Farid AitSahlia

Is concurrent engineering always a sensible proposition

Currently in the literature it is apparent that there exists a strong drive towards the employment of concurrent engineering as opposed to the serial progression of products through phases. In many cases there is much advantage to be gained from the early involvement of downstream activities such as the involvement of manufacturing in the early phases of engineering. This is the basis of the entire movement towards design for manufacturability. This note is a cautionary one which shows that the precise models, though rudimentarily simple, of the development process and its probabilities must be considered before statements can be made on the efficiency of one variety of engineering process over another; in other words, caveat emptor. Specifically, the authors show how this happens in a realistic environment and then propose a better alternative configuration that is a hybrid between serial and concurrent product design. >

Optimal crop planting schedules and financial hedging strategies under ENSO-based climate forecasts

This paper investigates the impact of ENSO-based climate forecasts on optimal planting schedules and financial yield-hedging strategies in a framework focused on downside risk. In our context, insurance and futures contracts are available to hedge against yield and price risks, respectively. Furthermore, we adopt the Conditional-Value-at-Risk (CVaR) measure to assess downside risk, and Gaussian copula to simulate scenarios of correlated non-normal random yields and prices. The resulting optimization problem is a mixed 0–1 integer programming formulation that is solved efficiently through a two-step procedure, first through an equivalent linear form by disjunctive constraints, followed by decomposition into sub-problems identified by hedging strategies. With data for a representative cotton producer in the Southeastern United States, we conduct a study that considers a wide variety of optimal planting schedules and hedging strategies under alternative risk profiles for each of the three ENSO phases (Niña, Niño, and Neutral.) We find that the Neutral phase generates the highest expected profit with the lowest downside risk. In contrast, the Niña phase is associated with the lowest expected profit and the highest downside risk. Additionally, yield-hedging insurance strategies are found to vary significantly, depending critically on the ENSO phase and on the price bias of futures contracts.

American option pricing under stochastic volatility: an empirical evaluation

Over the past few years, model complexity in quantitative finance has increased substantially in response to earlier approaches that did not capture critical features for risk management. However, given the preponderance of the classical Black–Scholes model, it is still not clear that this increased complexity is matched by additional accuracy in the ultimate result. In particular, the last decade has witnessed a flurry of activity in modeling asset volatility, and studies evaluating different alternatives for option pricing have focused on European-style exercise. In this paper, we extend these empirical evaluations to American options, as their additional opportunity for early exercise may incorporate stochastic volatility in the pricing differently. Specifically, the present work compares the empirical pricing and hedging performance of the commonly adopted stochastic volatility model of Heston (Rev Financial Stud 6:327–343, 1993) against the traditional constant volatility benchmark of Black and Scholes (J Polit Econ 81:637–659, 1973). Using S&P 100 index options data, our study indicates that this particular stochastic volatility model offers enhancements in line with their European-style counterparts for in-the-money options. However, the most striking improvements are for out-of-the-money options, which because of early exercise are more valuable than their European-style counterparts, especially when volatility is stochastic.

A canonical optimal stopping problem for American options under a double exponential jump-diffusion model

This paper presents a simple numerical approach to compute accurately the values and optimal exercise boundaries for American options when the underlying process is a double exponential jump-diffusion model that prices jump risk. The present work extends the canonical representation for American options initially developed in the Brownian motion set-up. Here, too, jump-diffusion pricing models can be reduced to a single optimal stopping problem, indexed by one more parameter, and linear spline approximations of the stopping boundary in the canonical scale with only a few knots are supported through numerical evidence. These approximations can then be exploited to solve the integral equation defining the early exercise boundary of an American option efficiently and accurately, thus leading to its efficient and accurate pricing and hedging.

American option pricing under stochastic volatility: an efficient numerical approach

This paper develops a new numerical technique to price an American option written upon an underlying asset that follows a bivariate diffusion process. The technique presented here exploits the supermartingale representation of an American option price together with a coarse approximation of its early exercise surface that is based on an efficient implementation of the least-squares Monte–Carlo algorithm (LSM) of Longstaff and Schwartz (Rev Financ Stud 14:113–147, 2001). Our approach also has the advantage of avoiding two main issues associated with LSM, namely its inherent bias and the basis functions selection problem. Extensive numerical results show that our approach yields very accurate prices in a computationally efficient manner. Finally, the flexibility of our method allows for its extension to a much larger class of optimal stopping problems than addressed in this paper.

Optimal Execution of Time-Constrained Portfolio Transactions

Time-constrained dynamic optimal portfolio transactions for institutional investors are investigated. The resulting constrained dynamic programming problem is solved approximately through a succession of quadratic programs. The ensuing strategies are then tested on real data. The model extends a recent one by accounting for liquidity differences between stocks.

Mean-Variance Spanning Tests: The Fiduciary Case in 401(k) Plans

We use regression-based tests for mean-variance spanning to justify a systematic implementation of efficient and prudent default fund selections in 401(k) plans. Though our approach ultimately rests on recommending two funds, it differs from the classical two-fund theorem, as the funds are not required to be efficient. In essence, our results illustrate the difference between, on one hand, theoretical mean-variance efficiency, and on the other, empirical mean-variance spanning. Furthermore, we address collinearity and the preponderance of numerical instability in regression-based testing for mean-variance spanning. Finally, we show that published statistical hypotheses for mean-variance testing of 401(k) plans are incorrectly specified.

Selected works of Kai Lai Chung

This unique volume presents a collection of the extensive journal publications written by Kai Lai Chung over a span of 70-odd years. It was produced to celebrate his 90th birthday. The selection is only a subset of the many contributions that he made throughout his prolific career. Another volume, Chance and Choice, published by World Scientific in 2004, contains yet another subset, with four articles in common with this volume. Kai Lai Chungs research contributions have had a major influence on several areas in probability. Among his most significant works are those related to sums of independent random variables, Markov chains, time reversal of Markov processes, probabilistic potential theory, Brownian excursions, and gauge theorems for the Schrodinger equation.As Kai Lai Chungs contributions spawned critical new developments, this volume also contains retrospective and perspective views provided by collaborators and other authors who themselves advanced the areas of probability and mathematics.

Information Stages in Efficient Markets

Market efficiency, in its strong form, asserts that asset prices fully reflect all available information. The classical event study methodology attempts to make explicit this link by assuming rigid and universal pre-event, event, and post-event periods. As an alternative, our framework captures the progressive diffusion of information around events as well as the overlapping impacts of separate events. We also illustrate that our approach captures mean-reversion of expected returns and increased volatility around announcement dates. These features reflect latent regime switches and are associated with semi-strong market efficiency.

Are There Critical Levels of Stochastic Volatility for Early Option Exercise

An appealing feature of Hestons stochastic volatility model is that it captures empirical characteristics such as fat return tails, leverage and volatility clustering. Another is the resulting analytic European option pricing formula, which can be computed efficiently. However, the corresponding, and more common, American option pricing problem is significantly more complex. In this paper we propose an American option pricing technique that is not only computationally efficient but also highlights the role played by the equilibrium volatility associated with Hestons model for early exercise. Our approach combines an early exercise boundary approximation involving this (constant) equilibrium volatility, for which fast and accurate algorithms abound, with the price decomposition formula for American options in Hestons model. Numerical comparisons against recent alternative techniques show our approach to be far more efficient. We also conduct an empirical out-of-sample validation based on S&P 100 data that shows that our mixed approach does result in a model that generates prices close to actuals.