We propose a new multivariate GARCH model with Dynamic Conditional Correlations that extends previous models by admitting multivariate thresholds in conditional volatilitiesand correlations. The model estimation is feasible in large dimensions and the positive definiteness of the conditional...

We propose a multivariate nonparametric technique for generating reliable short-term historical yield curve scenarios and confidence intervals. The approach is based on a Functional Gradient Descent (FGD) estimation of the conditional mean vector and covariance matrix of a multivariate interest...

This paper develops a continuous-time model of liquidity provision by banks, in which customers can deposit and withdraw their funds strategically ...

This paper finds out that the risk exposure of a trader subject to a VaR limit is always lower than that of an unconstrained trader and that the probability of extreme losses is also lower.

This paper solves the intertemporal investment problem of an investor holding a portfolio of default-free and defaultable bonds.

This paper developes a new methodology to measure conditional dependency between time series each driven by complicated marginal distributions.

Asset-backed securitisation (ABS) is an asset funding technique that involvesthe issuance of structured claims on the cash flow performance of a designatedpool of underlying receivables. Efficient risk management and asset allocation inthis growing segment of fixed income markets requires both...

We used neural-network based modelling to generalize the linear econometric return models and compare their out-of-sample predictive ability in terms of different performance measures under three density specifications.(...)

This paper presents a comprehensive review and comparison of five computational methods for Bayesian model selection, based on MCMC simulations from posterior model parameter distributions. We apply these methods to a well-known and important class of models in financial time series analysis,...

In this paper, we develop the theoretical and empirical properties of a new class of multivariate GARCH models capable of estimating large time-varying covariance matrices, Dynamic Conditional Correlation Multivariate GARCH.(...)