The asymmetrical movement between the downward and upward phases of the sample paths of many financial time series has been commonly noted by economists. Since this feature cannot be described by the Autoregressive Integrated Moving-average (ARIMA) model and the Autoregressive Conditional...

The simultaneous switching autoregressive (SSAR) model is a non-linear Markovian time series model, which was originally introduced by Kunitomo and Sato (1996a). This paper gives some conditions for the geometrical ergodicity of the SSAR models and discuss the estimation methods of unknown...

For estimating the integrated volatility by using high frequency data, Kunitomo and Sato (2008, 2011, 2013) have proposed the Separating Information Maximum Likelihood (SIML) method when there are micro-market noises. The SIML estimator has reasonable nite sample properties and asymptotic...

For estimating the integrated volatility and covariance by using high frequency financial data, we propose the Separating Information Maximum Likelihood (SIML) method when there are possibly micro-market noises. The resulting estimator, which is represented as a specific quadratic form of...

For the estimation problem of the realized volatility and hedging coefficient by using high-frequency data with possibly micro-market noise, we use the Separating Information Maximum Likelihood (SIML) method, which was recently developed by Kunitomo and Sato [11–13]. By analyzing the...

For estimating the realized volatility and covariance by using high frequency data, Kunitomo and Sato (2008a, b) have proposed the Separating Information Maximum Likelihood (SIML) method when there are micro-market noises. The SIML estimator has reasonable asymptotic properties; it is consistent...

For estimating the integrated volatility and covariance by using high frequency data, Kunitomo and Sato (2011, 2013) have proposed the Separating Information Maximum Likelihood (SIML) method when there are micro-market noises. The SIML estimator has reasonable nite sample properties and...