Valuation of Barrier Options in a Black--Scholes Setup with Jump Risk

This paper discusses the pitfalls in the pricing of barrier options a pproximations of the underlying continuous processes via discrete lattice models. These problems are studied first in a Black-Scholes model. Improvements result from a trinomial model and a further modified model where price changes occur at the jump times of a Poisson process. After the numerical difficulties have been resolved in the Black-Scholes model, unpredictable discontinuous price movements are incorporated.binomial model, option valuation, lattice--approach, barrier option

Building a Consistent Pricing Model from Observed Option Prices

This paper constructs a model for the evolution of a risky security that is consistent with a set of observed call option prices. It explicitly treats the fact that only a discrete data set can be observed in practice. The framework is general and allows for state dependent volatility and jumps. The theoretical properties are studied. An easy procedure to check for arbitrage opportunities in market data is proved and then used to ensure the feasibility of our approach. The implementation is discussed: testing on market data reveals a U-shaped form for the "local volatility" depending on the state and, surprisingly, a large probability for strong price movements.Markov Chain, no-arbitrage, cross-entropy, model risk

Stock Evolution under Stochastic Volatility: A Discrete Approach

This paper examines the pricing of options by approximating extensions of the Black-Scholes setup in which volatility follows a separate diffusion process. It gereralizes the well-known binomial model, constructing a discrete two-dimensional lattice. We discuss convergence issues extensively and calculate prices and implied volatilities for European- and American-style put options.binomial model, option valuation, lattice approach, stochastic volatility

Aggregation of preferences for skewed asset returns

This paper characterizes the equilibrium demand and risk premiums in the presence of skewness risk. We extend the classical mean-variance two-fund separation theorem to a three-fund separation theorem. The additional fund is the skewness portfolio, i.e. a portfolio that gives the optimal hedge of the squared market return; it contributes to the skewness risk premium through co-variation with the squared market return and supports a stochastic discount factor that is quadratic in the market return. When the skewness portfolio does not replicate the squared market return, a tracking error appears; this tracking error contributes to risk premiums through kurtosis and pentosis risk if and only if preferences for skewness are heterogeneous. In addition to the common powers of market returns, this tracking error shows up in stochastic discount factors as priced factors that are products of the tracking error and market returns