Tail Risk and Asset Prices

Kelly, Bryan T.; Jiang, Hao

doi:10.1093/rfs/hhu039

Cited by 586 publications

(79 citation statements)

References 77 publications

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“…In the second and third rows, we report one-factor (1F) and Fama and French (1993) three-factor (3F) alphas. In other rows, in addition to the four factors from the Carhart (1997) model, we include (i) the short-and long-term reversal factors from Kenneth French's data library, (ii) the Frazzini and Pedersen (2014) betting-against-beta (BAB) factor, (iii) the Kelly and Jiang (2014) tail risk factor, (iv) the Pástor and Stambaugh (2003) (PS) liquidity factor, (v) the Sadka (2006) (fixed-transitory and variable permanent) systematic liquidity factors, (vi) the Hirshleifer and Jiang (2010) undervaluedminus-overvalued (UMO) factor, (v) the Asness, Frazzini, and Pedersen (2019) quality-minus-junk (QMJ) factor, and (vi) the Stambaugh and Yuan (2017) mispricing factors (SY). Additionally, we run the new Fama and French (2015) five-factor model.…”

Section: Portfolio Sorts and Factor Modelsmentioning

confidence: 99%

The Perception of Dependence, Investment Decisions, and Stock Prices

Ungeheuer¹,

Weber²

2020

The Journal of Finance

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How do investors perceive dependence between stock returns; and how does their perception of dependence affect investments and stock prices? We show experimentally that investors understand differences in dependence, but not in terms of correlation. Participants invest as if applying a simple counting heuristic for the frequency of comovement. They diversify more when the frequency of comovement is lower even if correlation is higher due to dependence in the tails. Building on our experimental findings, we empirically analyze U.S. stock returns. We identify a robust return premium for stocks with high frequencies of comovement with the market return.

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Section: Portfolio Sorts and Factor Modelsmentioning

confidence: 99%

The Perception of Dependence, Investment Decisions, and Stock Prices

Ungeheuer¹,

Weber²

2020

The Journal of Finance

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“…In recent decades a large body of literature has focused on how to determine a. 4 Following Lux and Marchesi (2000) and Kelly and Jiang (2014), we set a to a fixed percentage p of data points, namely 10, 5 and 2.5 percent. Given the ordered time series, we can compute the Hill estimator with the following equation (Lux and Marchesi 2000):…”

Section: Time Series Properties Of Limit Order Book Measuresmentioning

confidence: 99%

Testing Stylized Facts of Bitcoin Limit Order Books

Schnaubelt

Rende

Krauß

2019

JRFM

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The majority of electronic markets worldwide employ limit order books, and the recently emerging exchanges for cryptocurrencies pose no exception. With this work, we empirically analyze whether commonly observed empirical properties from established limit order exchanges transfer to the cryptocurrency domain. Based on the literature, we establish a structured methodological framework to conduct analyses in a systematic and comprehensive way. We then present results from a unique and extensive limit order data set acquired from major cryptocurrency exchanges for the currency pair Bitcoin to US Dollar. We recover many observations from mature markets, such as a symmetry between the average ask and the average bid side of the order book, autocorrelation in returns on the smallest time scales only, volatility clustering and the timing of large trades. We also observe some idiosyncrasies: The distributions of trade size and limit order prices deviate from commonly observed patterns. Also, we find limit order books to be relatively shallow and liquidity costs to be relatively high when compared to established markets.

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“…The Pickands and ML estimators are extremely biased for any q less than about 0.99, but become very unstable as q moves above 0.97. The Hill estimator is upward biased, becoming more so as ξ falls below 0.50, and is unable to detect thin tails, reporting positive ξ when none exists 4 . The…”

Section: Monte Carlo Analysis Of Tail Shape Estimatorsmentioning

confidence: 99%

“…Understanding these "tail risks" has been at the heart of the recent push for better risk measurement and risk management systems. This includes efforts under the 2010 Dodd-Frank Act to better identify sources of systemic risk as well as academic work aimed at pricing tail risk (Kelly and Jiang ([3] [4]). Value at risk (VaR) and the related concept of expected shortfall (ES) have been the primary tools for measuring risk exposure in the financial services industry for over two decades, yet when these measures rely upon empirical frequencies of rare events, they tend to underestimate the likelihood of very rare outcomes.…”

Section: Introductionmentioning

confidence: 99%

Efficient Estimation of Distributional Tail Shape and the Extremal Index with Applications to Risk Management

Sapp¹

2016

JMF

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Abundant evidence indicates that financial asset returns are thicker-tailed than a normal distribution would suggest. The most negative outcomes which carry the potential to wreak financial disaster also tend to be the most rare and may fall outside the scope of empirical observation. The difficulty of modelling these rare but extreme events has been greatly reduced by recent advances in extreme value theory (EVT). The tail shape parameter and the extremal index are the fundamental parameters governing the extreme behavior of the distribution, and the effectiveness of EVT in forecasting depends upon their reliable, accurate estimation. This study provides a comprehensive analysis of the performance of estimators of both key parameters. Five tail shape estimators are examined within a Monte Carlo setting along the dimensions of bias, variability, and probability prediction performance. Five estimators of the extremal index are also examined using Monte Carlo simulation. A recommended best estimator is selected in each case and applied within a Value at Risk context to the Wilshire 5000 index to illustrate its usefulness for risk measurement.

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Tail Risk and Asset Prices

Cited by 586 publications

References 77 publications

The Perception of Dependence, Investment Decisions, and Stock Prices

The Perception of Dependence, Investment Decisions, and Stock Prices

Testing Stylized Facts of Bitcoin Limit Order Books

Efficient Estimation of Distributional Tail Shape and the Extremal Index with Applications to Risk Management

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