Document 6571175

國 立 中 央 大 學
統 計 研 究 所
學 術 演 講
主 講 人：郭美惠 教授（國立中山大學應用數學系）
講
題：Volatility of high frequency trading : estimation and online monitoring
時
間：103 年 10 月 28 日（星期二）上午 11：00 ～ 12：00
地
點：中央大學鴻經館 M605室
茶
會：上午 10：30 ～11：00
地 點：鴻經館 510 室
ABSTRACT
High-frequency trading (HFT) has often proven to be particularly profitable, yet it has
also been the subject of much public debate. For instance, U.S. Securities and Exchange
Commission and the Commodity Futures Trading Commission reported that HFT
strategies may have contributed to subsequent volatility of the 2010 Flash Crash. Volatility
is an important factor of high frequency trading strategy and a crucial measure of market
performance. In this talk, I will discuss volatility estimation and monitoring for high
frequency transaction data. In the first part, we propose a quadratic unbiased estimator of
the integrated volatility for stochastic volatility models with microstructure noise. The
proposed estimator minimizes the finite sample variance in the class of quadratic estimators
based on symmetric Toeplitz matrices. We show the proposed estimator has an asymptotic
mixed normal distribution with convergence rate O p (n −1/4 ) and achieves the maximum
likelihood estimator efficiency for constant volatility case. In the second part, we consider
the problem of monitoring the high frequency volatility. Define the market reaction portion
(MRP) as the ratio of the integrated volatility to the observed squared returns. Time series
models are fitted to the MRP data obtained from 70-minute returns of NYSE tick-to-tick
transaction data. Both retrospective and dynamic online control charts of the MRP data are
established based on the fitted time series models. The McNemar test supports that the
dynamic online control charts have the same power of detecting out of control events as the
retrospective control charts.
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