Volatility Forecasting with High Frequency Data

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Abstract/Contents

Abstract: The daily volatility is typically unobserved but can be estimated using high frequent tick-by-tick data. In this paper, we study the problem of forecasting the unobserved volatility using past values of measured volatility. Specifically, we use daily estimates of volatility based on high frequency data, called realized variance, and construct the optimal linear forecast of future volatility. Utilizing single exponential smoothing, we develop formulae that yield the optimal coefficients for our forecast. We compare the precision of our forecast with those of two popular forecasting models, the HAR regression model and the Local Level model, in terms of mean squared errors. In empirical analysis of the seven DJIA stocks, our model performs better than two competing models in most of the cases.

Type of resource	text
Date created	May 2007

Author	Jang, Youngjun
Primary advisor	Hansen, Peter
Degree granting institution	Stanford University, Department of Economics

Subject	Stanford Department of Economics
Subject	Realized Variance
Subject	High-Frequency Data
Subject	Instrumental Variables
Subject	Local Level Model
Subject	HAR model
Subject	linear forecasting
Genre	Thesis

Location	https://purl.stanford.edu/hq648dc6849

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Preferred Citation: Jang, Youngjun. (2007). Volatility Forecasting with High Frequency Data. Stanford Digital Repository. Available at: https://purl.stanford.edu/hq648dc6849

Stanford University, Department of Economics, Honors Theses

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