RT Generic
T1 Data cloning for a threshold asymmetric stochastic volatility model
A1 Marín Díazaraque, Juan Miguel
A1 Lopes Moreira Da Veiga, María Helena
A2 Universidad Carlos III de Madrid. Departamento de Estadística, 
AB 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.
SN 2387-0303
YR 2023
FD 2023-02-14
LK https://hdl.handle.net/10016/36569
UL https://hdl.handle.net/10016/36569
LA eng
DS e-Archivo
RD 17 jun. 2024