Option pricing should be based on a realistic process for the underlying and on the construction of a risk-neutral measure as induced by a no-arbitrage replication strategy. This paper presents a realistic and complete, "first principles,'' computation of option prices. The underlying is modeled...

Pushing models to extremes can expose output biases that stem from underlying assumptions. In the case of industry standard option valuation models, long term, high volatility securities provide a stress test vehicle. For instance, in evaluating a stock with 60% volatility, industry standard...

Implicit in industry standard option pricing models is the expectation that roughly 25% of stocks with 60% consistent volatility will septuple within 10 years, an extraordinary rate of appreciation. The exceptionally high equilibrium anticipated returns for an improbably large percentage of high...

Daily returns of financial assets are frequently found to exhibit positive autocorrelation at lag 1. When specifying a linear AR(l) conditional mean, one may ask how this predictability affects option prices. We investigate the dependence of option prices on autoregressive dynamics under...

This paper uses asymmetric heteroskedastic normal mixture models to fit return data and to price options. The models can be estimated straightforwardly by maximum likelihood, have high statistical fit when used on S&P 500 index return data, and allow for substantial negative skewness and time...

Factorial moments are convenient tools in nuclear physics to characterize the multiplicity distributions when phase-space resolution (Delta) becomes small. For uncorrelated particle production within Delta, Gaussian statistics holds and factorial moments Fq are equal to unity for all orders q....

This paper examines the theoretically obtained prices with values based on temperature data in the Isle of Man and the UK. We have also seen that the simulated temperature trajectories do not appear to include entire seasons where the temperature remains cooler than normal. Anecdotally we have...

We propose optimal mean-variance dynamic hedging strategies in discrete time under a multivariate Gaussian regime-switching model. The methodology, which also performs pricing, is robust to time-varying and clustering risk observed in financial time series. As such, it overcomes the main...

In this paper we examine the empirical performance of affine jump diffusion models with stochastic volatility in a time series study of crude oil prices. We compare four different models and estimate them using the Markov Chain Monte Carlo method. The support for a stochastic volatility model...

In this paper we solve the discrete time mean-variance hedging problem when asset returns follow a multivariate autoregressive hidden Markov model. Time dependent volatility and serial dependence are well established properties of financial time series and our model covers both. To illustrate...