TY - JOUR
T1 - Parameter instability, stochastic volatility and estimation based on simulated likelihood
T2 - Evidence from the crude oil market
AU - Nonejad, Nima
PY - 2017/2/1
Y1 - 2017/2/1
N2 - Stochastic volatility models with fixed parameters can be too restrictive for time-series analysis due to instability in the parameters that govern conditional volatility dynamics. We incorporate time-variation in the model parameters for the plain stochastic volatility model as well its extensions with: Leverage, volatility feedback effects and heavy-tailed distributed innovations. With regards to estimation, we rely on one recently discovered result, namely, that when an unbiasedly simulated estimated likelihood (available for example through a particle filter) is used inside a Metropolis-Hastings routine then the estimation error makes no difference to the equilibrium distribution of the algorithm, the posterior distribution. This in turn provides an off-the-shelf technique to estimate complex models. We examine the performance of this technique on simulated and crude oil returns from 1987 to 2016. We find that (i): There is clear evidence of time-variation in the model parameters, (ii): Time-varying parameter volatility models with leverage/Student's t-distributed innovations perform best, (iii): The timing of parameter changes align very well with events such as market turmoils and financial crises.
AB - Stochastic volatility models with fixed parameters can be too restrictive for time-series analysis due to instability in the parameters that govern conditional volatility dynamics. We incorporate time-variation in the model parameters for the plain stochastic volatility model as well its extensions with: Leverage, volatility feedback effects and heavy-tailed distributed innovations. With regards to estimation, we rely on one recently discovered result, namely, that when an unbiasedly simulated estimated likelihood (available for example through a particle filter) is used inside a Metropolis-Hastings routine then the estimation error makes no difference to the equilibrium distribution of the algorithm, the posterior distribution. This in turn provides an off-the-shelf technique to estimate complex models. We examine the performance of this technique on simulated and crude oil returns from 1987 to 2016. We find that (i): There is clear evidence of time-variation in the model parameters, (ii): Time-varying parameter volatility models with leverage/Student's t-distributed innovations perform best, (iii): The timing of parameter changes align very well with events such as market turmoils and financial crises.
KW - Bayes
KW - Crude oil
KW - Metropolis-Hastings
KW - Parameter instability
KW - Particle filtering
UR - http://www.scopus.com/inward/record.url?scp=85007361582&partnerID=8YFLogxK
U2 - 10.1016/j.econmod.2016.11.003
DO - 10.1016/j.econmod.2016.11.003
M3 - Journal article
AN - SCOPUS:85007361582
SN - 0264-9993
VL - 61
SP - 388
EP - 408
JO - Economic Modelling
JF - Economic Modelling
ER -