GENERAL EQUILIBRIUM WITH CONSTANT RELATIVE RISK AVERSION AND VASICEK INTEREST RATES1

We consider a pure exchange economy consisting of a single risky asset whose dividend drift rate is modeled as an Omstein-Uhlenbeck process, and a representative agent with power-utility who, in equilibrium, consumes the dividend paid by the risky asset. Endogenously determined interest rates are found to be of the Vasicek (1977) type the mean and variance of the equilibrium stock price are stochastic and have mean-reverting components A closed-form solution for a standard call option is determined for the case of log-utility. Equilibrium values have interesting implications for the equity premium puzzle observed by Mehra and Prescott (1985)     

A TEST OF A GENERAL EQUILIBRIUM STOCK OPTION PRICING MODEL

An empirical version of the Cox, Ingersoll, and Ross (1985a) call option pricing model is derived, assuming execution price uncertainty in the options market. the pricing restrictions come in the form of moment conditions in the option pricing error. These can be estimated and tested using a version of the method of simulated moments (MSM). Simulation estimates, obtained by discretely approximating the risk-neutral processes of the underlying stock price and the interest rate, are substituted for analytically unknown call prices. the asymptotics and other aspects of the MSM estimator are discussed. the model is tested on transaction prices at 15-minute intervals. It substantially outperforms the Black-Scholes model. the empirical success of the Cox-Ingersoll-Ross model implies that the continuous-time interest rate implicit in synchronous transaction quotes of 90-day Treasury-bill futures contracts is an-albeit noisy-proxy for the instantaneous volatility on common stock. the process of the instantaneous volatility is found to be close to nonstationary. It is well approximated by a heteroskedastic unit-root process. With this approximation, the Cox-Ingersoll-Ross model only slightly overprices long-maturity options.     

UNDERSTANDING THE FINANCIAL CONSEQUENCES OF THE BULLWHIP EFFECT IN A MULTI‐ECHELON SUPPLY CHAIN

This article is concerned with how supply chain strategies can mitigate the Bullwhip Effect and inflated inventories from the perspective of the central firm (typically a manufacturer) in the supply chain. We first outline a base case scenario with a validated system dynamics simulation model, using supply chain characteristics as reported by a real firm, in this case a Mexican electronics supplier to U.S. automobile assemblers. We find, surprisingly, that a lower Bullwhip Effect Index (BE) does not always lead to lower costs in the supply chain studied. Furthermore, sensitivity analysis suggests some interesting, counterintuitive results. The implications of these findings are further developed as we test how lead time reduction can also reduce the Bullwhip Effect in the simulated setting.     

CONVERGENCE OF A LEAST‐SQUARES MONTE CARLO ALGORITHM FOR AMERICAN OPTION PRICING WITH DEPENDENT SAMPLE DATA

We analyze the convergence of the Longstaff–Schwartz algorithm relying on only a single set of independent Monte Carlo sample paths that is repeatedly reused for all exercise time‐steps. We prove new estimates on the stochastic component of the error of this algorithm whenever the approximation architecture is any uniformly bounded set of L² functions of finite Vapnik–Chervonenkis dimension (VC‐dimension), but in particular need not necessarily be either convex or closed. We also establish new overall error estimates, incorporating bounds on the approximation error as well, for certain nonlinear, nonconvex sets of neural networks.