For forecasting volatility of futures returns, the paper proposes an indirect method based on the relationship between futures and the underlying asset for the returns and time-varying volatility. For volatility forecasting, the paper considers the stochastic volatility model with asymmetry and long memory, using high frequency data for the underlying asset. Empirical results for Nikkei 225 fut...

With the success of variable annuities, insurance companies are piling up large risks in terms of both equity and fixed income assets. These risks should be properly modeled as the resulting dynamic hedging strategy is very sensitive to the modeling assumptions. The current literature has been largely focusing on simple variations around Black-Scholes model with basic interest rates term struct...

We construct a risk-neutral stochastic volatility model using no-arbitrage pricing principles. We then study the behavior of the implied volatility of options that are deep in and out of the money according to this model. The motivation of this study is to show the diierence in the asymptotic behavior of the distribution tails between the usual Black-Scholes log-normal distribution and the risk...

1 Nonlinear Causality, with Applications to Liquidity and Stochastic Volatility Abstract The conditional Laplace transform is often easier to use in financial data analysis than the conditional density. This paper characterizes nonlinear causality hypotheses for models based on the conditional Laplace transform and provides interpretations of the linear and quadratic causality in this framework...

Empirical volatility changes in time and exhibits tails, which are heavier than normal. Moreover, empirical volatility has sometimes quite substantial upwards jumps and clusters on high levels. We investigate classical and nonclassical stochastic volatility models with respect to their extreme behavior. We show that classical stochastic volatility models driven by Brownian motion can model heav...

Empirical volatility changes in time and exhibits tails, which are heavier than normal. Moreover, empirical volatility has sometimes quite substantial upwards jumps and clusters on high levels. We investigate classical and nonclassical stochastic volatility models with respect to their extreme behavior. We show that classical stochastic volatility models driven by Brownian motion can model heav...