Publication: Data cloning for a threshold asymmetric stochastic volatility model
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2023-02-14
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Abstract
In this paper, we propose a new asymmetric stochastic volatility model whose asymmetry parameter can change depending on the intensity of the shock and is modeled as a threshold function whose threshold depends on
past returns. We study the model in terms of leverage and propagation
using a new concept that has recently appeared in the literature. We
find that the new model can generate more leverage and propagation than a
well-known asymmetric volatility model. We also propose to estimate the
parameters of the model by cloning data. We compare the estimates in
finite samples of data cloning and a Bayesian approach and find that
data cloning is often more accurate. Data cloning is a general technique
for computing maximum likelihood estimators and their asymptotic
variances using a Markov chain Monte Carlo (MCMC) method. The empirical
application shows that the new model often improves the fit compared to
the benchmark model. Finally, the new proposal together with data
cloning estimation often leads to more accurate 1-day and 10-day
volatility forecasts, especially for return series with high volatility.
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Asymmetric Stochastic Volatility, Data Cloning, Leverage Effect, Propagation, Volatility Forecasting