This paper extends the class of stochastic volatility diffusions for asset returns to encompass Poisson jumps of time-varying intensity. We find that any reasonably descriptive continuous-time model for equity-index returns must allow for discrete jumps as well as stochastic volatility with a pronounced negative relationship between return and volatility innovations. We also find that the dominant empirical characteristics of the return process appear to be priced by the option market. Our analysis indicates a general correspondence between the evidence extracted from daily equity-index returns and the stylized features of the corresponding options market prices. MUCH ASSET AND DERIVATIVE PRICING THEORY is based on diffusion models for primary securities. However, prescriptions for practical applications derived from these models typically produce disappointing results. A possible explanation could be that analytic formulas for pricing and hedging are available for only a limited set of continuous-time representations for asset returns

An extensive empirical literature in finance has documented not only the presence of anamolies in the Black-Scholes model, but also the "term-structures" of these anamolies (for instance, the behavior of the volatility smile or of unconditional returns at different maturities). Theoretical efforts in the literature at addressing these anamolies have largely focussed on two extensions of the Black-Scholes model: introducing jumps into the return process, and allowing volatility to be stochastic. This paper employs commonly-used versions of these two classes of models to examine the extent to which the models are theoretically capable of resolving the observed anamolies. We find that each model exhibits some "term-structure" patterns that are fundamentally inconsistent with those observed in the data. As a consequence, neither class of models constitutes an adequate explanation of the empirical evidence, although stochastic volatility models fare better than jumps in this regard.

We investigate a new basic model for asset pricing, the hyperbolic model, which allows an almost perfect statistical fit of stock return data. After a brief introduction into the theory supported by an appendix we use also secondary market data to compare the hyperbolic model to the classical Black-Scholes model. We study implicit volatilities, the smile effect and the pricing performance. Exploiting the full power of the hyperbolic model, we construct an option value process from a statistical point of view by estimating the implicit risk-neutral density function from option data. Finally we present some new valueat -risk calculations leading to new perspectives to cope with model risk. I Introduction There is little doubt that the Black-Scholes model has become the standard in the finance industry and is applied on a large scale in everyday trading operations. On the other side its deficiencies have become a standard topic in research. Given the vast literature where refinements a...

This paper studies the empirical performance of jump-diffusion models that allow for stochastic volatility and correlated jumps affecting both prices and volatility. The results show that the models in question provide reasonable fit to both option prices and returns data in the in-sample estimation period. This contrasts previous findings where stochastic volatility paths are found to be too smooth relative to the option implied dynamics. While the models perform well during the high volatility estimation period, they tend to overprice long dated contracts out-of-sample. This evidence points towards a too simplistic specification of the mean dynamics of volatility.

pp.567-611. Contents

This paper proposes and estimates a more general parametric stochastic variance model of equity index returns than has been previously considered using data from both underlying and options markets. The parameters of the model under both the objective and riskneutral measures are estimated simultaneously. I conclude that the square root stochastic variance model of Heston (1993) and others is incapable of generating realistic returns behavior and find that the data are more accurately represented by a stochastic variance model in the CEV class or a model that allows the price and variance processes to have a time-varying correlation. Specifically, I find that as the level of market variance increases, the volatility of market variance increases rapidly and the correlation between the price and variance processes becomes substantially more negative. The heightened heteroskedasticity in market variance that results generates realistic crash probabilities and dynamics and causes returns to display values of skewness and kurtosis much more consistent with their sample values. While the model dramatically improves the fit of options prices relative to the square root process, it falls short of explaining the implied volatility smile for short-dated options.

Please do not quote without permission * Chidambaran is visiting at NYU, on leave from Tulane. Lee holds joint appointments at Tulane and HKUST. Trigueros is at Tulane. We are grateful for the comments from participants at seminars at Tulane

The primary purpose of this paper is to model uncertain digital objects in view of financial risk management in an open network. We have made an abstraction of the objects and defined the security token, which is abbreviated into a word coinage setok. Each setok has its price, values, and timestamp on it as well as the main contents. Not only the price but also the values can be uncertain and may cause risks. A number of properties of the setok are defined. They include value response to compromise, price response to compromise, refundability, tradability, online divisibility, and offline divisibility. Then, in search of risk-hedging tools, a derivative written not on the price but on the value is introduced. The derivative investigated is a simple European-type call option. With the help of stochastic theory, we have derived several option-pricing formulae. These formulae do not require any divisibility of the underlying setok. With respect to applications, an inverse estimation of compromise probability is studied. The stochastic approach is extended to deal with a jump caused by the compromise and the resultant revocation. This extension gives a partial differential equation (PDE) to price the call option; given a set of parameters including the compromise probability, the PDE can tell us the option price. By making an inverse use of this, we can estimate the risk of compromise. Key words: network security, digital object, setok, risk hedge, derivative, option pricing. Contents 1

This paper proposes that equilibrium valuation is a powerful method to generate endogenous jumps in asset prices. We specify an economy with continuous consumption and dividend paths, in which endogenous price jumps originate from the market impact of regime-switches in the drifts and volatilities of fundamentals. We parsimoniously incorporate regimes of heterogeneous durations and verify that the persistence of a shock endogenously increases the magnitude of the induced price jump. As the number of frequencies driving fundamentals goes to infinity, the price process converges to a novel stochastic process, which we call a multifractal jump-diffusion.

This paper ischzzjKjh with simulation-basedinferenc in generalized models ofstocx#hA# volatility de#ned by heavy-tailed Student-t distributions (withunknown degrees of freedom) and exogenous variables in the observation and volatility equations and a jumpcmphxB-B in the observation equation. By building on the work of Kim, Shephard and Chib (Rev.Ecv.hx Stud. 65 (1998) 361), we develop e#clop arkov ckov onte Carlo algorithms for estimating these models. The paper alsodisczK-j how the likelihoodfunclih of these modelscd be cehqzjK by appropriateparticr #lter methods. Computation of the marginal likelihood by the method of Chib (J. Amer.