Modelling financial returns and portfolio construction for the Russian stock market
- 1 January 2014
- journal article
- research article
- Published by Inderscience Publishers in International Journal of Computational Economics and Econometrics
- Vol. 4 (1/2), 32-81
- https://doi.org/10.1504/ijcee.2014.060289
Abstract
In this paper, we consider multivariate models for returns on Russian equities based on normal distribution, t-distribution with scalar degrees of freedom parameter and t-distribution with vector degrees of freedom parameter. Our models capture autocorrelation, volatility clustering, dynamic links among equity returns and their volatilities, as well as the heavy tails of marginal distributions. Multivariate t-distribution with vector degrees of freedom parameter is a recent generalisation of the classic multivariate t-distribution and in the present paper it is applied to Russian financial data for the first time. Using our multivariate models we construct financial portfolios with conditional minimum variance and conditional maximum expectation of return. We compare optimised portfolios according to the risk and benefit of investment and analyse how the results of this comparison depend on the liquidity of equities involved.Keywords
This publication has 9 references indexed in Scilit:
- Efficient estimation of large portfolio loss probabilities in t-copula modelsEuropean Journal of Operational Research, 2010
- Efficient risk simulations for linear asset portfolios in the t-copula modelEuropean Journal of Operational Research, 2010
- The effects of misspecified marginals and copulas on computing the value at risk: A Monte Carlo studyComputational Statistics & Data Analysis, 2009
- Student-tdistribution based VAR-MGARCH: an application of the DCC model on international portfolio risk managementApplied Economics, 2008
- Estimation Methods for the Multivariate t DistributionActa Applicandae Mathematicae, 2008
- Selecting copulas for risk managementJournal of Banking & Finance, 2007
- Portfolio selection using hierarchical Bayesian analysis and MCMC methodsJournal of Banking & Finance, 2006
- Maximum Likelihood Estimation and Inference in Multivariate Conditionally Heteroscedastic Dynamic Regression Models With StudenttInnovationsJournal of Business & Economic Statistics, 2003
- Dynamic Conditional CorrelationJournal of Business & Economic Statistics, 2002