Volatility Implied by Option Prices: The Case of Takeover Bids

We model the effect of an impending share price jump on the implied standard deviation (ISD) of a company's options, testing the model by investigating its predictive ability for ISDs of companies subject to a takeover bid. Our model fits the observed ISDs well for all but certain deep in-the-money options. However, the model demonstrates that a discontinuity in the relationship between moneyness and the ISD both explains the combination of high and zero ISDs exhibited by these options, and impairs the predictive power of the model at these levels of moneyness.     

The Determinants of Implied Volatility: A Test Using LIFFE Option Prices

This paper presents and tests a model of the volatility of individual companies' stocks, using implied volatilities derived from option prices. The data comes from traded options quoted on the London International Financial Futures Exchange. The model relates equity volatilities to corporate earnings announcements, interest-rate volatility and to four determining variables representing leverage, the degree of fixed-rate debt, asset duration and cash flow inflation indexation. The model predicts that equity volatility is positively related to duration and leverage and negatively related to the degree of inflation indexation and the proportion of fixed-rate debt in the capital structure. Empirical results suggest that duration, the proportion of fixed-rate debt, and leverage are significantly related to implied volatility. Regressions using all four determining variables explain approximately 30% of the cross-sectional variation in volatility. Time series tests confirm an expected drop in volatility shortly after the earnings announcement and in most cases a positive relationship between the volatility of the stock and the volatility of interest rates.     

Implied Risk Aversion Parameter from Option Prices

This paper estimates the representative investor's coefficient of relative risk aversion using option price data. Estimation is carried out using the method of simulated moments. Employing the following assumptions: a) agents have constant proportional risk averse preferences, b) complete markets exist, and c) asset returns are distributed lognormally, an objective function is constructed within the equivalent martingale measure framework. Unlike the case of equity markets, the implied risk aversion parameter from option prices is quite low and stays between zero and one.     

Cash Flow Volatility, Prices and Price Volatility: An Experimental Study

The value of an asset is equal to the present value of its expected future cash flows. It is affected by the magnitude, timing and riskiness, or volatility, of the cash flows. We hypothesize that if the expected values of two assets?? cash flows are equal, the value of the asset with more volatile cash flows will be lower. Furthermore, we examine the impact of the volatility of cash flows on the volatility of prices. We consider a simple experimental environment where subjects trade in an asset which provides dividends from a known probability distribution. The expected value of the dividends is identical in all experimental treatments. The treatments vary with respect to the volatility of dividends. We find that when dividends are more volatile, transaction prices are lower. We also find that the volatility of prices is lower in the treatment with highly volatile dividends. In addition, as expected, trading volume is lower when cash flows are less volatile.     

Option Valuation with Systematic Stochastic Volatility

We use an extension of the equilibrium framework of Rubinstein ( 1976 ) and Brennan ( 1979 ) to derive an option valuation formula when the stock return volatility is both stochastic and systematic. Our formula incorporates a stochastic volatility process as well as a stochastic interest rate process in the valuation of options. If the “mean,” volatility, and “covariance” processes for the stock return and the consumption growth are predictable, our option valuation formula can be written in “preference-free” form. Further, many popular option valuation formulae in the literature can be written as special cases of our general formula.     

Skewness and Kurtosis Implied by Option Prices: A Correction

Corrado and Su (1996) provide skewness and kurtosis adjustment terms for the Black‐Scholes model, using a Gram‐Charlier expansion of the normal density function. In this note we provide a correction to the expression for the skewness coefficient and illustrate the effect on call option prices of the error found.     

A Comparison of Option Prices Under Different Pricing Measures in a Stochastic Volatility Model with Correlation

This paper investigates option prices in an incomplete stochastic volatility model with correlation. In a general setting, we prove an ordering result which says that prices for European options with convex payoffs are decreasing in the market price of volatility risk.As an example, and as our main motivation, we investigate option pricing under the class of q-optimal pricing measures. The q-optimal pricing measure is related to the marginal utility indifference price of an agent with constant relative risk aversion. Using the ordering result, we prove comparison theorems between option prices under the minimal martingale, minimal entropy and variance-optimal pricing measures. If the Sharpe ratio is deterministic, the comparison collapses to the well known result that option prices computed under these three pricing measures are the same.As a concrete example, we specialize to a variant of the Hull-White or Heston model for which the Sharpe ratio is increasing in volatility. For this example we are able to deduce option prices are decreasing in the parameter q. Numerical solution of the pricing pde corroborates the theory and shows the magnitude of the differences in option price due to varying q.JEL Classification: D52, G13     

Index Option Pricing Models with Stochastic Volatility and Stochastic Interest Rates

This paper specifies a multivariate stochasticvolatility (SV) model for the S & P500 index and spot interest rateprocesses. We first estimate the multivariate SV model via theefficient method of moments (EMM) technique based on observations ofunderlying state variables, and then investigate the respective effects of stochastic interest rates, stochastic volatility, and asymmetric S & P500 index returns on option prices. We compute option prices using both reprojected underlying historical volatilities and the implied risk premiumof stochastic volatility to gauge each model's performance through direct comparison with observed market option prices on the index. Our major empirical findings are summarized as follows. First, while allowing for stochastic volatility can reduce the pricing errors and allowing for asymmetric volatility or leverage effect does help to explain the skewness of the volatility smile, allowing for stochastic interest rates has minimal impact on option prices in our case. Second, similar to Melino and Turnbull (1990), our empirical findings strongly suggest the existence of a non-zero risk premium for stochastic volatility of asset returns. Based on the implied volatility risk premium, the SV models can largely reduce the option pricing errors, suggesting the importance of incorporating the information from the options market in pricing options. Finally, both the model diagnostics and option pricing errors in our study suggest that the Gaussian SV model is not sufficientin modeling short-term kurtosis of asset returns, an SV model withfatter-tailed noise or jump component may have better explanatory power.     

Do Stock Prices and Volatility Jump? Reconciling Evidence from Spot and Option Prices

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.     

Approximation and Calibration of Short-Term Implied Volatilities Under Jump-Diffusion Stochastic Volatility

We derive an asymptotic expansion formula for option impliedvolatility under a two-factor jump-diffusion stochastic volatilitymodel when time-to-maturity is small. We further propose a simplecalibration procedure of an arbitrary parametric model to short-termnear-the-money implied volatilities. An important advantageof our approximation is that it is free of the unobserved spotvolatility. Therefore, the model can be calibrated on optiondata pooled across different calendar dates to extract informationfrom the dynamics of the implied volatility smile. An exampleof calibration to a sample of S&P 500 option prices is provided.(JEL G12)     

Understanding Delta-Hedged Option Returns in Stochastic Volatility Environments

In this paper, we provide a novel representation of delta-hedged option returns in a stochastic volatility environment. The representation of delta-hedged option returns provided in this paper consists of two terms: volatility risk premium and parameter estimation risk. In an empirical analysis, we examine delta-hedged option returns based on the result of a historical simulation with the USD-JPY currency option market data from October 2003 to June 2010. We find that the delta-hedged option returns for OTM put options are strongly affected by parameter estimation risk as well as the volatility risk premium, especially in the post-Lehman shock period.     

Ex Ante Stock Market Return Volatility Implied by the OEX Option Premium

Previous empirical evidence suggests that stock return volatility expectations change over time, but the existing models of time-varying variance lack a theoretical structure that is rigorously linked to the efficient markets dividend discount model. This paper develops and tests such a model. The conditional forecast variance of the return on the stock market portfolio is expressed as a linear combination of the adjusted conditional forecast variance of the interest rate and the dividend growth rate. An empirical test using the implied variance of the S&P 100 index option provides evidence that supports the model's predictions.     

Pricing Options under Generalized GARCH and Stochastic Volatility Processes

In this paper, we develop an efficient lattice algorithm to price European and American options under discrete time GARCH processes. We show that this algorithm is easily extended to price options under generalized GARCH processes, with many of the existing stochastic volatility bivariate diffusion models appearing as limiting cases. We establish one unifying algorithm that can price options under almost all existing GARCH specifications as well as under a large family of bivariate diffusions in which volatility follows its own, perhaps correlated, process.     

Stock Splits,Volatility Increases,and Implied Volatilities

A test of the efficiency of the Chicago Board Options Exchange, relative to post-split increases in the volatility of common stocks, is presented. The Black-Scholes and Roll option pricing formulas are used to examine the behavior of implied standard deviations (ISDs) around split announcement and ex-dates. Comparisons with a control group of stocks find no relative increase in ISDs of stocks announcing splits. However, a relative increase is detected at the ex-date. Therefore, the joint hypothesis that 1) the Black-Scholes and Roll formulas are true and 2) the CBOE is efficient can be rejected.     

Jump-Diffusion Processes: Volatility Smile Fitting and Numerical Methods for Option Pricing

This paper discusses extensions of the implied diffusion approach of Dupire (1994) to asset processes with Poisson jumps. We show that this extension yields important model improvements, particularly in the dynamics of the implied volatility surface. The paper derives a forward PIDE (PartialIntegro-Differential Equation) and demonstrates how this equationcan be used to fit the model to European option prices. For numerical pricing of general contingent claims, we develop an ADI finite difference method that is shown to be unconditionally stable and, if combined with Fast Fourier Transform methods, computationally efficient. The paper contains several detailed examples fromthe S&P500 market. This revised version was published online in November 2006 with corrections to the Cover Date.     

Stochastic Dominance Bounds on American Option Prices in Markets with Frictions

We derive equilibrium restrictions on the range of the transactionprices of American options on the stock market index and indexfutures. Trading over the lifetime of the options is accountedfor, in contrast to earlier single-period results. The boundson the reservation purchase price of American puts and the reservationwrite price of American calls are tight. We allow the marketto be incomplete and imperfect due to the presence of proportionaltransaction costs in trading the underlying security and dueto bid-ask spreads in option prices. The bounds may be derivedfor any given probability distribution of the return of theunderlying security and admit price jumps and stochastic volatility.We assume that at least some of the traders maximize a time-separable utility function. The bounds are derived by applyingthe weak notion of stochastic dominance and are independentof a trader's particular utility function and initial portfolioposition.