Arnulfo Rodriguez

Predicting Stock Market Indices Movements

This paper examines the extent to which the daily movements of three large emerging markets stock indices are predictable. Lagged technical indicators are used as explanatory variables. In the analysis we employed seven classification techniques and assessed the discriminatory power of the classifiers through the area under the receiver operating characteristic (ROC) curve. The results show that the daily movements of the three indices are better predictable than random. After taking into account the bias induced by non-synchronous price quotations, a trading system with break-even costs is simulated. The non-random classifiers yield returns above those of both random walk and contrarian investment strategies. No inefficiency is found due to the fact that relatively low break-even transaction costs are enough to eliminate the sources of trading profits.

Uncertainty about the Persistence of Periods with Large Price Shocks and the Optimal Reaction of the Monetary Authority

Uncertainty about the persistence of periods characterized by large price shocks is an important aspect of monetary policy. This type of uncertainty posed some difficulties for central banks in 2004. This paper formalizes the treatment of this type of uncertainty by solving an optimal control problem in which the economy randomly alternates between two regimes characterized by different magnitudes of price shocks. By using an open economy model, we find that the optimal policy rule is both regime-contingent and robust. In particular, we find that: a) the optimal reaction of the interest rate is dependent on both the current regime and on the difference in the magnitude of the shocks between regimes; b) the alternation between regimes leads to more aggressive policy reactions with respect to inflation and the second lag of the real exchange rate; and c) after a robust selection of transition probabilities, the min-max probability of switching to the regime with large price shocks increases when such regime is more harmful. In general, cautious behavior renders smaller losses than recklessness for the central bank. This result argues in favor of caution over recklessness in the formulation of monetary policy when there is uncertainty about the persistence of periods with large price shocks

Recursive Thick Modeling and the Choice of Monetary Policy in Mexico

The choice of monetary policy is the most important concern of central banks. However, this choice is always confronted, inter alia, with two relevant aspects of economic policy: parameter instability and model uncertainty. This paper deals with both types of uncertainty using a very specific class of models in an optimal control framework. For optimal policy rates series featuring the first two moments similar to those of the actual nominal interest rates in Mexico, we show that recursive thick modeling gives a better approximation than recursive thin modeling. We complement previous work by evaluating the usefulness of both recursive thick modeling and recursive thin modeling in terms of direction-of-change forecastability.