Forecasting Volatility of Stock Indices with HMM-SV Models
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Date
2017
Authors
Nkemnole, E. B
Abass, O
Wulu, J. T
Journal Title
Journal ISSN
Volume Title
Publisher
Ectap
Abstract
The use of volatility models to generate volatility forecasts has given vent to a lot of literature.
However, it is known that volatility persistence, as indicated by the estimated parameter rp , in
Stochastic Volatility (SV) model is typically high. Since future values in SV models are based on
the estimation of the parameters, this may lead to poor volatility forecasts. Furthermore. this
high persistence, as contended by some writers, is due 10 the structure changes (e.g. shift of
volatility levels) in the volatility processes, which SV model cannot capture. This work deals with
the problem by bringing in the SV model based on Hidden Markov Models (HMMs), called
HMM-SV model. Via hidden states, HMMs allow for periods 'with different volatility levels
characterized by the hidden states. Within each state, SV model is applied to model conditional
volatility. Empirical analysis shows that our model, not only takes care of the structure changes
(hence giving better volatility forecasts), but also helps to establish an proficient forecasting
structure for volatility models
Description
Staff Publication
Keywords
Forecasting, Hidden Markov model, , Stochastic Volatility, Stock Exchange
Citation
Nkemnole, E.B, Wulu J.T & Abass, O (2017) Forecasting Volatility of Stock Indices with HMM-SV Models Theoretical and Applied Economics Volume XXIV (2017), No. 2(611), Summer, pp. 45-60