Reportgreen2003

A Multiple Indicators Model for Volatility Using Intra-Daily Data

131 citations0/0 files storedNational Bureau of Economic Research

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Financial Risk and Volatility Modeling Financial Markets and Investment Strategies Stochastic processes and financial applications

Abstract

Many ways exist to measure and model financial asset volatility.In principle, as the frequency of the data increases, the quality of forecasts should improve.Yet, there is no consensus about a "true" or "best" measure of volatility.In this paper we propose to jointly consider absolute daily returns, daily high-low range and daily realized volatility to develop a forecasting model based on their conditional dynamics.As all are non-negative series, we develop a multiplicative error model that is consistent and asymptotically normal under a wide range of specifications for the error density function.The estimation results show significant interactions between the indicators.We also show that one-month-ahead forecasts match well (both in and out of sample) the market-based volatility measure provided by an average of implied volatilities of index options as measured by VIX.

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References

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2011Observation Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk 2005Practical Volatility and Correlation Modeling for Financial Market Risk Management 2008A General Framework for Observation Driven Time-Varying Parameter Models 2007An empirical model of daily highs and lows 2005Variation, Jumps, Market Frictions and High Frequency Data in Financial Econometrics

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