A Test of Efficiency for the S & P 500 Index Option Market Using Variance Forecasts

A Test of Efficiency for the S & P 500 Index Option Market Using Variance Forecasts
Author: Jaesun Noh
Publisher:
Total Pages: 48
Release: 1993
Genre: Stock exchanges
ISBN:
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To forecast future option prices, autoregressive models of implied volatility derived from observed option prices are commonly employed [see Day and Lewis (1990), and Harvey and Whaley (1992)]. In contrast, the ARCH model proposed by Engle (1982) models the dynamic behavior in volatility, forecasting future volatility using only the return series of an asset. We assess the performance of these two volatility prediction models from S&P 500 index options market data over the period from September 1986 to December 1991 by employing two agents who trade straddles, each using one of the two different methods of forecast. Straddle trading is employed since a straddle does not need to be hedged. Each agent prices options according to her chosen method of forecast, buying (selling) straddles when her forecast price for tomorrow is higher (lower) than today's market closing price, and at the end of each day the rates of return are computed. We find that the agent using the GARCH forecast method earns greater profit than the agent who uses the implied volatility regression (IVR) forecast model. In particular, the agent using the GARCH forecast method earns a profit in excess of a cost of $0.25 per straddle with the near-the-money straddle trading.

Model-Based Versus Model-Free Implied Volatility

Model-Based Versus Model-Free Implied Volatility
Author: Ph.D. Biktimirov (CFA, Ernest N.)
Publisher:
Total Pages:
Release: 2019
Genre:
ISBN:
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This study compares the efficacy of Black-Scholes implied volatility (BSIV) with model-free implied volatility (MFIV) in providing volatility forecasts for 13 North American, European, and Asian stock market indexes: S&P 500 (United States), S&P/ASX 200 (Australia), S&P/TSX 60 (Canada), AEX (the Netherlands), EURO STOXX 50 (Eurozone) CAC 40 (France), DAX 30 (Germany), HSI (Hong Kong), NIFTY 50 (India), Nikkei 225 (Japan), KOSPI 200 (Korea), SMI (Switzerland), and FTSE 100 (United Kingdom). In-sample volatility forecasts show that both BSIV and MFIV significantly improve the fit of a GJR-GARCH(1,1) model. However, BSIV dominates MFIV for predicting future volatility. Out-of-sample one-month volatility forecasts also indicate that BSIV outperforms both MFIV and GJR-GARCH(1,1) volatility.

Option Market (In)efficiency and Implied Volatility Dynamics After Return Jumps

Option Market (In)efficiency and Implied Volatility Dynamics After Return Jumps
Author: Juho Kanniainen
Publisher:
Total Pages: 28
Release: 2019
Genre:
ISBN:
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In informationally efficient financial markets, option prices and this implied volatility should immediately be adjusted to new information that arrives along with a jump in underlying's return, whereas gradual changes in implied volatility would indicate market inefficiency. Using minute-by-minute data on S&P 500 index options, we provide evidence regarding delayed and gradual movements in implied volatility after the arrival of return jumps. These movements are directed and persistent, especially in the case of negative return jumps. Our results are significant when the implied volatilities are extracted from at-the-money options and out-of-the-money puts, while the implied volatility obtained from out-of-the-money calls converges to its new level immediately rather than gradually. Thus, our analysis reveals that the implied volatility smile is adjusted to jumps in underlying's return asymmetrically. Finally, it would be possible to have statistical arbitrage in zero-transaction-cost option markets, but under actual option price spreads, our results do not imply abnormal option returns.

Trading Volatility Spreads

Trading Volatility Spreads
Author: Peter F. Pope
Publisher:
Total Pages: 33
Release: 1999
Genre:
ISBN:
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If returns on two assets share common volatility components, the prices of options on the assets should be interdependent and the implied volatility spread should mean revert. We, first demonstrate, using the canonical correlation method, that there is a common component among the volatilities of the returns on Samp;P 100 and Samp;P 500 indexes. We then exploit this commonality by trading on the volatility spread between tick-by-tick OEX and SPX call options listed on the CBOE. Our vega-delta-neutral strategies generated significant profits, even after transaction costs are taken into account. The results suggest that the two options markets are not jointly efficient.

Forecasting Volatility in the Financial Markets

Forecasting Volatility in the Financial Markets
Author: Stephen Satchell
Publisher: Elsevier
Total Pages: 428
Release: 2011-02-24
Genre: Business & Economics
ISBN: 0080471420
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Forecasting Volatility in the Financial Markets, Third Edition assumes that the reader has a firm grounding in the key principles and methods of understanding volatility measurement and builds on that knowledge to detail cutting-edge modelling and forecasting techniques. It provides a survey of ways to measure risk and define the different models of volatility and return. Editors John Knight and Stephen Satchell have brought together an impressive array of contributors who present research from their area of specialization related to volatility forecasting. Readers with an understanding of volatility measures and risk management strategies will benefit from this collection of up-to-date chapters on the latest techniques in forecasting volatility. Chapters new to this third edition:* What good is a volatility model? Engle and Patton* Applications for portfolio variety Dan diBartolomeo* A comparison of the properties of realized variance for the FTSE 100 and FTSE 250 equity indices Rob Cornish* Volatility modeling and forecasting in finance Xiao and Aydemir* An investigation of the relative performance of GARCH models versus simple rules in forecasting volatility Thomas A. Silvey - Leading thinkers present newest research on volatility forecasting - International authors cover a broad array of subjects related to volatility forecasting - Assumes basic knowledge of volatility, financial mathematics, and modelling