In a laboratory experiment, we investigate the impact of temporary buy-options on efficiency, revenues, and bidding behavior in online proxy-auctions when bidders have independent private valuations. We show that the introduction of a buy-option reduces efficiency and at the same time fails to enhance revenues. In particular, we observe that the former presence of a temporary buy-option lowers ...

We propose a model to describe stock pinning on option expiration dates. We argue that if the open interest on a particular contract is unusually large, delta-hedging in aggregate by floor market-makers can impact the stock price and drive it to the strike price of the option. We derive a stochastic differential equation for the stock price which has a singular drift that accounts for the price...

In this article, we propose a numerical algorithm for computing price of discrete single and double barrier option under the emph{Black-Scholes} model. In virtue of some general transformations, the partial differential equations of option pricing in different monitoring dates are converted into simple diffusion equations. The present method is fast compared to alterna...

We examine how trinomial-tree based computations such as those involved in American or European-style option price valuations can be performed in parallel. Towards this we introduce a parallel algorithm for performing such computations on trinomial trees. The algorithm is described and analyzed in an architecture independent setting and achieves optimal theoretical speedup O(p) and is thus with...

A valuation model is presented for options on stocks for which BlackScholes arbitrage does not entirely eliminate risk. The price dynamics of a portfolio of options and the underlying security is quanti ed by requiring that the excess reward-to-risk ratio of the portfolio be identical to that of the underlying stock: excess return risk portfolio = excess return risk stock : The nonlinear evolut...

Background material on measure-theoretic probability theory and stochastic calculus is provided in order to clarify notation and inform the reader unfamiliar with these concepts. These fields are then employed in exploring two distinct but related approaches to fair option pricing: developing a partial differential equation whose solution, given specified boundary conditions, is the desired fai...

We propose a new cognitive framework for option price modelling, using quantum neural computation formalism. Briefly, when we apply a classical nonlinear neuralnetwork learning to a linear quantum Schrödinger equation, as a result we get a nonlinear Schrödinger equation (NLS), performing as a quantum stochastic filter. In this paper, we present a bidirectional quantum associative memory model f...

This paper studies the price of S&P 500 index options by using Heston's (1993) stochastic volatility option pricing model. The Heston model is calibrated by a two-step estimation procedure to incorporate both the information from time-series asset returns and the information from cross-sectional option data. In the first step, the recently developed, simulation-based “indirect inference method”...

We describe a model of a communication network that allows us to price complex network services as financial derivative contracts based on the spot price of the capacity in individual routers. We prove a theorem of a Girsanov transform that is useful for pricing linear derivatives on underlying assets, which can be used to price many complex network services, and it is used to price an option t...

We derive a closed-form solution for the price of a European call option in the presence of ambiguity about the stochastic process that determines the variance of the underlying asset's return. The option pricing formula of Heston (1993) is a particular case of ours, corresponding to the case in which there is no ambiguity (uncertainty is exclusively risk). In the presence of ambiguity, the var...