KIER DISCUSSION PAPER SERIES
KIER DISCUSSION PAPER SERIES
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发表时间：
2013
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通讯作者：
Manabu Asai
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作者：
Manabu Asai
最近，人们对替代连续时间多元随机波动率模型的建模和估计越来越感兴趣。我们提出了连续时间分数积分 Wishart 随机波动率 (FIWSV) 过程。我们推导了 FIWSV 模型的条件拉普拉斯变换，以获得矩的闭合形式表达式。我们进行两步过程，即第一步通过对数周期图回归估计分数积分的参数，第二步通过广义矩方法估计剩余参数。该过程的蒙特卡罗结果显示了有限样本中的合理性能。 S&P 500 和 FTSE 100 指数双变量数据的实证结果表明，数据有利于新的 FIWSV 过程，而不是协方差结构的 Wishart 自回归过程的单因素和双因素模型。
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. We derive the conditional Laplace transform of the FIWSV model in order to obtain a closed form expression of moments. We conduct a two-step procedure, namely estimating the parameter of fractional integration via log-periodgram regression 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 shows reasonable performances in finite samples. The empirical results for the bivariate data of the S&P 500 and FTSE 100 indexes show that the data favor the new FIWSV processes rather than one-factor and two-factor models of Wishart autoregressive processes for the covariance structure.