nep-fmk New Economics Papers
on Financial Markets
Issue of 2021‒12‒13
nine papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Credit Portfolio Convergence in U.S. Banks since the COVID-19 Shock By Andrew Hawley; Ke Wang
  2. The Impact of Impact Investing By Berk, Jonathan B.; van Binsbergen, Jules H.
  3. Hedging cryptocurrency options By Matic, Jovanka; Packham, Natalie; Härdle, Wolfgang
  4. Mean-Variance-VaR portfolios: MIQP formulation and performance analysis By Francesco Cesarone; Manuel L Martino; Fabio Tardella
  5. FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance By Xiao-Yang Liu; Hongyang Yang; Jiechao Gao; Christina Dan Wang
  6. A Universal End-to-End Approach to Portfolio Optimization via Deep Learning By Chao Zhang; Zihao Zhang; Mihai Cucuringu; Stefan Zohren
  7. The Equilibrium Value of Bitcoin By Radwanski, Juliusz
  8. Information dynamics of price and liquidity around the 2017 Bitcoin markets crash By Vaiva Vasiliauskaite; Fabrizio Lillo; Nino Antulov-Fantulin
  9. Portfolio advice before modern portfolio theory : the belle epoque for french analyst Alfred Neymarck By Cécile Edlinger; Maxime Merli; Antoine Parent

  1. By: Andrew Hawley; Ke Wang
    Abstract: The COVID-19 pandemic has materially affected U.S. consumer behavior and business operations in many important aspects. This note focuses on the changes in banks’ balance sheets and demonstrates how we could apply a novel measure of portfolio similarity to balance sheet data and assess the drivers of similarity change along the path of the pandemic.
    Date: 2021–11–26
  2. By: Berk, Jonathan B. (Stanford University and NBER); van Binsbergen, Jules H. (University of Pennsylvania and NBER)
    Abstract: We evaluate the quantitative impact of ESG divestitures. For divestitures to have impact they must change the cost of capital of affected firms. We derive a simple expression for the change in the cost of capital as a function of three inputs: (1) the fraction of socially conscious capital, (2) the fraction of targeted firms in the economy and (3) the correlation between the targeted firms and the rest of the stock market. Given the current state of ESG investment we find that the impact on the cost of capital is too small to meaningfully affect real investment decisions. We empirically corroborate these small estimates by studying firm changes in ESG status. When firms are either included or excluded from the leading socially conscious US index (FTSE USA 4Good) we find no detectable effect on the cost of capital. We conclude that current ESG divesture strategies have had little impact and will likely have little impact in the future. Our results suggest that to have impact, instead of divesting, socially conscious investors should invest and exercise their rights of control to change corporate policy.
    Date: 2021–10
  3. By: Matic, Jovanka; Packham, Natalie; Härdle, Wolfgang
    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, which in turn are used 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 Lévy 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
  4. By: Francesco Cesarone; Manuel L Martino; Fabio Tardella
    Abstract: Value-at-Risk is one of the most popular risk management tools in the financial industry. Over the past 20 years several attempts to include VaR in the portfolio selection process have been proposed. However, using VaR as a risk measure in portfolio optimization models leads to problems that are computationally hard to solve. In view of this, few practical applications of VaR in portfolio selection have appeared in the literature up to now. In this paper, we propose to add the VaR criterion to the classical Mean-Variance approach in order to better address the typical regulatory constraints of the financial industry. We thus obtain a portfolio selection model characterized by three criteria: expected return, variance, and VaR at a specified confidence level. The resulting optimization problem consists in minimizing variance with parametric constraints on the levels of expected return and VaR. This model can be formulated as a Mixed-Integer Quadratic Programming (MIQP) problem. An extensive empirical analysis on seven real-world datasets demonstrates the practical applicability of the proposed approach. Furthermore, the out-of-sample performance of the optimal Mean-Variance-VaR portfolios seems to be generally better than that of the optimal Mean-Variance and Mean-VaR portfolios.
    Date: 2021–11
  5. By: Xiao-Yang Liu; Hongyang Yang; Jiechao Gao; Christina Dan Wang
    Abstract: Deep reinforcement learning (DRL) has been envisioned to have a competitive edge in quantitative finance. However, there is a steep development curve for quantitative traders to obtain an agent that automatically positions to win in the market, namely \textit{to decide where to trade, at what price} and \textit{what quantity}, due to the error-prone programming and arduous debugging. In this paper, we present the first open-source framework \textit{FinRL} as a full pipeline to help quantitative traders overcome the steep learning curve. FinRL is featured with simplicity, applicability and extensibility under the key principles, \textit{full-stack framework, customization, reproducibility} and \textit{hands-on tutoring}. Embodied as a three-layer architecture with modular structures, FinRL implements fine-tuned state-of-the-art DRL algorithms and common reward functions, while alleviating the debugging workloads. Thus, we help users pipeline the strategy design at a high turnover rate. At multiple levels of time granularity, FinRL simulates various markets as training environments using historical data and live trading APIs. Being highly extensible, FinRL reserves a set of user-import interfaces and incorporates trading constraints such as market friction, market liquidity and investor's risk-aversion. Moreover, serving as practitioners' stepping stones, typical trading tasks are provided as step-by-step tutorials, e.g., stock trading, portfolio allocation, cryptocurrency trading, etc.
    Date: 2021–11
  6. By: Chao Zhang; Zihao Zhang; Mihai Cucuringu; Stefan Zohren
    Abstract: We propose a universal end-to-end framework for portfolio optimization where asset distributions are directly obtained. The designed framework circumvents the traditional forecasting step and avoids the estimation of the covariance matrix, lifting the bottleneck for generalizing to a large amount of instruments. Our framework has the flexibility of optimizing various objective functions including Sharpe ratio, mean-variance trade-off etc. Further, we allow for short selling and study several constraints attached to objective functions. In particular, we consider cardinality, maximum position for individual instrument and leverage. These constraints are formulated into objective functions by utilizing several neural layers and gradient ascent can be adopted for optimization. To ensure the robustness of our framework, we test our methods on two datasets. Firstly, we look at a synthetic dataset where we demonstrate that weights obtained from our end-to-end approach are better than classical predictive methods. Secondly, we apply our framework on a real-life dataset with historical observations of hundreds of instruments with a testing period of more than 20 years.
    Date: 2021–11
  7. By: Radwanski, Juliusz
    Abstract: Can the value of a cryptocurrency be uniquely determined by the fundamentals, such as the rule for money growth implicit in the design of the protocol? To answer this question, we construct a recursive asset-pricing model for a single fiat cryptocurrency, similar to actual Bitcoin. We think of our model as an ideal laboratory, in which equilibria correspond to model solutions that can generate actual data. Our approach stresses the role of the value function as an object of rational choice and hence rests on solid micro-foundations. By imposing enough economically motivated restrictions on that choice, we are able to pin down unique equilibrium and hence demonstrate that the value of our cryptocurrency is immune to self-fulfilling expectations. This result depends only on the design of the cryptocurrency protocol.
    Keywords: Bitcoin, cryptocurrency, equilibrium, expectations, money, sunspots.
    JEL: E40 E50 G12
    Date: 2021–11–18
  8. By: Vaiva Vasiliauskaite; Fabrizio Lillo; Nino Antulov-Fantulin
    Abstract: We study the information dynamics between the largest Bitcoin exchange markets during the bubble in 2017-2018. By analysing high-frequency market-microstructure observables with different information theoretic measures for dynamical systems, we find temporal changes in information sharing across markets. In particular, we study the time-varying components of predictability, memory, and synchronous coupling, measured by transfer entropy, active information storage, and multi-information. By comparing these empirical findings with several models we argue that some results could relate to intra-market and inter-market regime shifts, and changes in direction of information flow between different market observables.
    Date: 2021–11
  9. By: Cécile Edlinger (BETA - Bureau d'Économie Théorique et Appliquée - UNISTRA - Université de Strasbourg - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Maxime Merli (UNISTRA - Université de Strasbourg); Antoine Parent (OFCE - Observatoire français des conjonctures économiques - Sciences Po - Sciences Po)
    Abstract: In this article, we propose an original analysis of advice given by financial analysts prior to WW1. Our article focuses on the writings of A. Neymarck, one of the most popular French analysts in the early 20th Century. The creation of portfolios from a new database composed of the monthly returns of all the security types listed on the official Paris Stock Exchange from 1903 to 1912 has provided results demonstrating that Neymarck correctly identified the risk in a number of sectors. The performances of these portfolios, which were built according to Neymarck's guidelines, confirm Neymarck's ranking in terms of both risk and return: the richer the investor, the riskier and the more profitable his portfolio was seen to be. Finally, the Modern Portfolio Theory enables us to pinpoint the few imperfections in Neymarck's advice, which globally appears to be driven by reliable financial analysis.
    Keywords: Portfolio advice,Diversification before WW1,Financial markets prior WW1
    Date: 2021

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