARCH models to characterise the volatility of Bitcoin returns is widely observed in the empirical literature. In this paper, we consider an alternative approach involving non-parametric method to model and forecast Bitcoin return volatility. We show that the out-of-sample volatility forecast of the non-parametric GARCH model yields superior performance relative to an extensive class of parametric GARCH models. The improvement in forecasting accuracy of Bitcoin return volatility based on the non-parametric GARCH model suggests that this method offers an attractive and viable alternative to the commonly used parametric GARCH models.
    Keywords: Bitcoin; volatility; GARCH; Nonparametric; Forecasting.
    JEL: C14 C53 C58
    Date: 2021–12–13
  10. By: Jovanka Lili Matic; Natalie Packham; Wolfgang Karl H\"ardle
    Abstract: The cryptocurrency (CC) market is volatile, non-stationary and non-continuous. This poses unique challenges for pricing and hedging CC options. We study the hedge behaviour and effectiveness for a wide range of models. First, we calibrate market data to SVI-implied volatility surfaces to price options. To cover a wide range of market dynamics, we generate price paths using two types of Monte Carlo simulations. In the first approach, price paths follow an SVCJ model (stochastic volatility with correlated jumps). The second approach simulates paths from a GARCH-filtered kernel density estimation. In these two markets, options are hedged with models from the class of affine jump diffusions and infinite activity L\'evy processes. Including a wide range of market models allows to understand the trade-off in the hedge performance between complete, but overly parsimonious models, and more complex, but incomplete models. Dynamic Delta, Delta-Gamma, Delta-Vega and minimum variance hedge strategies are applied. The calibration results reveal a strong indication for stochastic volatility, low jump intensity and evidence of infinite activity. With the exception of short-dated options, a consistently good performance is achieved with Delta-Vega hedging in stochastic volatility models. Judging on the calibration and hedging results, the study provides evidence that stochastic volatility is the driving force in CC markets.
    Date: 2021–11
  11. By: Zhijing Zhang; Yue Yu; Qinghua Ma; Haixiang Yao
    Abstract: In allusion to some contradicting results in existing research, this paper selects China's latest stock data from 2005 to 2020 for empirical analysis. By choosing this periods' data, we avoid the periods of China's significant stock market reforms to reduce the impact of the government's policy on the factor effect. In this paper, the redundant factors (HML, CMA) are orthogonalized, and the regression analysis of 5*5 portfolio of Size-B/M and Size-Inv is carried out with these two orthogonalized factors. It found that the HML and the CMA are still significant in many portfolios, indicating that they have a strong explanatory ability, which is also consistent with the results of GRS test. All these show that the five-factor model has a better ability to explain the excess return rate. In the concrete analysis, this paper uses the methods of the five-factor 25-group portfolio returns calculation, the five-factor regression analysis, the orthogonal treatment, the five-factor 25-group regression and the GRS test to more comprehensively explain the excellent explanatory ability of the five-factor model to the excess return. Then, we analyze the possible reasons for the strong explanatory ability of the HML, CMA and RMW from the aspects of price to book ratio, turnover rate and correlation coefficient. We also give a detailed explanation of the results, and analyze the changes of China's stock market policy and investors' investment style recent years. Finally, this paper attempts to put forward some useful suggestions on the development of asset pricing model and China's stock market.
    Date: 2021–10
  12. By: Vladimir Pyrlik; Pavel Elizarov; Aleksandra Leonova
    Abstract: We assess the performance of selected machine learning algorithms (lasso, random forest, gradient boosting, and long short-term memory) in forecasting the daily realized volatility of returns of selected top stocks in the Russian stock market in comparison with a heterogeneous autoregressive realized volatility benchmark in 2018-2020. We seek to improve the predictive power of the models by including various economic indicators that carry information about future volatility. We find that lasso delivers a good combination of easy implementation and forecast precision. The other algorithms require fine-tuning and frequent re-training, otherwise they are likely to fail to outperform the benchmark often enough. Only the basic lagged log-RV values are significant explanatory variables in terms of the benchmark in-sample quality. Many economic indicators of mixed frequencies improve the predictive power of lasso though, including calendar and overnight effects, financial spillovers from local and global markets, and various macroeconomics indicators.
    Keywords: heterogeneous autoregressive model; machine learning; lasso; gradient boosting; random forest; long short-term memory; realized volatility; Russian stock market; mixed-frequency data;
    Date: 2021–11

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