QFRM CUHK
Statistics Risk Management Science
STAT CUHK
Statistics Quantitative Finance and Risk Management Science
Risk Management Science Science Faculty
CUHK STAT
STATistics
Quantitative Finance and Risk Management Science
Past Events
Topic: Modelling and Forecasting High-frequency Volatility with Vine Copulas
Date: 12/11/2019
Time: 14:30 p.m. - 15:30 p.m.
Venue: Lady Shaw Building, Room LT6
Category: Seminar
Speaker: Dr. Martin MAGRIS
PDF: 20191112_Martin.pdf
Details:

Abstract

The heterogeneous autoregressive (HAR) model is extended by modeling the joint distribution of the four partial-volatility terms therein involved. Namely, today's, yesterday's, last week's and last month's volatility components. The joint distribution relies on a (C-) Vine copula construction, allowing to conveniently extract volatility forecasts based on the conditional expectation of today's volatility given its past terms. The proposed empirical application of the novel CV-HAR model involves more than seven years of high-frequency transaction prices for ten stocks and evaluates the in-sample, out-of-sample and one-step-ahead forecast performance of the CV-HAR model for daily realized-kernel measures of volatility. The proposed new forecasting model is shown to outperform the HAR benchmark under different models for marginal distributions, copula construction methods, and forecasting settings.