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Risk-neutral Distribution (risk-neutral + distribution)

Distribution by Scientific Domains
Business, Economics, Finance and Accounting100%

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]

RISK PREMIUM EFFECTS ON IMPLIED VOLATILITY REGRESSIONS

THE JOURNAL OF FINANCIAL RESEARCH, Issue 2 2010
Leonidas S. Rompolis
Abstract This article provides new insights into the sources of bias of option implied volatility to forecast its physical counterpart. We argue that this bias can be attributed to volatility risk premium effects. The latter are found to depend on high-order cumulants of the risk-neutral density. These cumulants capture the risk-averse behavior of investors in the stock and option markets for bearing the investment risk that is reflected in the deviations of the implied risk-neutral distribution from the normal distribution. We show that the bias of implied volatility to forecast its corresponding physical measure can be eliminated when the implied volatility regressions are adjusted for risk premium effects. The latter are captured mainly by the third-order risk-neutral cumulant. We also show that a substantial reduction of higher order risk-neutral cumulants biases to predict their corresponding physical cumulants is supported when adjustments for risk premium effects are made. [source]

Pricing American options by canonical least-squares Monte Carlo

THE JOURNAL OF FUTURES MARKETS, Issue 2 2010
Qiang Liu
Options pricing and hedging under canonical valuation have recently been demonstrated to be quite effective, but unfortunately are only applicable to European options. This study proposes an approach called canonical least-squares Monte Carlo (CLM) to price American options. CLM proceeds in three stages. First, given a set of historical gross returns (or price ratios) of the underlying asset for a chosen time interval, a discrete risk-neutral distribution is obtained via the canonical approach. Second, from this canonical distribution independent random samples of gross returns are taken to simulate future price paths for the underlying. Third, to those paths the least-squares Monte Carlo algorithm is then applied to obtain early exercise strategies for American options. Numerical results from simulation-generated gross returns under geometric Brownian motions show that the proposed method yields reasonably accurate prices for American puts. The CLM method turns out to be quite similar to the nonparametric approach of Alcock and Carmichael and simulations done with CLM provide additional support for their recent findings. CLM can therefore be viewed as an alternative for pricing American options, and perhaps could even be utilized in cases when the nature of the underlying process is not known. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:175,187, 2010 [source]

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]