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on Financial Markets |
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Issue of 2021‒03‒15
fourteen papers chosen by |
| By: | Jacky Lin; Genevieve C. Selden; John B. Shoven; Clemens Sialm |
| Abstract: | The Dow Jones Industrial Average has historically been the most quoted stock index in the United States. It has several unique features. It uses price weights, it ignores cash dividend payments, and it also treats stock dividends, rights issues, and other corporate actions inconsistently. We show that price indices which use alternative weighting methods and more systematic inclusion criteria perform similarly to the Dow. However, ignoring cash and stock dividends underestimates the long-run returns earned by stock market investors dramatically. If the DJIA had consistently adjusted for dividends and other corporate actions since 1928, the index would have closed at 1,113,047 instead of 28,538 points at the end of 2019. |
| JEL: | G10 |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:28528&r=all |
| By: | Di Muzio, Tim |
| Abstract: | Reflections on leveraging finance against finance. |
| Keywords: | capital as power,finance,hype,stock market |
| JEL: | G P16 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:esprep:229951&r=all |
| By: | Sirio Aramonte; Mohammad Jahan-Parvar; Samuel Rosen; John W. Schindler |
| Abstract: | We propose a method to extract the risk-neutral distribution of firm-specific stock returns using both options and credit default swaps (CDS). Options and CDS provide information about the central part and the left tail of the distribution, respectively. Taken together, but not in isolation, options and CDS span the intermediate part of the distribution, which is driven by exposure to the risk of large but not extreme returns. Through a series of asset-pricing tests, we show that this intermediate-return risk carries a premium, particularly at times of heightened market stress. |
| Keywords: | risk neutral distributions, investor expectations, CDS spreads |
| JEL: | G12 G13 G14 |
| Date: | 2021–01 |
| URL: | http://d.repec.org/n?u=RePEc:bis:biswps:921&r=all |
| By: | Nassim Dehouche |
| Abstract: | A reputation of high volatility accompanies the emergence of Bitcoin as a financial asset. This paper intends to nuance this reputation and clarify our understanding of Bitcoin's volatility. Using daily, weekly, and monthly closing prices and log-returns data going from September 2014 to January 2021, we find that Bitcoin is a prime example of an asset for which the two conceptions of volatility diverge. We show that, historically, Bitcoin allies both high volatility (high Standard Deviation) and high predictability (low Approximate Entropy), relative to Gold and S&P 500. Moreover, using tools from Extreme Value Theory, we analyze the convergence of moments, and the mean excess functions of both the closing prices and the log-returns of the three assets. We find that the closing price of Bitcoin is consistent with a generalized Pareto distribution, when the closing prices of the two other assets (Gold and S&P 500) present thin-tailed distributions. However, returns for all three assets are heavy tailed and second moments (variance, standard deviation) non-convergent. In the case of Bitcoin, lower sampling frequencies (monthly vs weekly, weekly vs daily) drastically reduce the Kurtosis of log-returns and increase the convergence of empirical moments to their true value. The opposite effect is observed for Gold and S&P 500. These properties suggest that Bitcoin's volatility is essentially an intra-day and intra-week phenomenon that is strongly attenuated on a weekly time-scale, and make it an attractive store of value to investors and speculators, but its high standard deviation excludes its use a currency. |
| Date: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2103.00395&r=all |
| By: | Tobias Dieler (University of Bristol - Department of Finance and Accounting); Loriano Mancini (USI Lugano - Institute of Finance; Swiss Finance Institute); Norman Schürhoff (University of Lausanne; Swiss Finance Institute; Centre for Economic Policy Research (CEPR)) |
| Abstract: | Repo markets trade off the efficient allocation of liquidity in the financial sector with resilience to funding shocks. The repo trading and clearing mechanisms are crucial determinants of the allocation-resilience tradeoff. The two common mechanisms, anonymous central-counterparty (CCP) and non-anonymous over-the-counter (OTC) markets, are inefficient and their welfare rankings depend on funding tightness. CCP (OTC) markets inefficiently liquidate high (low) quality assets for large (small) funding shocks. Two innovations to repo market design contribute to maximize welfare: a liquidity-contingent trading mechanism and a two-tiered guarantee fund. |
| Keywords: | repo market, funding run, financial stability, asymmetric information, central clearing, novation, guarantee fund, collateral |
| JEL: | G01 G14 G21 G28 |
| Date: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp2110&r=all |
| By: | M. Avellaneda; T. N. Li; A. Papanicolaou; G. Wang |
| Abstract: | We propose a new approach for trading VIX futures. We assume that the term structure of VIX futures follows a Markov model. The trading strategy selects a multi-tenor position by maximizing the expected utility for a day-ahead horizon given the current shape and level of the VIX futures term structure. Computationally, we model the functional dependence between the VIX futures curves, the VIX futures positions, and the expected utility as a deep neural network with five hidden layers. Out-of-sample backtests of the VIX futures trading strategy suggest that this approach gives rise to reasonable portfolio performance, and to positions in which the investor can be either long or short VIX futures contracts depending on the market environment. |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2103.02016&r=all |
| By: | Charles Smith; Peter Van Tassel |
| Abstract: | Can option traders take a square root? Surprisingly, maybe not. This post shows that VIX futures prices exhibit significant deviations from their option-implied upper bounds—the square root of variance swap forward rates—thus violating the law of one price, a fundamental concept in economics and finance. The deviations widen during periods of market stress and predict the returns of VIX futures. Just as the stock market struggles with multiplication, the equity volatility market appears unable to take a square root at times. |
| Keywords: | variance swaps; VIX futures; term structure; variance risk premium; return predictability |
| JEL: | G1 |
| Date: | 2021–02–01 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fednls:89616&r=all |
| By: | Geoffrey Ducournau |
| Abstract: | We propose in this paper to consider the stock market as a physical system assimilate to a fluid evolving in a macroscopic space subject to a Force that influences its movement over time where this last is arising from the collision between the supply and the demand of Financial agents. In fluid mechanics, this Force also results from the collisions of fluid molecules led by its physical property such as density, viscosity, and surface tension. The purpose of this article is to show that the dynamism of the stock market behavior can be explained qualitatively and quantitatively by considering the supply & demand collision as the result of Financial agents physical properties defined by Stokes Law. The first objective of this article is to show theoretically that fluid mechanics equations can be used to describe stock market physical properties. The second objective based on the knowledge of stock market physical properties is to propose an Econophysics analog of the stock market viscosity and Reynolds number to measure stock market conditions, whether laminar, transitory, or turbulent. The Reynolds Number defined in this way can be applied in research into the study and classification of stock market dynamics phases through for instance the creation of Econophysics analog of Moddy diagram, this last could be seen as a physical way to quantify asset and stock index idiosyncratic risk. The last objective is to present evidence from a computer simulation that the stock market behavior can be a priori, and posteriori explained by physical properties (viscosity & density) quantifiable by fluid mechanics law (Stokes law) and measurable with the stock market Reynolds Number. |
| Date: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2103.00721&r=all |
| By: | Henri Fraisse; Matthias Laporte |
| Abstract: | Taking advantage of granular data we measure the change in bank capital requirement resulting from the implementation of AI techniques to predict corporate defaults. For each of the largest banks operating in France we design an algorithm to build pseudo-internal models of credit risk management for a range of methodologies extensively used in AI (random forest, gradient boosting, ridge regression, deep learning). We compare these models to the traditional model usually in place that basically relies on a combination of logistic regression and expert judgement. The comparison is made along two sets of criterias capturing : the ability to pass compliance tests used by the regulators during on-site missions of model validation (i), and the induced changes in capital requirement (ii). The different models show noticeable differences in their ability to pass the regulatory tests and to lead to a reduction in capital requirement. While displaying a similar ability than the traditional model to pass compliance tests, neural networks provide the strongest incentive for banks to apply AI models for their internal model of credit risk of corporate businesses as they lead in some cases to sizeable reduction in capital requirement.[1] |
| Keywords: | Artificial Intelligence, Credit Risk, Regulatory Requirement |
| JEL: | C4 C55 G21 K35 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:bfr:banfra:809&r=all |
| By: | Charles Smith; Peter Van Tassel |
| Abstract: | Investors can buy volatility hedges on the stock market using variance swaps or VIX futures. One motivation for hedging volatility is its negative relationship with the stock market. When volatility increases, stock returns tend to decline contemporaneously, a result known as the leverage effect. In this post, we measure the cost of volatility hedging by decomposing the prices of variance swaps and VIX futures into volatility forecasts and estimates of expected returns (“equity volatility term premia”) from January 1996 to June 2020. |
| Keywords: | variance swaps; VIX futures; term structure; variance risk premium; return predictability |
| JEL: | G1 |
| Date: | 2021–02–03 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fednls:89758&r=all |
| By: | Spilak, Bruno; Härdle, Wolfgang Karl |
| Abstract: | Tail risk protection is in the focus of the financial industry and requires solid mathematical and statistical tools, especially when a trading strategy is derived. Recent hype driven by machine learning (ML) mechanisms has raised the necessity to display and understand the functionality of ML tools. In this paper, we present a dynamic tail risk protection strategy that targets a maximum predefined level of risk measured by Value-At-Risk while controlling for participation in bull market regimes. We propose different weak classifiers, parametric and non-parametric, that estimate the exceedance probability of the risk level from which we derive trading signals in order to hedge tail events. We then compare the different approaches both with statistical and trading strategy performance, finally we propose an ensemble classifier that produces a meta tail risk protection strategy improving both generalization and trading performance. |
| JEL: | C00 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:irtgdp:2020015&r=all |
| By: | Philippe van der Beck (Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute); Coralie Jaunin (University of Lausanne - School of Economics and Business Administration (HEC-Lausanne); Swiss Finance Institute) |
| Abstract: | Retail trading activity has soared during the COVID-19 pandemic. This paper quantifies the impact of the retail investment boom on the US stock market within a structural model. Using account holdings data from the online trading platform “Robinhood Markets Inc.” and 13F filings, we estimate retail and institutional demand curves and derive aggregate pricing implications via market clearing. The inelastic nature of institutional demand allows Robinhood investors to have a substantial effect on stock returns during the COVID-19 pandemic despite their negligible wealth share. We find that Robinhood traders account for over 7% of the cross-sectional variation in stock returns during the second quarter of 2020. We furthermore show that without the surge in retail trading activity the aggregate market capitalization of the smallest quintile of US stocks would have been over 30% lower. Lastly, Robinhood traders’ are able to affect the price of some large individual companies that are being held primarily by passive institutional investors. |
| Keywords: | Retail investors, Demand system, Institutional investors, COVID-19, Robinhood |
| JEL: | G11 G14 G12 G23 |
| Date: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp2112&r=all |
| By: | Jérémy Fouliard; Michael Howell; Hélène Rey |
| Abstract: | Financial crises cause economic, social and political havoc. Macroprudential policies are gaining traction but are still severely under-researched compared to monetary policy and fiscal policy. We use the general framework of sequential predictions also called online machine learning to forecast crises out-of-sample. Our methodology is based on model averaging and is "meta-statistic" since we can incorporate any predictive model of crises in our set of experts and test its ability to add information. We are able to predict systemic financial crises twelve quarters ahead out-of-sample with high signal-to-noise ratio in most cases. We analyse which experts provide the most information for our predictions at each point in time and for each country, allowing us to gain some insights into economic mechanisms underlying the building of risk in economies. |
| JEL: | E37 E44 G01 |
| Date: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:bis:biswps:926&r=all |
| By: | Ateeb Akhter Shah Syed; Kaneez Fatima |
| Abstract: | This paper examines the impact of coronavirus (COVID-19) on stock market volatility (SMV) in Pakistan by controlling the effect of exchange rate, interest rate and government/central bank interventions to combat the pandemic. We used the vector autoregressive (VAR) model over a sample period ranging from February 25, 2020 to December 7, 2020. We find that a shock to total daily coronavirus cases in Pakistan lead to a significant increase in SMV. This result is aligned with a vast literature on pandemics and investors uncertainty and remains robust to several robustness checks applied in our analysis. |
| Date: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2103.03219&r=all |