題  目：Spot covariance estimation with synchronous high-frequency finance data

日  期：113年2月20日（星期二）

時  間：下午14:10開始

地  點：科學館S433 

摘要Abstract:

Empirical studies have pointed out the importance of considering different temporal variations in correlations between asset prices. Currently, high-frequency profiles sampled asynchronously across different assets have mainly applied for integrated covariance estimation but less so for spot covariance estimation. Based on the seminal works of Malliavin and Mancino [1,2] in conjunction with the principle component analysis approach,  in this talk, we try to propose a novel spot covariance estimation with synchronous high-frequency finance data. We will point out which kind of high-frequency data we are interested in and briefly explain why we apply the Malliavin-Mancino method to these data.
References
[1] P. Malliavin and M. E. Mancino. Fourier series method for measurement of multivariate volatilities. Finance Stoch., 6(1):49–61, 2002.
[2] P. Malliavin and M. E. Mancino. A Fourier transform method for nonparametric estimation of multivariate volatility. Ann. Statist., 37(4):1983–2010, 2009.

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