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Volatility Risk (volatility + risk)

Distribution by Scientific Domains
Business, Economics, Finance and Accounting100%

Selected Abstracts

ANALYTICAL COMPARISONS OF OPTION PRICES IN STOCHASTIC VOLATILITY MODELS

MATHEMATICAL FINANCE, Issue 1 2005
Vicky Henderson
This paper gives an ordering on option prices under various well-known martingale measures in an incomplete stochastic volatility model. Our central result is a comparison theorem that proves convex option prices are decreasing in the market price of volatility risk, the parameter governing the choice of pricing measure. The theorem is applied to order option prices under q -optimal pricing measures. In doing so, we correct orderings demonstrated numerically in Heath, Platen, and Schweizer (Mathematical Finance, 11(4), 2001) in the special case of the Heston model. [source]

Stock Returns and Volatility: Pricing the Short-Run and Long-Run Components of Market Risk

THE JOURNAL OF FINANCE, Issue 6 2008
TOBIAS ADRIAN
ABSTRACT We explore the cross-sectional pricing of volatility risk by decomposing equity market volatility into short- and long-run components. Our finding that prices of risk are negative and significant for both volatility components implies that investors pay for insurance against increases in volatility, even if those increases have little persistence. The short-run component captures market skewness risk, which we interpret as a measure of the tightness of financial constraints. The long-run component relates to business cycle risk. Furthermore, a three-factor pricing model with the market return and the two volatility components compares favorably to benchmark models. [source]

The CBOE S&P 500 three-month variance futures

THE JOURNAL OF FUTURES MARKETS, Issue 1 2010
Jin E. Zhang
In this article, we study the market of the Chicago Board Options Exchange S&P 500 three-month variance futures that were listed on May 18, 2004. By using a simple mean-reverting stochastic volatility model for the S&P 500 index, we present a linear relation between the price of fixed time-to-maturity variance futures and the VIX2. The model prediction is supported by empirical tests. We find that a model with a fixed mean-reverting speed of 1.2929 and a daily-calibrated floating long-term mean level has a good fit to the market data between May 18, 2004, and August 17, 2007. The market price of volatility risk estimated from the 30-day realized variance and VIX2 has a mean value of ,19.1184. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:48,70, 2010 [source]

Do Stock Prices and Volatility Jump?

THE JOURNAL OF FINANCE, Issue 3 2004
Option Prices, Reconciling Evidence from Spot
This paper examines the empirical performance of jump diffusion models of stock price dynamics from joint options and stock markets data. The paper introduces a model with discontinuous correlated jumps in stock prices and stock price volatility, and with state-dependent arrival intensity. We discuss how to perform likelihood-based inference based upon joint options/returns data and present estimates of risk premiums for jump and volatility risks. The paper finds that while complex jump specifications add little explanatory power in fitting options data, these models fare better in fitting options and returns data simultaneously. [source]