Financial Engineering

The Great Divide in economic and financial development and the convergence in financial architecture among the successful countries raise fundamental questions about how financial development interacts with economic growth. Is it possible to engineer a development takeoff by creating a modern financial architecture from scratch? Or are financial institutions and markets a reflection of underlying conditions in the real sector? Or are both financial development and economic growth driven by some other underlying variables?

A planner and agent in a permanent-income economy cannot observe part of the state, regard their model as an approximation, and value decision rules that are robust across a set of models. They use robust decision theory to choose allocations. Equilibrium prices reflect the preference for robustness and so embody a “market price of Knightian uncertainty.” We compute market prices of risk and compare them with a model that assumes that the state is fully observed. We use detection error probabilities to constrain a single parameter that governs the taste for robustness.

We investigate the relation between dividend changes and future profitability, measured in terms of either future earnings or future abnormal earnings. Supporting "the information content of dividends hypothesis," we find that dividend changes provide information about the level of profitability in subsequent years, incremental to market and accounting data. We also document that dividend changes are positively related to earnings changes in each of the two years after the dividend change.

The spectacularly successful transformation of Times Square has become a model for other cities. From its beginning as Longacre Square, Times Square’s commercialism, signage, cultural diversity, and social tolerance have been deeply embedded in New York City’s psyche. Its symbolic role guaranteed that any plan for its renewal would push the hot buttons of public controversy: free speech, property-taking through eminent domain, development density, tax subsidy, and historic preservation.

The calculation of value-at-risk (VAR) for large portfolios of complex derivative securities presents a tradeoff between speed and accuracy. The fastest methods rely on simplifying assumptions about changes in underlying risk factors and about how a portfolio's value responds to these changes in the risk factors. Greater realism in measuring changes in portfolio value generally comes at the price of much longer computing times.

Financial statement analysis has traditionally been seen as part of the fundamental analysis required for equity valuation. But the analysis has typically been ad hoc. Drawing on recent research on accounting-based valuation, this paper outlines a financial statement analysis for use in equity valuation. Standard profitability analysis is incorporated, and extended, and is complemented with an analysis of growth. An analysis of operating activities is distinguished from the analysis of financing activities. The perspective is one of forecasting payoffs to equities.

The purpose of this chapter is to propose and illustrate a simple measure of the risk of a cash shortfall arising from the funding requirements of a futures hedge. We give particular attention to the probability of a large shortfall anytime up to a specified horizon as opposed to merely at that horizon.

This article develops precise connections among two general approaches to building interest rate models: a general equilibrium approach using a pricing kernel and the Heath, Jarrow, and Morton framework based on specifying forward rate volatilities and the market price of risk. The connections exploit the observation that a pricing kernel is uniquely determined by its drift. Through these connections we provide, for any arbitrage-free term structure model, a representative-consumer real production economy supporting that term structure model in equilibrium.

Pricing financial options often requires Monte Carlo methods. One particular case is that of barrier options, whose payoff may be zero depending on whether or not an underlying asset crosses a barrier during the life of the option. This paper develops variance reduction techniques that take advantage of the special structure of barrier options, and are appropriate for general simulation problems with similar structure. We use a change of measure at each step of the simulation to reduce the variance arising from the possibility of a barrier corssing at each monitoring date.