A1286
Title: Low-rank structured nonparametric prediction of instantaneous volatility
Authors: Sung Hoon Choi - University of Connecticut (United States) [presenting]
Donggyu Kim - UC Riverside (United States)
Abstract: Based on Ito semimartingale models, several studies have proposed methods for forecasting intraday volatility using high-frequency financial data. These approaches typically rely on restrictive parametric assumptions and are often vulnerable to model misspecification. To address this issue, a novel nonparametric prediction method for the future intraday instantaneous volatility process during trading hours is introduced, which leverages both previous days' data and the current day's observed intraday data. The approach imposes an interday-by-intraday matrix representation of the instantaneous volatility, which is decomposed into a low-rank conditional expectation component and a noise matrix. To predict the future conditional expected volatility vector, this low-rank structure is exploited and the structural intraday-volatility prediction (SIP) procedure is proposed. The asymptotic properties of the SIP estimator are established and its effectiveness is demonstrated through an out-of-sample prediction study using real high-frequency trading data.
