Michael W. Brandt

Estimating Portfolio and Consumption Choice: A Conditional Euler Equations Approach

This paper develops a nonparametric approach to examine how portfolio and consumption choice depends on variables that forecast time-varying investment opportunities. I estimate single-period and multiperiod portfolio and consumption rules of an investor with constant relative risk aversion and a one-month to twenty-year horizon. The investor allocates wealth to the NYSE index and a 30-day Treasury bill. I find that the portfolio choice varies significantly with the dividend yield, default premium, term premium, and lagged excess return. Furthermore, the optimal decisions depend on the investors horizon and rebalancing frequency.

Simulated Likelihood Estimation of Diffusions with an Application to Exchange Rate Dynamics in Incomplete Markets

We present an econometric method for estimating the parameters of a diffusion model from discretely sampled data. The estimator is transparent, adaptive, and inherits the asymptotic properties of the generally unattainable maximum likelihood estimator. We use this method to estimate a new continuous-time model of the Joint dynamics of interest rates in two countries and the exchange rate between the two currencies. The model allows financial markets to be incomplete and specifies the degree of incompleteness as a stochastic process. Our empirical results offer several new insights into the dynamics of exchange rates.

Portfolio Choice Problems

Publisher Summary This chapter focuses on the econometric treatment of portfolio choice problems. The goal is to describe, discuss, and illustrate through examples the different econometric approaches proposed in the literature for relating the theoretical formulation and solution of a portfolio choice problem to the data. In focusing on the econometrics of the portfolio choice problem, this chapter is at best a cursory overview of the broad portfolio choice literature. Much of the discussion is focused on the single-period portfolio choice problem with standard preferences, normally distributed returns, and frictionless markets. There are many recent advances in the portfolio choice literature. The econometric techniques discussed in this chapter can be applied to realistic formulations. It also discusses a number of modeling issues and extensions that arise in formulating the problem.

Volatility Forecasting With Range-Based EGARCH Models

We provide a simple, yet highly effective framework for forecasting return volatility by combining exponential generalized autoregressive conditional heteroscedasticity models with data on the range. Using Standard and Poors 500 index data for 1983–2004, we demonstrate the importance of a long-memory specification, based on either a two-factor structure or fractional integration, that allows for some asymmetry between market returns and volatility innovations. Out-of-sample forecasts reinforce the value of both this specification and the use of range data in the estimation. We find substantial forecastability of volatility as far as 1 year from the end of the estimation period, contradicting the return-based conclusions of West and Cho and of Christoffersen and Diebold that predicting volatility is possible only for short horizons.

A No-Arbitrage Approach to Range-Based Estimation of Return Covariances and Correlations

We extend range-based volatility estimation to the multivariate case. In particular, we propose a range-based covariance estimator motivated by a key financial economic consideration, the absence of arbitrage, in addition to statistical considerations. We show that this estimator is highly efficient yet robust to the market microstructure noise arising from bid-ask bounce and asynchronous trading.

The effect of macroeconomic news on beliefs and preferences: Evidence from the options market

Abstract We examine the effect of regularly scheduled macroeconomic announcements on the beliefs and preferences of participants in the U.S. Treasury market by comparing the option-implied state-price densities (SPDs) of bond prices shortly before and after the announcements. We find that the announcements reduce the uncertainty implicit in the second moment of the SPD regardless of the content of the news. The changes in the higher-order moments, in contrast, depend on whether the news is good or bad for economic prospects. We explore three alternative explanations for our empirical findings: relative mispricing, changes in beliefs, and changes in preferences. We find that our results are consistent with time-varying risk aversion.

Price Discovery in the Treasury Futures Market

The paper conducts a regression analysis utilizing both futures and cash market prices and net orderflow to determine where price discovery takes place as well as the forces at play that influence the location. Specifically, given the strong theoretical linkage between the U.S. Treasury cash and futures markets, they compare how orderflow contributes to price discovery and analyze how and when information flows from one market to the other. How a number of environmental variables (trader type, financing rates, and liquidity) impact the information flows between these two markets is also considered. Their findings provide new evidence on the extent to which price discovery happens away from a primary market.

Cross-sectional tests of deterministic volatility functions

Abstract We study the cross-sectional performance of option pricing models in which the volatility of the underlying stock is a deterministic function of the stock price and time. For each date in our sample of FTSE 100 index option prices, we fit an implied binomial tree to the panel of all European style options with different strike prices and maturities and then examine how well this model prices a corresponding panel of American style options. We find that the implied binomial tree model performs no better than an ad-hoc procedure of smoothing Black–Scholes implied volatilities across strike prices and maturities. Our cross-sectional results complement the time-series findings of Dumas et al. [J. Finance 53 (1998) 2059].

Earnings Announcements are Full of Surprises

We study the drift in returns of portfolios formed on the basis of the stock price reaction around earnings announcements. The Earnings Announcement Return (EAR) captures the market reaction to unexpected information contained in the companys earnings release. Besides the actual earnings news, this includes unexpected information about sales, margins, investment, and other less tangible information communicated round the earnings announcement. A strategy that buys and sells companies sorted on EAR produces an average abnormal return of 7.55% per year, 1.3%more than a strategy based on the traditional measure of earnings surprise, SUE. The post earnings announcement drift for EAR strategy is stronger than post earnings announcement drift for SUE. More importantly, unlike SUE, the EAR strategy returns do not show a reversal after 3 quarters. The EAR and SUE strategies appear to be independent of each other. A strategy that exploits both pieces of information generates abnormal returns of about 12.5% on an annual basis.

Simulated Likelihood Estimation of Affine Term Structure Models from Panel Data

We show how to estimate affine term structure models from a panel of noisy bond yields using simulated maximum likelihood based on importance sampling. We approximate the likelihood function of the state-space representation of the model by correcting the likelihood function of a Gaussian first-order approximation for the non-normalities introduced by the affine factor dynamics. Depending on the accuracy of the correction, which is computed through simulations, the quality of the estimator ranges from quasi-maximum likelihood (no correction) to exact maximum likelihood as the simulation size grows.