This paper proposes a new approach to modeling volatility changes and clustering, we use a parsimonious high-order markov chain which allows for duration dependence.

In this paper, we apply a collection of parametric (Normal, Normal GARCH, Student GARCH, RiskMetrics and high-frequency duration models) and non-parametric (empirical quantile, extreme distributions models) Value-at-Risk (VaR) techniques to intraday data for three stocks traded on the New York...

We investigate in this paper the attitudes towards risk of bettors in British horse races. The model we use allows us to go beyond the expected utility framework and to explore various alternative proposals by estimating a multinomial model on a 34443-race dataset. We find that rank-dependent...

In this paper we model Value-at-Risk (VaR) for daily stock index returns using a collection of parametric models of the ARCH family based on the skewed Student distribution. We show that models that rely on a symmetric density distribution for the error term underperform with respect to skewed...

This paper presents a useful theoretical and empirical modelling for a probabilistic evaluation of the bank risk states. Bank riskiness is related to a stochastic recursive profit function from which different positions arise. Provided that the bank's decision maker objective is to maximise a...

This study uses GARCH modelling to estimate and forecast conditional variances and covariances of returns calculated from a set of financial market series: twelve markka exchange rates, twelve corresponding short-term euro interest rates and the Finnish short-term interest rate, the Finnish...