Evaluation of Multivariate GARCH Models in an Optimal Asset Allocation Framework

This paper analyses plethora of advanced multivariate econometric models, which forecast the mean and variance-covariance of the asset returns in order to create optimal asset allocation models. Most existing studies compare the performance of a limited number of Generalised Autoregressive Conditional Heteroscedasticity (GARCH) models, and they are only based on speci fic optimisation models. In this study, we provide an in-depth knowledge of large asset modelling and optimisation strategies for solving a portfolio selection problem. Speci cally, we use symmetric GARCH models and an asymmetric version of it (GJR-GARCH). Several studies have also tried to examine the effectiveness of using parametric copula in estimating portfolio risk measures but their results have been inconclusive. We are interested in evaluating if copula-GARCH could be an optimal model for assessing the performance of a portfolio. This study, therefore, implemented various copula-GARCH based models using the static and dynamic (DCC) estimation of the correlation. By employing different model speci fications, we are able to explore the empirical applicability of the multivariate GARCH models when estimating large conditional covariance matrices. In constructing the optimal portfolios, we evaluate the minimum variance, mean-variance, maximising Sharpe ratio, mean-CVaR, and maximisation of Sortino ratio. We compare the out-of-sample performance for each of the models based on the risk-adjusted performance for a portfolio with and without short sales, consisting eight stocks and four bond indices of 10 years maturity, in the United States (US), United Kingdom (UK), Germany, Japan, Netherlands, Canada and Hong Kong. Our results suggest that the dynamic models are more capable of delivering better performance gains than the static models. These models reduce portfolio risk and improve the realised return in the out-of-sample period. This paper concludes that by adding copula functions to the model, it does not give a better performance model when compared to the dynamic correlation model

Bilateral intra-industry trade flows and intellectual property rights protections: further evidence from the United Kingdom

This paper investigates the relationship between the United Kingdom's (hereafter referred as UK) bilateral intra-industry trade (IIT) and foreign intellectual property rights (IPRs) protections. The empirical investigation is based on pooled UK data and benefits from the theoretical distinction between horizontal and vertical IIT. It also estimates a gravity equation for international trade using both fixed and random effects models. We then extend the analysis by employing the GMM system for dynamic panel models. The principal findings suggest that the UK's IIT is stimulated when the level of a trading partner's IPRs and its imitative ability are considered jointly. However, when IPRs and imitation abilities are considered separately, their disparate effects are not an important factor in determining UK IIT flows

Forecasting robust value-at-risk estimates: Evidence from UK banks

In this paper, we present a novel approach for forecasting Value-at-Risk (VaR) by combining a Bayesian GARCH(1,1) model with Student's-t distribution for the underlying volatility models, vine copula functions to model dependence, and peaks-over-threshold (POT) method of extreme value theory (EVT) to model the tail behaviour of asset returns. We further propose a new approach for threshold selection in extreme value analysis, which we call a hybrid method. The empirical results and back-testing analysis show that the model captures VaR quite well through periods of calmness and crisis; therefore, it is suitable for use as a measure of risk. Our results also suggest that with a correct implementation of the VaR model, Basel III is not needed

Inference of financial networks using the normalised mutual information rate

In this paper we study data from financial markets using an information theory tool that we call the normalised Mutual Information Rate and show how to use it to infer the underlying network structure of interrelations in foreign currency exchange rates and stock indices of 14 countries world-wide and the European Union. We first present the mathematical method and discuss about its computational aspects, and then apply it to artificial data from chaotic dynamics and to correlated random variates. Next, we apply the method to infer the network structure of the financial data. Particularly, we study and reveal the interrelations among the various foreign currency exchange rates and stock indices in two separate networks for which we also perform an analysis to identify their structural properties. Our results show that both are small-world networks sharing similar properties but also having distinct differences in terms of assortativity. Finally, the consistent relationships depicted among the 15 economies are further supported by a discussion from the economics view point

Refining value-at-risk estimates using a Bayesian Markov-switching GJR-GARCH copula-EVT model

In this paper, we propose a model for forecasting Value-at-Risk (VaR) using a Bayesian Markov-switching GJR-GARCH(1,1) model with skewed Student’s-t innovation, copula functions and extreme value theory. A Bayesian Markov-switching GJR-GARCH(1,1) model that identifies non-constant volatility over time and allows the GARCH parameters to vary over time following a Markov process, is combined with copula functions and EVT to formulate the Bayesian Markov-switching GJR-GARCH(1,1) copula-EVT VaR model, which is then used to forecast the level of risk on financial asset returns. We further propose a new method for threshold selection in EVT analysis, which we term the hybrid method. Empirical and back-testing results show that the proposed VaR models capture VaR reasonably well in periods of calm and in periods of crisis

Parameter estimates for two-state MS-GJR-GARCH(1,1) model with skewed Student’s-<i>t</i> distribution.

<p>Parameter estimates for two-state MS-GJR-GARCH(1,1) model with skewed Student’s-<i>t</i> distribution.</p