We propose a shrinkage estimator for parameters θ which improves the mean squared error of functions x (θ) over standard choices. When the restricted model estimator is in the class of minimum distance estimators, we project onto the restricted parameter space using a matrix based on the...

The financial market presents non-linearities for the behavior of stock returns for periods of high and low market. This article studies portfolios whose variance-covariance matrices are estimates using a multivariate model with regime change. Investment strategies for portfolios are presented...

In this paper, we propose a general data-driven framework that unifies the valuation and risk measurement of financial derivatives, which is especially useful in markets with thinly-traded derivatives. We first extract the empirical characteristic function from market-observable time series for...

In this paper, we propose a general data-driven framework that unifies the valuation and risk measurement of financial derivatives, which is especially useful in markets with thinly-traded derivatives. We first extract the empirical characteristic function from market-observable time series for...

Recently, several copula-based approaches have been proposed for modeling stationary multivariate time series. All of them are based on vine copulas, and they differ in the choice of the regular vine structure. In this article, we consider a copula autoregressive (COPAR) approach to model the...

Determining multiple assets’ portfolio volatility using the VaR model has proven to have so many pitfalls; once the portfolio assets are more than two, the value at risk tends to become erratic while repeated computations generate different values therefore making the VaR model...

This paper presents a forward looking model for selection of hedge fund investment strategies. Given excess skewness observed in hedge funds' return distributions, we assume that the historical returns have a skew student t distribution. We implement a Bayesian framework to derive the parameters...