[Update: Within four weeks of the original publication of this research report, Risk Magazine reported in its 28th February 2012 issue story titled 'Goodbye VaR? Basel to Consider Other Risk Metrics': "A review of trading book capital rules, due to be launched in March by the Basel Committee on...

We propose an optimisation approach to determine the optimal decay factor in time weighted (BRW) simulation. The backtesting of the BRW simulation involving different decay factors together with a broad range of competing VaR models has been performed on a sample of seven stock indexes and two...

This is the first paper that econometrically estimates the impact of rising Bioenergy production on global CO2 emissions. We apply a structural vector autoregression (SVAR) approach to time series from 1961 to 2009 with annual observation for the world biofuel production and global CO2...

This is the first paper that econometrically estimates the impact of rising Bioenergy production on global CO2 emissions. We apply a structural vector autoregression (SVAR) approach to time series from 1961 to 2009 with annual observation for the world biofuel production and global CO2...

Correct specification of a conditional quantile model implies that a particular conditional moment is equal to zero. We nonparametrically estimate the conditional moment function via series regression and test whether it is identically zero using uniform functional inference. Our approach is...

The implied volatility of a European option as a function of strike price and time to maturity forms a volatility surface. Traders price according to the dynamics of this high dimensional surface. Recent developments that employ semiparametric models approximate the implied volatility surface...

We propose a Bayesian infinite hidden Markov model to estimate time-varying parameters in a vector autoregressive model. The Markov structure allows for heterogeneity over time while accounting for state-persistence. By modelling the transition distribution as a Dirichlet process mixture model,...

In this paper we introduce a non-parametric estimation method for a large Vector Autoregression (VAR) with time-varying parameters. The estimators and their asymptotic distributions are available in closed form. This makes the method computationally efficient and capable of handling information...

This paper proposes methods for estimation and inference in multivariate, multi-quantile models. The theory can simultaneously accommodate models with multiple random variables, multiple confidence levels, and multiple lags of the associated quantiles. The proposed framework can be conveniently...