Copula functions have become standard practice for pricing multi-name credit derivatives. Marginal default distributions are often chosen by using a simple deterministic intensity function. It is wellknown that this approach only generates default time correlation and, apart from jumps due to default events, does not generate correlation between the conditional default intensities, or the condi...

This paper examines the intertemporal relation between expected return and risk for 30 stocks in the Dow Jones Industrial Average. The mean-reverting dynamic conditional correlation model of Engle (2002) is used to estimate a stock’s conditional covariance with the market and test whether the conditional covariance predicts time-variation in the stock’s expected return. The risk-aversion coeffi...

Two-level dynamic branch predictors try to predict the outcomes of conditional branches using both a table of state counters associated with specific branch instructions and a buffer of recent branch outcomes to correlate the counters with specific branch histories. However, there is always a question of how much correlation to use, and some programs benefit from higher levels of correlation th...

In this study we have analysed wind and wave time series data resulting from hourly measurements on the sea surface in Bushehr, the northern part of the Persian Gulf, from 15 July to 4 August 2000. Wind speed (U10) ranged from 0.34 to 10.38 m/s as alternating sea and land breezes. The lowest wind speed occurs at about midnight and the highest at around noon. The calculated autocorrelation of wi...

A key problem in financial econometrics is the modeling, estimation and forecasting of conditional return volatility and correlation. Having accurate forecasting models for conditional volatility and correlation is important for accurate derivatives pricing, risk management and asset allocation decisions. It is well known that conditional volatility and correlation are highly predictable. An in...