Kiel Working Papers, Kiel Institute for World Economics
No 1532:
Forecasting Volatility under Fractality, Regime-Switching, Long Memory and Student-t Innovations
Thomas Lux and Leonardo Morales-Arias
Abstract: We examine the performance of volatility models that
incorporate features such as long (short) memory, regime-switching and
multifractality along with two competing distributional assumptions of the
error component, i.e. Normal vs Student-t. Our precise contribution is
twofold. First, we introduce a new model to the family of Markov-Switching
Multifractal models of asset returns (MSM), namely, the Markov-Switching
Multifractal model of asset returns with Student-t innovations (MSM-t).
Second, we perform a comprehensive panel forecasting analysis of the MSM
models as well as other competing volatility models of the Generalized
Autoregressive Conditional Heteroskedasticity (GARCH) legacy. Our
cross-sections consist of all-share equity indices, bond indices and real
estate security indices at the country level. Furthermore, we investigate
complementarities between models via combined forecasts. We find that: (i)
Maximum Likelihood (ML) and Generalized Method of Moments (GMM) estimation
are both suitable for MSM-t models, (ii) empirical panel forecasts of MSM-t
models show an improvement over the alternative volatility models in terms
of mean absolute forecast errors and that (iii) forecast combinations
obtained from the different MSM and (FI)GARCH models considered appear to
provide some improvement upon forecasts from single models
Keywords: Multiplicative volatility models, long memory, Student-t innovations, international volatility forecasting; (follow links to similar papers)
JEL-Codes: C20,; G12; (follow links to similar papers)
35 pages, July 2009
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