A fractionally integrated Wishart stochastic volatility model
提出一个连续时间的分数积分Wishart随机波动率模型，推导出条件拉普拉斯变换以得到矩的闭式表达式，并用两步法估计参数。蒙特卡洛模拟显示有限样本性能合理，对S&P 500和FTSE 100指数的实证表明该模型优于单因子和双因子Wishart自回归模型。
There has recently been growing interest in modeling and estimating alternative continuous time multivariate stochastic volatility models. We propose a continuous time fractionally integrated Wishart stochastic volatility (FIWSV) process, and derive the conditional Laplace transform of the FIWSV model in order to obtain a closed form expression of moments. A two-step procedure is used, namely estimating the parameter of fractional integration via the local Whittle estimator in the first step, and estimating the remaining parameters via the generalized method of moments in the second step. Monte Carlo results for the procedure show a reasonable performance in finite samples. The empirical results for the S&P 500 and FTSE 100 indexes show that the data favor the new FIWSV process rather than the one-factor and two-factor models of the Wishart autoregressive process for the covariance structure.