Arbeitspapier

Forecasting Value-at-Risk using Block Structure Multivariate Stochastic Volatility Models

Most multivariate variance or volatility models suffer from a common problem, the “curse of dimensionality”. For this reason, most are fitted under strong parametric restrictions that reduce the interpretation and flexibility of the models. Recently, the literature has focused on multivariate models with milder restrictions, whose purpose is to combine the need for interpretability and efficiency faced by model users with the computational problems that may emerge when the number of assets can be very large. We contribute to this strand of the literature by proposing a block-type parameterization for multivariate stochastic volatility models. The empirical analysis on stock returns on the US market shows that 1% and 5 % Value-at-Risk thresholds based on one-step-ahead forecasts of covariances by the new specification are satisfactory for the period including the Global Financial Crisis.

Language
Englisch

Bibliographic citation
Series: Tinbergen Institute Discussion Paper ; No. 13-073/III

Classification
Wirtschaft
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Model Construction and Estimation
Econometric and Statistical Methods and Methodology: General
Subject
block structures
multivariate stochastic volatility
curse of dimensionality
leverage effects
multi-factors
heavy-tailed distribution
Kapitaleinkommen
Risikomaß
Prognoseverfahren
Volatilität
Stochastischer Prozess
Statistische Verteilung
Schätzung
USA

Event
Geistige Schöpfung
(who)
Asai, Manabu
Caporin, Massimiliano
McAleer, Michael
Event
Veröffentlichung
(who)
Tinbergen Institute
(where)
Amsterdam and Rotterdam
(when)
2013

Handle
Last update
10.03.2025, 11:41 AM CET

Data provider

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Object type

  • Arbeitspapier

Associated

  • Asai, Manabu
  • Caporin, Massimiliano
  • McAleer, Michael
  • Tinbergen Institute

Time of origin

  • 2013

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