Revisiting linear and lognormal stochastic volatility models
نویسندگان
چکیده
منابع مشابه
Estimating Volatility from ATM Options with Lognormal Stochastic Variance
We propose a non-linear State Space representation to model ATM implied volatilities and to estimate the unobserved stochastic volatility for the underlying asset. We are able to estimate the average volatility risk premia and we can also address the presence of long memory in the unobserved volatility factor. We then applied our methodology to implied volatilities on currency options. These da...
متن کاملDisplaced Lognormal Volatility Skews: Analysis and Applications to Stochastic Volatility Simulations
We analyze the implied volatility skews generated by displaced lognormal diffusions. In particular, we prove the global monotonicity of implied volatility, and an at-the-money bound on the steepness of downward volatility skews, under displaced lognormal dynamics, which therefore cannot reproduce some features observed in equity markets. A variant, the displaced anti-lognormal, overcomes the st...
متن کاملMultivariate Stochastic Volatility with Bayesian Dynamic Linear Models
This paper develops a Bayesian procedure for estimation and forecasting of the volatility of multivariate time series. The foundation of this work is the matrix-variate dynamic linear model, for the volatility of which we adopt a multiplicative stochastic evolution, using Wishart and singular multivariate beta distributions. A diagonal matrix of discount factors is employed in order to discount...
متن کاملEfficient Mean Estimation in Lognormal Linear Models
Lognormal linear models are widely used in applications, and many times it is of interest to predict the response variable at the original scale for a new set of covariate values. In this paper we consider the problem of efficient estimation of the conditional mean of the response variable at the original scale for lognormal linear models. Several existing estimators are reviewed, including the...
متن کاملExtremes of Stochastic Volatility Models
Extreme value theory for a class of stochastic volatility models, in which the logarithm of the conditional variance follows a Gaussian linear process, is developed. A result for the asymptotic tail behavior of the transformed stochastic volatility process is established and used to prove that the suitably normalized extremes converge in distribution to the double exponential (Gumbel) distribut...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Banach Center Publications
سال: 2020
ISSN: 0137-6934,1730-6299
DOI: 10.4064/bc122-10