nep-fmk New Economics Papers
on Financial Markets
Issue of 2023‒08‒14
eight papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Portfolio Optimization: A Comparative Study By Jaydip Sen; Subhasis Dasgupta
  2. Valuation of Equity Linked Securities with Guaranteed Return By David Xiao
  3. Liquidity Premium and Liquidity-Adjusted Return and Volatility: illustrated with a Liquidity-Adjusted Mean Variance Framework and its Application on a Portfolio of Crypto Assets By Qi Deng
  4. Are ESG ratings informative to forecast idiosyncratic risk? By Christophe Boucher; Wassim Le Lann; Stéphane Matton; Sessi Tokpavi
  5. Currency Risk Premiums: A Multi-horizon Perspective By Mikhail Chernov; Magnus Dahlquist
  6. Systemic Tail Risk: High-Frequency Measurement, Evidence and Implications By Deniz Erdemlioglu; Christopher J. Neely; Xiye Yang
  7. Generic Forward Curve Dynamics for Commodity Derivatives By David Xiao
  8. A Scalable Reinforcement Learning-based System Using On-Chain Data for Cryptocurrency Portfolio Management By Zhenhan Huang; Fumihide Tanaka

  1. By: Jaydip Sen; Subhasis Dasgupta
    Abstract: Portfolio optimization has been an area that has attracted considerable attention from the financial research community. Designing a profitable portfolio is a challenging task involving precise forecasting of future stock returns and risks. This chapter presents a comparative study of three portfolio design approaches, the mean-variance portfolio (MVP), hierarchical risk parity (HRP)-based portfolio, and autoencoder-based portfolio. These three approaches to portfolio design are applied to the historical prices of stocks chosen from ten thematic sectors listed on the National Stock Exchange (NSE) of India. The portfolios are designed using the stock price data from January 1, 2018, to December 31, 2021, and their performances are tested on the out-of-sample data from January 1, 2022, to December 31, 2022. Extensive results are analyzed on the performance of the portfolios. It is observed that the performance of the MVP portfolio is the best on the out-of-sample data for the risk-adjusted returns. However, the autoencoder portfolios outperformed their counterparts on annual returns.
    Date: 2023–07
  2. By: David Xiao
    Abstract: Equity-linked securities with a guaranteed return become very popular in financial markets ether as investment instruments or life insurance policies. The contract pays off a guaranteed amount plus a payment linked to the performance of a basket of equities averaged over a certain period. This paper presents a new model for valuing equity-linked securities. Our study shows that the security price can be replicated by the sum of the guaranteed amount plus the price of an Asian style option on the basket. Analytical formulas are derived for the security price and corresponding hedge ratios. The model appears to be accurate over a wide range of underlying security parameters according to numerical studies. Finally, we use our model to value a segregated fund with a guarantee at maturity.
    Date: 2023–06
  3. By: Qi Deng
    Abstract: We establish innovative measures of liquidity premium Beta on both asset and portfolio levels, and corresponding liquidity-adjusted return and volatility, for selected crypto assets. We develop liquidity-adjusted ARMA-GARCH/EGARCH representation to model the liquidity-adjusted return of individual assets, and liquidity-adjusted VECM/VAR-DCC/ADCC structure to model the liquidity-adjusted variance of portfolio. Both models exhibit improved predictability at high liquidity, which affords a liquidity-adjusted mean-variance (LAMV) framework a clear advantage over its regular mean variance (RMV) counterpart in portfolio performance.
    Date: 2023–06
  4. By: Christophe Boucher; Wassim Le Lann (UO - Université d'Orléans, LEO - Laboratoire d'Économie d'Orleans [2022-...] - UO - Université d'Orléans - UT - Université de Tours - UCA - Université Clermont Auvergne); Stéphane Matton; Sessi Tokpavi
    Abstract: Sustainable investing is growing fast and investors are increasingly integrating environmental, social, and governance (ESG) criteria. However, ESG ratings are derived using heterogeneous methodologies and can be quite divergent across providers, which suggests the need for a formal statistical procedure to evaluate their accuracy. This paper develops a backtesting procedure that evaluates how well these extra-financial metrics help in predicting a company's idiosyncratic risk. Technically, the inference is based on extending the conditional predictive ability test of Giacomini and White (2006) to a panel data setting. We apply our methodology to the forecasting of stock returns idiosyncratic volatility and compare two ESG rating systems from Sustainalytics and Asset4 across three investment universes (Europe, North America, and the Asia-Pacific region). The results show that the null hypothesis of no informational content in ESG ratings is strongly rejected in Europe, whereas results appear mixed in the other regions. Furthermore, the predictive accuracy gains are higher when considering the environmental dimension of ESG ratings. Importantly, applying the test only to firms over which there is a high degree of consensus between the ESG rating agencies leads to higher predictive accuracy gains for all three universes. Beyond providing insights into the accuracy of each of the ESG rating systems, this last result suggests that information gathered from several ESG rating providers should be cross-checked before ESG is integrated into investment processes.
    Keywords: Backtesting, ESG ratings, Idiosyncratic realised volatility, Test of equal predictive power, Panel data, Consensus ESG ratings
    Date: 2023–06–24
  5. By: Mikhail Chernov; Magnus Dahlquist
    Abstract: We review the literature on multi-horizon currency risk premiums. We show how the multi-horizon implications arise from the classic present-value relationship. We further show how these implications manifest themselves in the interaction between bond and currency risk premiums. This link is strengthened by explicitly accounting for stochastic discount factors. Information about currency risk premiums at different horizons presents a wealth of new evidence and challenges for existing models.
    JEL: E43 E52 F31 G12 G15
    Date: 2023–06
  6. By: Deniz Erdemlioglu; Christopher J. Neely; Xiye Yang
    Abstract: We develop a new framework to measure market-wide (systemic) tail risk in the cross-section of high-frequency stock returns. We estimate the time-varying jump intensities of asset prices and introduce a testing approach that identifies multi-asset tail risk based on the release times of scheduled news announcements. Using high-frequency data on individual U.S. stocks and sector-specific ETF portfolios, we find that most of the FOMC announcements create systemic left tail risk, but there is no evidence that macro announcements do so. The magnitude of the tail risk induced by Fed news varies over the business cycle, peaks during the global financial crisis and remains high over different phases of unconventional monetary policy. We use our approach to construct a Fed-induced systemic tail risk (STR) indicator. STR helps explain the pre-FOMC announcement drift and significantly increases variance risk premia, particularly for the meetings without press conferences.
    Keywords: time-varying tail risk; high-frequency data; Federal Open Market Committee (FOMC) news; monetary policy announcements; cojumps; systemic risk; jump intensity
    JEL: C12 C14 C22 C32 C58 G12 G14
    Date: 2023–07–20
  7. By: David Xiao
    Abstract: This article presents a generic framework for modeling the dynamics of forward curves in commodity market as commodity derivatives are typically traded by futures or forwards. We have theoretically demonstrated that commodity prices are driven by multiple components. As such, the model can better capture the forward price and volatility dynamics. Empirical study shows that the model prices are very close to the market prices, indicating prima facie that the model performs quite well.
    Date: 2023–06
  8. By: Zhenhan Huang; Fumihide Tanaka
    Abstract: On-chain data (metrics) of blockchain networks, akin to company fundamentals, provide crucial and comprehensive insights into the networks. Despite their informative nature, on-chain data have not been utilized in reinforcement learning (RL)-based systems for cryptocurrency (crypto) portfolio management (PM). An intriguing subject is the extent to which the utilization of on-chain data can enhance an RL-based system's return performance compared to baselines. Therefore, in this study, we propose CryptoRLPM, a novel RL-based system incorporating on-chain data for end-to-end crypto PM. CryptoRLPM consists of five units, spanning from information comprehension to trading order execution. In CryptoRLPM, the on-chain data are tested and specified for each crypto to solve the issue of ineffectiveness of metrics. Moreover, the scalable nature of CryptoRLPM allows changes in the portfolios' cryptos at any time. Backtesting results on three portfolios indicate that CryptoRLPM outperforms all the baselines in terms of accumulated rate of return (ARR), daily rate of return (DRR), and Sortino ratio (SR). Particularly, when compared to Bitcoin, CryptoRLPM enhances the ARR, DRR, and SR by at least 83.14%, 0.5603%, and 2.1767 respectively.
    Date: 2023–07

This nep-fmk issue is ©2023 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.
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