In this paper, we develop and apply Bayesian inference for an extended NelsonSiegel (1987) term structure model capturing interest rate risk. The so-called Stochastic Volatility Nelson-Siegel (SVNS) model allows for stochastic volatility in the underlying yield factors. We propose a Markov chain Monte Carlo (MCMC) algorithm to efficiently estimate the SVNS model using simulation-based inference...

We price for different affine stochastic volatility models some derivatives that recently appeared in the market. These products are characterized by payoffs depending on both stock and its volatility. We provide closed-form solution for different products and two multivariate Wishartbased stochastic volatility models. The methodology turns out to be independent of the dimension of the problem....

For asset prices that follow stochastic-volatility diffusions, we use asymptotic methods to investigate the behavior of the local volatilities and Black–Scholes volatilities implied by option prices, and to relate this behavior to the parameters of the stochastic volatility process. We also give applications, including risk-premium-based explanations of the biases in some näıve pricing and hedg...

In this paper, we obtain sharp asymptotic formulas with error estimates for the Mellin convolution of functions defined on (0,∞) and use these formulas to characterize the asymptotic behavior of marginal distribution densities of stock price processes in mixed stochastic models. Special examples of mixed models are jump-diffusion models and stochastic volatility models with jumps. We apply our ...

In modern asset price models, stochastic volatility plays a crucial role explaining several stylized facts of returns. Recently, Barndorff-Nielsen and Shephard [4] introduced a class of stochastic volatility models (the so called BNS SV model) based on superposition of Ornstein-Uhlenbeck processes driven by subordinators. The BNS SV model forms a flexible class, which can easily explain heavy-t...

In modern asset price models, stochastic volatility plays a crucial role explaining several stylized facts of returns. Recently, Barndorff-Nielsen and Shephard [4] introduced a class of stochastic volatility models (the so called BNS SV model) based on superposition of Ornstein-Uhlenbeck processes driven by subordinators. The BNS SV model forms a flexible class, which can easily explain heavy-t...

We develop a new approach for pricing European-style contingent claims written on the time T spot price of an underlying asset whose volatility is stochastic. Like most of the stochastic volatility literature, we assume continuous dynamics for the price of the underlying asset. In contrast to most of the stochastic volatility literature, we do not directly model the dynamics of the instantaneou...

In this paper we consider the problem of hedging contingent claims on a stock under transaction costs and stochastic volatility. Extensive research has clearly demonstrated that the volatility of most stocks is not constant over time. As small changes of the volatility can have a major impact on the value of contingent claims, hedging strategies should try to eliminate this volatility risk. We ...