Estimation of Generalized Realized Stochastic Volatility Model: An Application to Calendar Effect of Nikkei 225
扩展了已实现随机波动率模型，加入回归结构、收益与测量误差的相关性及多个已实现测度，用贝叶斯混合抽样法估计，并应用于日经225指数，发现潜在波动率与已实现波动率在周内日历效应上表现不同。
A stochastic volatility model with realized measures of volatility (Realized Stochastic Volatility model, RSV model) is extended. The regression structures, correlations between returns and realized measurement errors, and multiple realized measures are incorporated into the model. A Bayesian estimation method using the Markov chain Monte Carlo method which is called the mixture sampler is proposed for the generalized realized RSV models. The realized volatility models in the previous studies are surveyed and relation them and our models is discussed. The proposed models and estimation methods are applied for the Nikkei 225 daily closing price returns and realized volatility measures. The calendar effects of latent and realized volatilities are estimated separately. Different behaviors are found for them in the day-of-the-week effect., A stochastic volatility model with realized measures of volatility (Realized Stochastic Volatility model, RSV model) is extended. The regression structures, correlations between returns and realized measurement errors, and multiple realized measures are incorporated into the model. A Bayesian estimation method using the Markov chain Monte Carlo method which is called the mixture sampler is proposed for the generalized realized RSV models. The realized volatility models in the previous studies are surveyed and relation them and our models is discussed. The proposed models and estimation methods are applied for the Nikkei 225 daily closing price returns and realized volatility measures. The calendar effects of latent and realized volatilities are estimated separately. Different behaviors are found for them in the day-of-the-week effect.