Forecasting a Large Dimensional Covariance Matrix of a Portfolio of Different Asset Classes
AbstractIn portfolio and risk management, estimating and forecasting the volatilities and correlations of asset returns plays an important role. Recently, interest in the estimation of the covariance matrix of large dimensional portfolios has increased. Using a portfolio of 63 assets covering stocks, bonds and currencies, this paper aims to examine and compare the predictive power of different popular methods adopted by i) market practitioners (such as the sample covariance, the 250-day moving average, and the exponentially weighted moving average); ii) some sophisticated estimators recently developed in the academic literature (such as the orthogonal GARCH model and the Dynamic Conditional Correlation model); and iii) their combinations. Based on five different criteria, we show that a combined forecast of the 250-day moving average, the exponentially weighted moving average and the orthogonal GARCH model consistently outperforms the other methods in predicting the covariance matrix for both one-quarter and one-year ahead horizons.
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Bibliographic InfoPaper provided by Hong Kong Monetary Authority in its series Working Papers with number 0901.
Length: 32 pages
Date of creation: Jan 2009
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Volatility forecasting; Risk management; Portfolio management; Model evaluation;
Find related papers by JEL classification:
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
- G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-02-14 (All new papers)
- NEP-ECM-2009-02-14 (Econometrics)
- NEP-FOR-2009-02-14 (Forecasting)
- NEP-ORE-2009-02-14 (Operations Research)
- NEP-RMG-2009-02-14 (Risk Management)
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