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on Financial Markets |
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Issue of 2022‒05‒09
eight papers chosen by |
| By: | Keagile Lesame (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Geoffrey Ngene (Stetson School of Business and Economics, Mercer University, Georgia 31207, USA); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Elie Bouri (School of Business, Lebanese American University, Lebanon) |
| Abstract: | This paper investigates whether investors in international Real Estate Investment Trusts (REITs) markets engage in herding behaviour due to the economic uncertainty induced by the COVID-19 pandemic in 2020. Using a comprehensive sample of 27 countries encompassing both developed and emerging markets, the results show consistent evidence of herding formation in international REITs markets based on both static and time-varying estimates. International herding is mainly driven by herding in developed market REITs. Further analysis provides a direct evidence showing that herding in REITs markets during the pandemic resulted from the economic uncertainty brought on by the global health crisis. A quantile-on-quantile regression reveals that higher uncertainty associated with COVID-19 pandemic intensifies herding. |
| Keywords: | International REITs, Herding, COVID-19, Quantile-on-Quantile Regression, Probit Model |
| JEL: | C22 R3 |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:202218&r= |
| By: | Emmanuel Joel Aikins Abakah; Guglielmo Maria Caporale; Luis A. Gil-Alana |
| Abstract: | This paper assesses the impact of US policy responses to the Covid-19 pandemic on various cryptocurrencies and also technology stocks using fractional integration techniques. More precisely, it analyses the behaviour of the percentage returns in the case of nine major coins (Bitcoin - BITC, Stella - STEL, Litecoin - LITE, Ethereum - ETHE, XRP (Ripple), Dash, Monero - MONE, NEM, Tether – TETH) and two technology related stock market indices (the KBW NASDAQ Technology Index – KFTX, and the NASDAQ Artificial Intelligence index - AI) over the period 1 January 2020-5 March 2021. The results suggest that fiscal measures such as debt relief and fiscal policy announcements had positive effects on the series examined during the pandemic, when an increased mortality rate tended instead to drive them down; by contrast, monetary measures and announcements appear to have had very little impact and the Covid-19 containment measures none at all. |
| Keywords: | Covid-19 pandemic, cryptocurrencies, Fintech, artificial intelligence, Covid-19 policies, fractional integration |
| JEL: | C22 C32 G15 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_9624&r= |
| By: | Daniel Dimitrov (University of Amsterdam) |
| Abstract: | This paper examines the optimal allocation of risk across generations whose savings mix is subject to illiquidity in the form of uncertain trading costs. We use a stylised two-period OLG framework, where each generation makes a portfolio allocation decision for retirement, and show that illiquidity reduces the range of transferable shocks between generations and thus lowers the benefits of risk-sharing. Higher illiquidity then may justify higher levels of risk sharing to compensate for the trading friction. We still find that a contingent transfers policy based on a reasonably parametrised savings portfolio with liquid and illiquid assets increased aggregate welfare. |
| Keywords: | intergenerational risk sharing, (il)liquidity, stochastic overlapping generations, funded pension plan |
| JEL: | G11 G23 E21 H55 |
| Date: | 2022–03–30 |
| URL: | http://d.repec.org/n?u=RePEc:tin:wpaper:20220028&r= |
| By: | Magnus Wiese; Phillip Murray |
| Abstract: | We develop a risk-neutral spot and equity option market simulator for a single underlying, under which the joint market process is a martingale. We leverage an efficient low-dimensional representation of the market which preserves no static arbitrage, and employ neural spline flows to simulate samples which are free from conditional drifts and are highly realistic in the sense that among all possible risk-neutral simulators, the obtained risk-neutral simulator is the closest to the historical data with respect to the Kullback-Leibler divergence. Numerical experiments demonstrate the effectiveness and highlight both drift removal and fidelity of the calibrated simulator. |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2202.13996&r= |
| By: | Florentina \c{S}oiman (CASC, CNRS - UMR3571); Guillaume Dumas (CNRS - UMR3571); Sonia Jimenez-Garces (CERAG) |
| Abstract: | Decentralized Finance (DeFi) is a nascent set of financial services, using tokens, smart contracts, and blockchain technology as financial instruments. We investigate four possible drivers of DeFi returns: exposure to cryptocurrency market, the network effect, the investor's attention, and the valuation ratio. As DeFi tokens are distinct from classical cryptocurrencies, we design a new dedicated market index, denoted DeFiX. First, we show that DeFi tokens returns are driven by the investor's attention on technical terms such as "decentralized finance" or "DeFi", and are exposed to their own network variables and cryptocurrency market. We construct a valuation ratio for the DeFi market by dividing the Total Value Locked (TVL) by the Market Capitalization (MC). Our findings do not support the TVL/MC predictive power assumption. Overall, our empirical study shows that the impact of the cryptocurrency market on DeFi returns is stronger than any other considered driver and provides superior explanatory power. |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2204.00251&r= |
| By: | Galvani, Valentina (University of Alberta, Department of Economics); Faychuk, Vita (Gustavus Adolphus College) |
| Abstract: | We explore the existence of a mean-variance core subset of cryptocurrencies that subsumes the risk-reward of the broader market. The analysis considers both the perspective of long-short and long-only investors. The results indicate that most cryptocurrencies are redundant from the standpoint of both types of investors, with the exception of Bitcoin, which consistently improves the Sharpe ratio of even broad cryptocurrency portfolios. We show that the core can be often identified ex-ante as the cryptocurrencies attracting the highest levels of investors’ attention. |
| Keywords: | Sharpe Ratio; Cryptocurrencies; Bitcoin; Short-Selling; Spanning |
| JEL: | G11 G12 G14 G40 |
| Date: | 2022–03–24 |
| URL: | http://d.repec.org/n?u=RePEc:ris:albaec:2022_004&r= |
| By: | Konstantin G\"orgen; Jonas Meirer; Melanie Schienle |
| Abstract: | We study the estimation and prediction of the risk measure Value at Risk for cryptocurrencies. Using Generalized Random Forests (GRF) (Athey et al., 2019) that can be adapted to specifically fit the framework of quantile prediction, we show their superior performance over other established methods such as quantile regression and CAViaR, particularly in unstable times. We investigate the small-sample prediction properties in comparison to standard techniques in a Monte Carlo simulation study. In a comprehensive empirical assessment, we study the performance not only for the major cryptocurrencies but also in the stock market. Generally, we find that GRF outperforms established methods especially in crisis situations. We further identify important predictors during such times and show their influence on forecasting over time. |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2203.08224&r= |
| By: | Charl Maree; Christian W. Omlin |
| Abstract: | The common purpose of applying reinforcement learning (RL) to asset management is the maximization of profit. The extrinsic reward function used to learn an optimal strategy typically does not take into account any other preferences or constraints. We have developed a regularization method that ensures that strategies have global intrinsic affinities, i.e., different personalities may have preferences for certain assets which may change over time. We capitalize on these intrinsic policy affinities to make our RL model inherently interpretable. We demonstrate how RL agents can be trained to orchestrate such individual policies for particular personality profiles and still achieve high returns. |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2204.09218&r= |