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The Informational Content of Implied Volatility in Individual Stocks and the Market. By: Andrey Fradkin Date: 1/22/08. Introduction.
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The Informational Content of Implied Volatility in Individual Stocks and the Market By: AndreyFradkin Date: 1/22/08
Introduction • Examine relative informational content of implied volatility forecasts against historical volatility forecasts using high frequency data for 10 large cap stocks trading on NYSE and for the SPY. • Important prior studies on this subject are Andersen et al(2007) and Jiang and Tian(2006). Find contradictory evidence as to which measure is better. • This study is first to use individual stocks, HAR-RV CJ model, RAV, and robust regression in examining this question. • HAR-RV-CJ model allows for the separation of the jump component of variance from continuous component of volatility. Andrey Fradkin: Informational Content of Volatility
Outline • Jump Detection • HAR-RV-CJ Models • Data Preparation • Summary Statistics • Summary Plots • Significance of Jump Statistics in HAR-RV-CJ Models Andrey Fradkin: Informational Content of Volatility
Background Mathematics Realized Variation: Realized Bi-Power Variation: Andrey Fradkin: Informational Content of Volatility
Background Mathematics Part 2 • Tri-Power Quarticity • Quad-Power Quarticity Andrey Fradkin: Informational Content of Volatility
Background Mathematics Part 3 • Z-statistics (max version) – Paper uses .999 significance level Andrey Fradkin: Informational Content of Volatility
Original HAR-RV-J Model(Developed in Andersen, Bollerslev, Diebold 2006, Extended for use with RAV measure in Forsberg and Ghysels(2007)) Andrey Fradkin: Informational Content of Volatility
The HAR-RV-CJ Model Andrey Fradkin: Informational Content of Volatility
Mincer-Zarnowitz Regressions • Standard framework for evaluating implied volatility forecasts. • Implied volatilities are obtained from the OptionMetrics database. One implied volatility is used per day. This implied volatility is taken from an at-the-money call option expiring about a month ahead. Andrey Fradkin: Informational Content of Volatility
Combined Regressions • Combine implied forecasts with historical forecasts. • Compare marginal improvements from historical to combined and from implied to combined. • Two types of combined forecasts, one using RAV and the other using RV-CJ Andrey Fradkin: Informational Content of Volatility
Combined Regressions Andrey Fradkin: Informational Content of Volatility
Data Management • Uses TAQ high-frequency data that was cleaned by Tzuo Law. Prices are sampled at five minute intervals. • 10 equities and the SPY are used in the study. Equities chosen are based on the high open interest in their options. • In-Sample data ranges from 2001 through 2004 • The Out-of-Sample data encompasses all of 2005 Andrey Fradkin: Informational Content of Volatility
10 Stocks Referred to from now on by Ticker • BMY - Bristol-Meyers • C - Citigroup • GE - General Electric • GS -Goldman Sachs • HD - Home Depot • KO – Coca Cola • MDT - Medtronic • MOT – Motorola • NOK – Nokia • TXN – Texas Instruments • SPY – SPY RV’s with Vix Implied Volatility • SPX – SPY RV’s with SPX Option Implied Volatility Andrey Fradkin: Informational Content of Volatility
Summary Statistics AndreyFradkin: Informational Content of Volatility
HAR-RV-CJ – Level Month Ahead – Newey-West Standard Errors Lag(60) * p<0.05, ** p<0.01, *** p<0.001 Andrey Fradkin: Informational Content of Volatility
HAR-RV-CJ – 1 week Newey-West Standard Errors Lag(60) Andrey Fradkin: Informational Content of Volatility
HAR-RV-CJ – Level Day Ahead – Newey-West Standard Errors Lag(60) Andrey Fradkin: Informational Content of Volatility
Realized Variance and IV Andrey Fradkin: Informational Content of Volatility
Realized Variance and RAV Andrey Fradkin: Informational Content of Volatility
SPY RV and RAV Andrey Fradkin: Informational Content of Volatility