Measures of financial volatility exhibit clustering and persistence and can be jointly modeled as the element by element product of a vector of conditionally autoregressive scale factors and a multivariate i.i.d. innovation process (vector Multiplicative Error Model – vMEM). Since similar profiles are shared across measures, a restricted vMEM decomposes the conditional expected volatility into ...

Fluctuations in commodity prices are a major concern to many market participants. This paper uses realized volatility methods to calculate daily volatility and correlation estimates for three grain futures prices (corn, soybean and wheat). The realized volatility estimates exhibit the properties consistent with the stylized facts observed in earlier studies. According to the realized correlatio...

Realized volatility of stock returns is often decomposed into two distinct components that are attributed to continuous price variation and jumps. This paper proposes a tobit multivariate factor model for the jumps coupled with a standard multivariate factor model for the continuous sample path to jointly forecast volatility in three Chinese Mainland stocks. Out of sample forecast analysis show...

This paper provides a simple unified framework for assessing the empirical linkages between returns and realized and implied volatilities. First, we show that whereas the volatility feedback effect as measured by the sign of the correlation between contemporaneous return and realized volatility depends importantly on the underlying structural model parameters, the correlation between return and...

Rapid development in the computer technology has made the financial transaction data visible at an ultimate limit level. The realized volatility, as a proxy for the ”true” volatility, can be constructed using the high frequency data. This paper extends a threshold stochastic volatility specification proposed in So, Li and Lam (2002) by incorporating the high frequency volatility measures. Due t...