In this paper, we introduce a new approach for volatility modeling in discrete and continuous time. We follow the … stochastic volatility literature by assuming that the variance is a function of a state variable. However, instead of assuming …

generalizes the Hull-White stochastic volatility formula. Using this generalized formula in an ad-hoc fashion to extract two … implicit parameters and forecast next day S&P 500 option prices, we obtain similar pricing errors than with implied volatility …

show that stochastic volatility factor models with possibly cross-correlated disturbances cannot be identified from returns … factors volatility are maintained. Second, we provide an alternative way to maintain identifying restrictions through either …, identification is obtained with conditional moment restrictions which pave the way for instrumental variables estimation and …

Stochastic volatility models, aka SVOL, are more difficult to estimate than standard time-varying volatility models … volatility forecasts especially around crucial periods of high volatility. We extend the basic SVOL needs to allow for the … model diagnostics, such as the identification of outliers for stochastic volatility models or the assessment of the …

interpreted as the level of the spread, and the second factor is the volatility of the spread. The riskless interest rate is … allows us to model the volatility of corporate credit spreads as stochastic, and also allows us to capture higher moments of …

in a conditionally log-normal setting. Extensions to mixtures of log-normals lead to stochastic volatility models …, including models with leverage effect. We characterize implications of such models for volatility smiles and show that they are … fully similar to the ones derived from continuous-time stochastic volatility models. We then review usual continuous …

Discrete time stochastic volatility models (hereafter SVOL) are noticeably harder to estimate than the successful ARCH …(volatilities/ parameters) and p(parameters). This hierarchy provides a natural environment for the construction of stochastic volatility models …, therefore, provides a general perspective on specification and implementation of stochastic volatility models. We apply various …

One of the early examples of stochastic volatility models is Clark [1973]. He suggested that asset price movements …) generally introduces stochastic volatility in the process of the state variable when recorded in calendar time. The paper …

In this paper, we consider temporal aggregation of volatility models. We introduce a semiparametric class of volatility … models termed square-root stochastic autoregressive volatility (SR-SARV) and characterized by an autoregressive dynamic of … stochastic volatility models are characterized by observable multiperiod conditional moment restrictions. The SR-SARV class is a …

This paper examines the valuation of European- and American-style volatility options based on a general equilibrium … stochastic volatility framework. Properties of the optimal exercise region and of the option price are provided when volatility … the MRLP (mean-reverting in the log) volatility model which has received considerable empirical support. In this context …