Arbeitspapier

Forecasting Co-Volatilities via Factor Models with Asymmetry and Long Memory in Realized Covariance

Modelling covariance structures is known to suffer from the curse of dimensionality. In order to avoid this problem for forecasting, the authors propose a new factor multivariate stochastic volatility (fMSV) model for realized covariance measures that accommodates asymmetry and long memory. Using the basic structure of the fMSV model, the authors extend the dynamic correlation MSV model, the conditional/stochastic Wishart autoregressive models, the matrix-exponential MSV model, and the Cholesky MSV model. Empirical results for 7 financial asset returns for US stock returns indicate that the new fMSV models outperform existing dynamic conditional correlation models for forecasting future covariances. Among the new fMSV models, the Cholesky MSV model with long memory and asymmetry shows stable and better forecasting performance for one-day, five-day and ten-day horizons in the periods before, during and after the global financial crisis.

Language
Englisch

Bibliographic citation
Series: Tinbergen Institute Discussion Paper ; No. 14-037/III

Classification
Wirtschaft
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Forecasting Models; Simulation Methods
Financial Econometrics
Financial Forecasting and Simulation
Subject
Dimension reduction
Factor Model
Multivariate Stochastic Volatility
Leverage Effects
Long Memory
Realized Volatility.

Event
Geistige Schöpfung
(who)
Asai, Manabu
McAleer, Michael
Event
Veröffentlichung
(who)
Tinbergen Institute
(where)
Amsterdam and Rotterdam
(when)
2014

Handle
Last update
10.03.2025, 11:42 AM CET

Data provider

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Object type

  • Arbeitspapier

Associated

  • Asai, Manabu
  • McAleer, Michael
  • Tinbergen Institute

Time of origin

  • 2014

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