Volatility Expectations and Returns

Volatility Expectations and Returns
Author: Lars A. Lochstoer
Publisher:
Total Pages: 0
Release: 2022
Genre:
ISBN:

We provide evidence that agents have slow-moving beliefs about stock market volatility that lead to initial underreaction to volatility shocks followed by delayed overreaction. These dynamics are mirrored in the VIX and variance risk premiums which reflect investor expectations about volatility and are also supported in surveys and in firm-level option prices. We embed these expectations into an asset pricing model and find that the model can account for a number of stylized facts about market returns and return volatility which are difficult to reconcile, including a weak, or even negative, risk-return tradeoff.

Forecasting Expected Returns in the Financial Markets

Forecasting Expected Returns in the Financial Markets
Author: Stephen Satchell
Publisher: Elsevier
Total Pages: 299
Release: 2011-04-08
Genre: Business & Economics
ISBN: 0080550673

Forecasting returns is as important as forecasting volatility in multiple areas of finance. This topic, essential to practitioners, is also studied by academics. In this new book, Dr Stephen Satchell brings together a collection of leading thinkers and practitioners from around the world who address this complex problem using the latest quantitative techniques.*Forecasting expected returns is an essential aspect of finance and highly technical *The first collection of papers to present new and developing techniques *International authors present both academic and practitioner perspectives

Expected and Realized Returns on Volatility

Expected and Realized Returns on Volatility
Author: Guanglian Hu
Publisher:
Total Pages: 50
Release: 2020
Genre:
ISBN:

Expected returns on market volatility, which can be obtained from VIX futures in closed form, predict subsequent multi-period realized volatility returns. Expected volatility returns are negative on average, but become more negative after volatility increases. This generates a positive relation with subsequent realized returns on volatility, which are more negative following increases in volatility. Expected volatility returns also predict future index returns, because realized volatility returns are negatively correlated with realized index returns. We show how these results are related to existing results on the predictive power of the market variance risk premium, the slope of the VIX term structure, and the VIX premium. The results are robust to a wide range of variations in the empirical setup.

Volatility

Volatility
Author: Robert A. Jarrow
Publisher:
Total Pages: 472
Release: 1998
Genre: Derivative securities
ISBN:

Written by a number of authors, this text is aimed at market practitioners and applies the latest stochastic volatility research findings to the analysis of stock prices. It includes commentary and analysis based on real-life situations.

The Persistence of Volatility and Stock Market Fluctuations

The Persistence of Volatility and Stock Market Fluctuations
Author: James M. Poterba
Publisher:
Total Pages: 44
Release: 1984
Genre: Stock price forecasting
ISBN:

This paper examines the potential influence of changing volatility in stock market prices on the level of stock market prices. It demonstrates that volatility is only weakly serially correlated, implying that shocks to volatility do not persist. These shocks can therefore have only a small impact on stockmarket prices, since changes in volatility affect expected required rates of return for relatively short intervals. These findings lead us to be skeptical of recent claims that the stock market's poor performance during the 1970's can be explained by volatility-induced increases in risk premia.

Heterogeneous Expectations and Long Range Correlation of the Volatility of Asset Returns

Heterogeneous Expectations and Long Range Correlation of the Volatility of Asset Returns
Author: Jérôme Coulon
Publisher:
Total Pages: 64
Release: 2010
Genre:
ISBN:

Inspired by the recent literature on aggregation theory, we aim at relating the long range correlation of the stocks return volatility to the heterogeneity of the investors' expectations about the level of the future volatility. Based on a semi-parametric model of investors' anticipations, we make the connection between the distributional properties of the heterogeneity parameters and the auto-covariance/auto-correlation functions of the realized volatility. We report different behaviors, or change of convention, whose observation depends on the market phase under consideration. In particular, we report and justify the fact that the volatility exhibits significantly longer memory during the phases of speculative bubble than during the phase of recovery following the collapse of a speculative bubble.

The Cross-section of Volatility and Expected Returns

The Cross-section of Volatility and Expected Returns
Author: Andrew Ang
Publisher:
Total Pages: 55
Release: 2004
Genre: Stocks
ISBN:

"We examine the pricing of aggregate volatility risk in the cross-section of stock returns. Consistent with theory, we find that stocks with high sensitivities to innovations in aggregate volatility have low average returns. In addition, we find that stocks with high idiosyncratic volatility relative to the Fama and French (1993) model have abysmally low average returns. This phenomenon cannot be explained by exposure to aggregate volatility risk. Size, book-to-market, momentum, and liquidity effects cannot account for either the low average returns earned by stocks with high exposure to systematic volatility risk or for the low average returns of stocks with high idiosyncratic volatility"--National Bureau of Economic Research web site.

The Information Content of Implied Volatilities and Model-Free Volatility Expectations

The Information Content of Implied Volatilities and Model-Free Volatility Expectations
Author: Stephen J. Taylor
Publisher:
Total Pages: 64
Release: 2008
Genre:
ISBN:

The volatility information content of stock options for individual firms is measured using option prices for 149 U.S. firms during the period from January 1996 to December 1999. Volatility forecasts defined by historical stock returns, at-the-money (ATM) implied volatilities and model-free (MF) volatility expectations are compared for each firm. The recently developed model-free volatility expectation incorporates information across all strike prices, and it does not require the specification of an option pricing model.Our analysis of ARCH models shows that, for one-day-ahead estimation, historical estimates of conditional variances outperform both the ATM and the MF volatility estimates extracted from option prices for more than one-third of the firms. This result contrasts with the consensus about the informational efficiency of options written on stock indices; several recent studies find that option prices are more informative than daily stock returns when estimating and predicting index volatility. However, for the firms with the most actively traded options, we do find that the option forecasts are nearly always more informative than historical stock returns. When the prediction horizon extends until the expiry date of the options, our regression results show that the option forecasts are more informative than forecasts defined by historical returns for a substantial majority (86%) of the firms. Although the model-free (MF) volatility expectation is theoretically more appealing than alternative volatility estimates and has been demonstrated to be the most accurate predictor of realized volatility by Jiang and Tian (2005) for the Samp;P 500 index, the results for our firms show that the MF expectation only outperforms both the ATM implied volatility and the historical volatility for about one-third of the firms. The firms for which the MF expectation is best are not associated with a relatively high level of trading in away-from-the-money options.