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Abstract. Nowadays many researchers use GARCH models to generate
volatility forecasts. However, it is well known that volatility persistence,
as indicated by the sum of the two parameters G1 and A1[1], in GARCH
models is usually too high. Since volatility forecasts in GARCH models
are based on these two parameters, this may lead to poor volatility
forecasts. It has long been argued that this high persistence is due to
the structure changes(e.g. shift of volatility levels) in the volatility processes,
which GARCH models cannot capture. To solve this problem, we
introduce our GARCH model based on Hidden Markov Models(HMMs),
called HMM-GARCH model. By using the concept of hidden states,
HMMs allow for periods with different volatility levels characterized by
the hidden states. Within each state, local GARCH models can be applied
to model conditional volatility. Empirical analysis demonstrates
that our model takes care of the structure changes and hence yields better
volatility forecasts.
1 Introduction
2.HMM-GARCH Model
2.1GARCH Models
2.2 Hidden Markov Models
2.3 HMM-GARCH Model
3.Volatility Forecast Evaluation and Comparison
4 Conclusion
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