Special Issue on Statistical and Computational Methods in Finance

In recent decades major developments in computational methods allowed revolutionary changes to take place in the statistical and econometric analysis of financial processes. Evaluating various forecasts and policy scenarios with their implied risk using advanced computational techniques and modern financial models is becoming more and more standard practice. The contents of this special issue reflect the growing interest in this area of research. The journalComputational Statistics and Data Analysis has regular issues on computational and financial econometrics, and statistical methods in finance. Of particular interest are papers in important areas of econometric and financial applications where both computational techniques and numerical methods have a major impact (Amendola et al., 2006; Belsley et al., 2007; Geweke et al., 2007; Gilli and Winker, 2007; Pollock and Proietti, 2007). The goal is to provide sources of information about the most recent developments in computational econometrics that are currently scattered throughout publications in specialized areas. This special issue comprises 18 articles covering a wide range of topics such as dynamic evolution of the volatility of financial returns, model-free measurements of volatility, combination of volatility forecasts, and a transmission mechanism of volatility between markets and operational risk management. Several methodological approaches are proposed based on estimation methods such as MLE, GMM and Bayes and also based on techniques such as copulas, wavelets and geostatistical procedures. Ruiz and Veiga (this issue) focus on leverage effects and long-memory in volatility. They present a new model: the asymmetric long-memory stochastic volatility (A-LMSV) model to cope with leverage effects. Statistical properties of the new model are derived and compared with the properties of the FIEGARCH model. The results are illustrated by fitting both models so as to represent the dynamic evolution of volatilities of daily returns of the S&P500 and DAX indexes. The paper by Creal (this issue) compares alternative filtering and smoothing algorithms for estimating stochastic volatility (SV) models when realized volatility is used as an observable measure of the unobserved true volatility. The author examines how well the particle filter compares with the Kalman filter in estimating the integrated variance under a number of different specifications of the model. Lindström et al. (this issue) introduce a framework based on the state-space formulation of the option valuation model. The performance and computational efficiency of standard and iterated extended Kalman filters are investigated. The tracking time-varying parameters and latent processes such as SV processes have also been studied through a simulation. Omori andWatanabe (this issue) propose an efficient Bayesian method using Monte Carlo Markov Chain (MCMC) for the estimation of asymmetric SV models. They extend their previous results to develop a block sampler method that can take into account asymmetric effects in the returns. The paper by Strickland et al. (this issue) examines the effects of parameterization on the simulation efficiency of MCMC algorithms for non-Gaussian state-space models. Specifically, the authors consider the stochastic conditional duration (SCD) and the SVmodels.They investigate four alternative parameterizations: centred, non-centred in location, non-centred in scale and non-centred in both location and scale. The relations among the simulation efficiency of the MCMC sampler, the magnitudes of the population parameters and the particular parameterizations of the state-space model are examined.