In this paper we examine the empirical performance of affine jump diffusion models with stochastic volatility in a time series study of crude oil prices. We compare four different models and estimate them using the Markov Chain Monte Carlo method. The support for a stochastic volatility model...

Volatility long memory is a stylized fact that has been documented for a long time. Existing literature have two ways to model volatility long memory: component volatility models and fractionally integrated volatility models. This paper develops a new fractionally integrated GARCH model, and...

A regime switching model in continuous time is introduced where a variety of jumps are allowed in addition to the diffusive component. The characteristic function of the process is derived in closed form, and is subsequently employed to create the likelihood function. In addition, standard...

The increasing availability of intraday financial data has led to improvements in daily volatility forecasting through long-memory models of realized volatility. This paper demonstrates the merit of the non-parametric Nearest Neighbor (NN) approach for S&P 100 realized variance forecasting. A...

A discrete time model of a financial market is considered. We focus on the study of a guaranteed profit of an investor which arises when the stock price jumps are bounded. The limit distribution of the profit as the model becomes closer to the classical model of the geometric Brownian motion is...

A discrete time model of financial markets is considered. It is assumed that the stock price evolution is described by a homogeneous Markov chain. In the focus of attention is the expected value of the guaranteed profit of the investor that arises when the jumps of the stock price are bounded....

A discrete time model of financial markets is considered. It is assumed that the relative jumps of the risky security price are independent non-identically distributed random variables. In the focus of attention is the expected non-risky profit of the investor that arises when the jumps of the...

It is common practice to identify the number and sources of shocks that move implied volatilities across space and time by applying Principal Components Analysis (PCA) to pooled covariance matrices of changes in implied volatilities. This approach, however, is likely to result in a loss of...

We formulate and analyze an inverse problem using derivatives prices to obtain an implied filtering density on volatility's hidden state. Stochastic volatility is the unobserved state in a hidden Markov model (HMM), and can be tracked using Bayesian filtering. However, derivative data can be...

Equity-Indexed Annuities (EIAs) are deferred annuities which accumulate value over time according to crediting formulas and realized equity index returns. We propose an efficient algorithm to value two popular crediting formulas found in EIAs - Annual Point-to-Point (APP) and Monthly...