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Volatility and Tail Risk

Paper Session

Sunday, Jan. 7, 2018 8:00 AM - 10:00 AM

Loews Philadelphia, Regency Ballroom C2
Hosted By: American Finance Association
  • Chair: Nicola Fusari, Johns Hopkins University

Crash Beliefs From Investor Surveys

William Goetzmann
,
Yale University
Dasol Kim
,
U.S. Office of Financial Research
Robert Shiller
,
Yale University

Abstract

Historical data suggest that the base rate for a severe, single-day stock market crash is relatively low. Surveys of individual and institutional investors, conducted regularly over a 26 year period in the United States, show that they assess the probability to be much higher. We examine factors that influence investor responses and test the role of media influence. We find evidence consistent with an availability bias. Adverse market events made salient by financial press are associated with higher subjective crash probabilities. Non-market-related, rare disasters are also associated with higher subjective crash probabilities. Finally, subjective crash probabilities are negatively associated with future flows to equity-based mutual funds.

A Hidden Markov Model of Leverage Dynamics, Tail Risk, and Value-momentum Correlation

Kent Daniel
,
Columbia University
Ravi Jagannathan
,
Northwestern University, NBER, ISB, and SAIF
Soohun Kim
,
Georgia Institute of Technology

Abstract

Momentum strategies exhibit rare but dramatic losses (crashes), which we show are a result of the leverage dynamics of stocks in the momentum portfolio. When the economy is in a hidden turbulent state, associated with a depressed and volatile stock market, the short-side of the momentum portfolio becomes highly levered, and behaves like a call option on the market index portfolio, making momentum crashes more likely. We develop a hidden Markov model of the unobserved turbulent state that affects the joint returns of the momentum strategy and the market index portfolios. We find that the use of a combination of normal and Student's t-distributions for the hidden residuals in the model to construct the likelihood of the realized momentum and market index returns dramatically improves the model's ability to predict crashes. The same variable that forecasts momentum crashes also forecasts the correlation between momentum and value, two of the benchmark investment styles used in performance appraisal of quant portfolio managers. The correlation is negative only when the probability of the economy being in a turbulent state is high. The conditional correlation is zero otherwise, which is two thirds of the time. Half of the negative relationship between value and momentum is due to the leverage dynamics of stocks in the momentum portfolio. The other half is due to a hidden risk factor, likely related to funding liquidity identified in Asness et al. (2013), which emerges only when the economy is more likely to be in the turbulent state.

Credit-Implied Volatility

Gerardo Manzo
,
Two Sigma Investments
Bryan Kelly
,
University of Chicago
Diogo Palhares
,
AQR Capital Management

Abstract

We define and construct a credit-implied volatility (CIV) surface from the firm-by-maturity panel of CDS spreads. We use this framework to organize the behavior of corporate credit markets into three stylized facts. First, CIV exhibits a steep moneyness smirk. Second, the joint dynamics of credit spreads on all firms are captured by three interpretable factors in the CIV surface. Third, the cross section of CDS risk premia is fully explained by exposures to CIV surface shocks. We propose a structural model for joint asset behavior of all firms that is characterized by stochastic volatility and severe, time-varying downside tail risk in aggregate asset growth.

A Theory of Dissimilarity Between Stochastic Discount Factors

Gurdip Bakshi
,
University of Maryland
Xiaohui Gao Bakshi
,
University of Maryland
George Panayotov
,
Hong Kong University of Science and Technology

Abstract

This paper proposes a measure of dissimilarity between stochastic discount factors (SDFs) in
different economies. The SDFs are made comparable using the respective bond prices as the
numeraire. The measure is based on a probability distance metric, is dimensionless, synthesizes
features of the risk-neutral distribution of currency returns, and can be extracted from currency
option prices. Linking theory to data, our empirical implementation reveals a salient geographical
pattern in dissimilarity across 45 pairs of industrialized economies. We compare the dissimilarity
between SDFs derived from several international asset pricing models to the empirical analog,
offering a dimension to gauge models.
Discussant(s)
Asaf Manela
,
Washington University-St. Louis
Tyler Muir
,
University of California-Los Angeles
Kris Jacobs
,
University of Houston
Jaroslav Borovicka
,
New York University
JEL Classifications
  • G1 - General Financial Markets