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Forecasting Multivariate Volatilities with Exogenous Predictors: An Application to Industry Diversification Strategies

Author

Listed:
  • Jiawen Luo

    (School of Business Administration, South China University of Technology, Guangzhou, China)

  • Oguzhan Cepni

    (Copenhagen Business School, Department of Economics, Porcelænshaven 16A, Frederiksberg DK-2000, Denmark)

  • Riza Demirer

    (Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026-1102, USA)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

Abstract
We propose a procedure to forecast the realized covariance matrix for a given set of assets via spectral decomposition within a multivariate heterogeneous autoregressive (MHAR) framework. Utilizing high-frequency data for the U.S. aggregate and industry indexes and a large set of exogenous predictors that include financial, macroeconomic, sentiment, and climate-based factors, we evaluate the out-of-sample performance of industry portfolios constructed from forecasted realized covariance matrices across various univariate and multivariate forecasting models. While the climate and sentiment-based forecasting models generally yield more accurate forecasts of realized covariance compared to the macroeconomic and financial based models, particularly at the short forecast horizon, we find that the models that include industry-level information, generally yield better economic outcomes, in line with the established evidence of the predictive information captured at the industry level. Our results suggest that the MHAR framework coupled with DRD decomposition that splits the covariance matrix into a diagonal matrix of realized variances and realized correlations, can be utilized in a high-frequency setting to implement diversification and smart beta strategies for various investment horizons; however, the choice of the predictors should be aligned with the target investment horizon.

Suggested Citation

  • Jiawen Luo & Oguzhan Cepni & Riza Demirer & Rangan Gupta, 2022. "Forecasting Multivariate Volatilities with Exogenous Predictors: An Application to Industry Diversification Strategies," Working Papers 202258, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202258
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    References listed on IDEAS

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    Cited by:

    1. Niu, Zibo & Demirer, Riza & Suleman, Muhammad Tahir & Zhang, Hongwei & Zhu, Xuehong, 2024. "Do industries predict stock market volatility? Evidence from machine learning models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 90(C).

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    More about this item

    Keywords

    Volatility forecasting; Multivariate HAR model; Forecast evaluation; Beta forecasting; Economic analysis;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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