Author: Christos ChristitsasPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Extracting Market Expectations from Traded Option Prices: an Empirical Test of the Stochastic Volatility Model on FTSE 100 Index Options
Author: Christos ChristitsasExtracting Market Expectations from Traded Options Prices: a Comparative Assessment of the Black Scholes and Stochastic Volatility Models
Author: Rajeev VohoraThe Empirical Performance of the Stochastic Volatility Model in Pricing FTSE-100 Index Options
Author: Krisda PhatcharoenThe Stochastic Behavior of Market Volatility Implied in the Prices of Index Options and a Test of Market Efficiency
Author: Changhyon ChoPublisher:
ISBN:
Category : Financial futures
Languages : en
Pages : 360
Book Description
A Test of Efficiency for the S & P 500 Index Option Market Using Variance Forecasts
Author: Jaesun NohPublisher:
ISBN:
Category : Stock exchanges
Languages : en
Pages : 48
Book Description
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.
Application of Stochastic Volatility Models in Option Pricing
Author: Pascal DebusPublisher: GRIN Verlag
ISBN: 3656491941
Category : Business & Economics
Languages : de
Pages : 59
Book Description
Bachelorarbeit aus dem Jahr 2010 im Fachbereich BWL - Investition und Finanzierung, Note: 1,2, EBS Universität für Wirtschaft und Recht, Sprache: Deutsch, Abstract: The Black-Scholes (or Black-Scholes-Merton) Model has become the standard model for the pricing of options and can surely be seen as one of the main reasons for the growth of the derivative market after the model ́s introduction in 1973. As a consequence, the inventors of the model, Robert Merton, Myron Scholes, and without doubt also Fischer Black, if he had not died in 1995, were awarded the Nobel prize for economics in 1997. The model, however, makes some strict assumptions that must hold true for accurate pricing of an option. The most important one is constant volatility, whereas empirical evidence shows that volatility is heteroscedastic. This leads to increased mispricing of options especially in the case of out of the money options as well as to a phenomenon known as volatility smile. As a consequence, researchers introduced various approaches to expand the model by allowing the volatility to be non-constant and to follow a sto-chastic process. It is the objective of this thesis to investigate if the pricing accuracy of the Black-Scholes model can be significantly improved by applying a stochastic volatility model.
Empirical Test of Option Pricing Models with Stochastic Volatility in S & P 5oo Futures Options
Author: Sichong ChenEmpirical Performance of Option Pricing Models with Stochastic Local Volatility
Author: Greg OrosiPublisher:
ISBN:
Category :
Languages : en
Pages : 16
Book Description
We examine the empirical performance of several stochastic local volatility models that are the extensions of the Heston stochastic volatility model. Our results indicate that the stochastic volatility model with quadratic local volatility significantly outperforms the stochastic volatility model with CEV type local volatility. Moreover, we compare the performance of these models to several other benchmarks and find that the quadratic local volatility model compares well to the best performing option pricing models reported in the current literature for European-style S&P500 index options. Our results also indicate that the model with quadratic local volatility reproduces the characteristics of the implied volatility surface more accurately than the Heston model. Finally, we demonstrate that capturing the shape of the implied volatility surface is necessary to price binary options accurately.
Market Expectations and Option Prices
Author: Martin MandlerPublisher: Boom Koninklijke Uitgevers
ISBN: 9783790800494
Category : Business & Economics
Languages : en
Pages : 244
Book Description
This book surveys and summarizes the numerous approaches used to extract information on market expectations from option prices. The various approaches are thoroughly explained and many practical issues are discussed, including: data selection, data preparation, and presentation and interpretation of results. This enables the reader to easily implement these techniques in his own applied work. Most studies concerning uncertainty in financial markets focus on actual uncertainty as represented by historical volatility measures, variances etc. In contrast, using option prices allows us to study uncertainty in expectations, i.e. to take a forward looking perspective. In some applications we study how ECB-council meetings affect uncertainty in money market expectations. Most interesting among our results is a number of event studies which compare how uncertainty in market participants’ expectations reacts to anticipated and unanticipated results of ECB-council meetings.
The Information Content of Implied Volatilities and Model-Free Volatility Expectations
Author: Stephen J. TaylorPublisher:
ISBN:
Category :
Languages : en
Pages : 64
Book Description
The volatility information content of stock options for individual firms is measured using option prices for 149 U.S. firms during the period from January 1996 to December 1999. Volatility forecasts defined by historical stock returns, at-the-money (ATM) implied volatilities and model-free (MF) volatility expectations are compared for each firm. The recently developed model-free volatility expectation incorporates information across all strike prices, and it does not require the specification of an option pricing model.Our analysis of ARCH models shows that, for one-day-ahead estimation, historical estimates of conditional variances outperform both the ATM and the MF volatility estimates extracted from option prices for more than one-third of the firms. This result contrasts with the consensus about the informational efficiency of options written on stock indices; several recent studies find that option prices are more informative than daily stock returns when estimating and predicting index volatility. However, for the firms with the most actively traded options, we do find that the option forecasts are nearly always more informative than historical stock returns. When the prediction horizon extends until the expiry date of the options, our regression results show that the option forecasts are more informative than forecasts defined by historical returns for a substantial majority (86%) of the firms. Although the model-free (MF) volatility expectation is theoretically more appealing than alternative volatility estimates and has been demonstrated to be the most accurate predictor of realized volatility by Jiang and Tian (2005) for the Samp;P 500 index, the results for our firms show that the MF expectation only outperforms both the ATM implied volatility and the historical volatility for about one-third of the firms. The firms for which the MF expectation is best are not associated with a relatively high level of trading in away-from-the-money options.