An Intertemporal CAPM with Stochastic Volatility

Campbell, John Y.; Giglio, Stefano; Polk, Christopher; Turley, Robert

doi:10.3386/w18411

Cited by 104 publications

(10 citation statements)

References 91 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These papers use measures of stock market volatility. More recently, Campbell et al (2012) find that both news about discount rates and stochastic volatility help to explain the value anomaly.…”

Section: A the Settingmentioning

confidence: 99%

Consumption Volatility Risk

Boguth¹,

Kuehn

2013

The Journal of Finance

143

View full text Add to dashboard Cite

Empirically, the conditional volatility of aggregate consumption growth varies over time. While many papers test the consumption CAPM based on realized consumption growth, little is known about how the time-variation of consumption growth volatility affects asset prices. We show that in a model where (i) the agent has recursive preferences, (ii) the conditional first and second moments of consumption growth follow a Markov chain and (iii) the state of the economy is latent, the perception about conditional moments of consumption growth affect excess returns. In the data, we find that the perceived consumption volatility is a priced source of risk and exposure to it strongly negatively predicts future returns in the cross-section. These results suggest that the representative agent has an elasticity of intertemporal substitution greater than unity. In the time-series, changes in beliefs about the volatility state strongly forecast aggregate quarterly excess returns.JEL Classification: G12, G17, E44.

show abstract

Section: A the Settingmentioning

confidence: 99%

Consumption Volatility Risk

Boguth¹,

Kuehn

2013

The Journal of Finance

143

View full text Add to dashboard Cite

show abstract

“…Zhang (2006, 2009) study the cross-sectional variation in risk premia and idiosyncratic volatility and find a significant positive relationship between the two. Campbell, Giglio, Polk, and Turley (2018) include stochastic volatility in an intertemporal CAPM framework and conclude that volatility risk is priced in US stocks and may explain stock return anomalies such as the value premium. Previous empirical studies on market volatility have mainly concentrated on the S&P 500 index, individual US stocks, and currency markets for a variety of reasons including easy access to the relevant data, long time periods, liquidity, coverage in time-strike space (for implied volatility), etc.…”

Section: Volatility Estimationmentioning

confidence: 99%

“…US recession period data are from the National Bureau of Economic Research (NBER). The choice of variables used in constructing the macroeconomic uncertainty series is motivated by previous studies (Bali et al, 2014;Bloom, 2014;Campbell & Shiller, 1988;Campbell et al, 2018). INF_U-US inflation from change in consumer price index.…”

Section: Macroeconomic Uncertainty Indicatorsmentioning

confidence: 99%

Economic uncertainty, trading activity, and commodity futures volatility

Watugala

2019

Journal of Futures Markets

View full text Add to dashboard Cite

This paper investigates the dynamics of commodity futures volatility. I derive the variance decomposition for the futures basis and show unexpected excess returns result from new information about expected future interest rates, convenience yields, and risk premia. Measures of uncertainty in economic conditions have significant predictive power for realized volatility of commodity futures returns, after controlling for lagged volatility, returns, commodity index trading, hedging pressure, and other trading activity, even during the so‐called “index financialization” period. During this period, hedge fund performance predicts volatility in grain commodities, which are affected by the US ethanol mandate.

show abstract

“…A large number of papers have argued that stochastic volatility is important for the understanding of empirical features of asset markets. For recent examples, see Bansal et al, 11 Bollerslev et al, 12 Campbell et al, 13 and Drechsler, 14 etc.…”

Section: Introductionmentioning

confidence: 99%

A stochastic maximum principle for processes driven by G‐Brownian motion and applications to finance

Sun

Zhang

Guo

2017

Optim Control Appl Methods

View full text Add to dashboard Cite

Summary On the basis of the theory of stochastic differential equations on a sublinear expectation space false(normalΩ,scriptH,trueE^false), we develop a stochastic maximum principle for a general stochastic optimal control problem, where the controlled state process is a stochastic differential equation driven by G‐Brownian motion. Furthermore, under some convexity assumptions, we obtain sufficient conditions for the optimality of the maximum in terms of the scriptH‐function. Finally, applications of the stochastic maximum principle to the mean‐variance portfolio selection problem in the financial market with ambiguous volatility is discussed.

show abstract

An Intertemporal CAPM with Stochastic Volatility

Cited by 104 publications

References 91 publications

Consumption Volatility Risk

Consumption Volatility Risk

Economic uncertainty, trading activity, and commodity futures volatility

A stochastic maximum principle for processes driven by G‐Brownian motion and applications to finance

Contact Info

Product

Resources

About