Standard Stochastic Volatility Models VS Moving Average Stochastic Volatility Model

Question

Hi... I am comparing the log-volatility of two SV models with an application to MATLAB. Since I am a rookie in this field, I do not know if I am wrong in interpreting the graph. In my opinion the only thing I can say is that the standard SV model underestimate the volatility in the volatility is small but I am not sure of my graph. Have you ever seen something like that? Am I completely wrong?

Here the references for the models: for the SV-MA model see: Chan, J.C.C. (2013). Moving Average Stochastic Volatility Models with Application to Inflation Forecast, Journal of Econometric, 176 (2), 162-172.

and for the standard model see: Chan, J.C.C. and Hsiao, C.Y.L (2014). Estimation of Stochastic Volatility Models with Heavy Tails and Serial Dependence. In: I. Jeliazkov and X.S. Yang (Eds.),Bayesian Inference in the Social Sciences, 159-180, John Wiley & Sons, New York.

Answer 1

Finally I have found the answer on my own. The problem was related to the trasformation of the dataset. The original code used: ${{y}_{t}}=400*(\log ({{P}_{t}})-\log ({{P}_{t-1}}))$ as dataset. Initially I did not care about multipling by 400 because I thought it was usless. Instead it makes a big difference. Now the two series are completely overlapped, I think it depends on how MATLAB manages the figures in the spreadsheet.