			Home About us Contact

Option Pricing Models (option + pricing_models)

Distribution by Scientific Domains

Business, Economics, Finance and Accounting	91%

Distribution within Business, Economics, Finance and Accounting

General & Introductory Finance & Investments	54%
Institutional & Corporate Finance	17%

Selected Abstracts

Testing Option Pricing Models with Stochastic Volatility, Random Jumps and Stochastic Interest Rates

INTERNATIONAL REVIEW OF FINANCE, Issue 3-4 2002
George J. Jiang
In this paper, we propose a parsimonious GMM estimation and testing procedure for continuous-time option pricing models with stochastic volatility, random jump and stochastic interest rate. Statistical tests are performed on both the underlying asset return model and the risk-neutral option pricing model. Firstly, the underlying asset return models are estimated using GMM with valid statistical tests for model specification. Secondly, the preference related parameters in the risk-neutral distribution are estimated from observed option prices. Our findings confirm that the implied risk premiums for stochastic volatility, random jump and interest rate are overall positive and varying over time. However, the estimated risk-neutral processes are not unique, suggesting a segmented option market. In particular, the deep ITM call (or deep OTM put) options are clearly priced with higher risk premiums than the deep OTM call (or deep ITM put) options. Finally, while stochastic volatility tends to better price long-term options, random jump tends to price the short-term options better, and option pricing based on multiple risk-neutral distributions significantly outperforms that based on a single risk-neutral distribution. [source]

Specification Analysis of Option Pricing Models Based on Time-Changed Lévy Processes

THE JOURNAL OF FINANCE, Issue 3 2004
Jing-zhi Huang
We analyze the specifications of option pricing models based on time-changed Lévy processes. We classify option pricing models based on the structure of the jump component in the underlying return process, the source of stochastic volatility, and the specification of the volatility process itself. Our estimation of a variety of model specifications indicates that to better capture the behavior of the S&P 500 index options, we need to incorporate a high frequency jump component in the return process and generate stochastic volatilities from two different sources, the jump component and the diffusion component. [source]

Managing Risks in Multiple Online Auctions: An Options Approach,

DECISION SCIENCES, Issue 3 2005
Ram Gopal
ABSTRACT The scenario of established business sellers utilizing online auction markets to reach consumers and sell new products is becoming increasingly common. We propose a class of risk management tools, loosely based on the concept of financial options that can be employed by such sellers. While conceptually similar to options in financial markets, we empirically demonstrate that option instruments within auction markets cannot be developed employing similar methodologies, because the fundamental tenets of extant option pricing models do not hold within online auction markets. We provide a framework to analyze the value proposition of options to potential sellers, option-holder behavior implications on auction processes, and seller strategies to write and price options that maximize potential revenues. We then develop an approach that enables a seller to assess the demand for options under different option price and volume scenarios. We compare option prices derived from our approach with those derived from the Black-Scholes model (Black & Scholes, 1973) and discuss the implications of the price differences. Experiments based on actual auction data suggest that options can provide significant benefits under a variety of option-holder behavioral patterns. [source]

Smiles, Bid-ask Spreads and Option Pricing

EUROPEAN FINANCIAL MANAGEMENT, Issue 3 2001
Ignacio Peña
Given the evidence provided by Longstaff (1995), and Peña, Rubio and Serna (1999) a serious candidate to explain the pronounced pattern of volatility estimates across exercise prices might be related to liquidity costs. Using all calls and puts transacted between 16:00 and 16:45 on the Spanish IBEX-35 index futures from January 1994 to October 1998 we extend previous papers to study the influence of liquidity costs, as proxied by the relative bid-ask spread, on the pricing of options. Surprisingly, alternative parametric option pricing models incorporating the bid-ask spread seem to perform poorly relative to Black-Scholes. [source]

Testing Option Pricing Models with Stochastic Volatility, Random Jumps and Stochastic Interest Rates

STRATEGY AND SHAREHOLDER VALUE CREATION: THE REAL OPTIONS FRONTIER

JOURNAL OF APPLIED CORPORATE FINANCE, Issue 2 2000
Martha Amram
The current interest in real options reflects the dramatic increase in the uncertainty of the business environment. Viewed narrowly, the real options approach is the extension of financial option pricing models to the valuation of options on real (that is, nonfinancial) assets. More broadly, the real options approach is a way of thinking that helps managers formulate their strategic options,the future opportunities that are created by today's investments,while considering their likely effect on shareholder value. But if the real options framework promises to link strategy more closely to shareholder value creation, there are some major challenges on the frontier of application. In the first part of this paper, the authors tackle the question, "What is really new about real options, and how does the approach differ from other wellestablished ways to make strategic decisions under uncertainty?" This article provides a specific definition of real options that relies on the ability to track marketpriced risk. Using examples from oil exploration and pharmaceutical drug development, the authors also show how specific features of the industry and the application itself determine the usefulness of the real options approach. The second part of the paper addresses the question: Given the many differences between real and financial options, how should a real options application be framed? The authors examine the use of real options in the valuation of Internet companies to demonstrate the required judgment and tradeoffs in the framing of real options applications. The case of Webvan, an online grocer, is used to illustrate the inter-action between strategy, execution, and valuation. [source]

SELF-DECOMPOSABILITY AND OPTION PRICING

MATHEMATICAL FINANCE, Issue 1 2007
Peter Carr
The risk-neutral process is modeled by a four parameter self-similar process of independent increments with a self-decomposable law for its unit time distribution. Six different processes in this general class are theoretically formulated and empirically investigated. We show that all six models are capable of adequately synthesizing European option prices across the spectrum of strikes and maturities at a point of time. Considerations of parameter stability over time suggest a preference for two of these models. Currently, there are several option pricing models with 6,10 free parameters that deliver a comparable level of performance in synthesizing option prices. The dimension reduction attained here should prove useful in studying the variation over time of option prices. [source]

MSM Estimators of European Options on Assets with Jumps

MATHEMATICAL FINANCE, Issue 2 2001
João Amaro de Matos
This paper shows that, under some regularity conditions, the method of simulated moments estimator of European option pricing models developed by Bossaerts and Hillion (1993) can be extended to the case where the prices of the underlying asset follow Lévy processes, which allow for jumps, with no losses on their asymptotic properties, still allowing for the joint test of the model. [source]

Specification Analysis of Option Pricing Models Based on Time-Changed Lévy Processes

Hedging under the influence of transaction costs: An empirical investigation on FTSE 100 index options

THE JOURNAL OF FUTURES MARKETS, Issue 5 2007
Andros Gregoriou
The Black,Scholes (BS; F. Black & M. Scholes, 1973) option pricing model, and modern parametric option pricing models in general, assume that a single unique price for the underlying instrument exists, and that it is the mid- (the average of the ask and the bid) price. In this article the authors consider the Financial Times and London Stock Exchange (FTSE) 100 Index Options for the time period 1992,1997. They estimate the ask and bid prices for the index, and show that, when substituted for the mid-price in the BS formula, they provide superior option price predictors, for call and put options, respectively. This result is reinforced further when they .t a non-parametric neural network model to market prices of liquid options. The empirical .ndings in this article suggest that the ask and bid prices of the underlying asset provide a superior fit to the mid/closing price because they include market maker's, compensation for providing liquidity in the market for constituent stocks of the FTSE 100 index. © 2007 Wiley Periodicals, Inc. Jrl Fut Mark 27:471,494, 2007 [source]

Option pricing with a non-zero lower bound on stock price

THE JOURNAL OF FUTURES MARKETS, Issue 8 2005
Ming Dong
Black, F. and Scholes, M. (1973) assume a geometric Brownian motion for stock prices and therefore a normal distribution for stock returns. In this article a simple alternative model to Black and Scholes (1973) is presented by assuming a non-zero lower bound on stock prices. The proposed stock price dynamics simultaneously accommodate skewness and excess kurtosis in stock returns. The feasibility of the proposed model is assessed by simulation and maximum likelihood estimation of the return probability density. The proposed model is easily applicable to existing option pricing models and may provide improved precision in option pricing. © 2005 Wiley Periodicals, Inc. Jrl Fut Mark 25:775,794, 2005 [source]

Assessment and propagation of input uncertainty in tree-based option pricing models

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 3 2009
Henryk Gzyl
Abstract This paper aims to provide a practical example of assessment and propagation of input uncertainty for option pricing when using tree-based methods. Input uncertainty is propagated into output uncertainty, reflecting that option prices are as unknown as the inputs they are based on. Option pricing formulas are tools whose validity is conditional not only on how close the model represents reality, but also on the quality of the inputs they use, and those inputs are usually not observable. We show three different approaches to integrating out the model nuisance parameters and show how this translates into model uncertainty in the tree model space for the theoretical option prices. We compare our method with classical calibration-based results assuming that there is no options market established and no statistical model linking inputs and outputs. These methods can be applied to pricing of instruments for which there is no options market, as well as a methodological tool to account for parameter and model uncertainty in theoretical option pricing. Copyright © 2008 John Wiley & Sons, Ltd. [source]