Financial prediction with constrained tail risk

A new class of asymmetric loss functions derived from the least absolute deviations or least squares loss with a constraint on the mean of one tail of the residual error distribution, is introduced for analyzing financial data. Motivated by risk management principles, the primary intent is to provide “cautious” forecasts under uncertainty. The net effect on fitted models is to shape the residuals so that on average only a prespecified proportion of predictions tend to fall above or below a desired threshold. The loss functions are reformulated as objective functions in the context of parameter estimation for linear regression models, and it is demonstrated how optimization can be implemented via linear programming. The method is a competitor of quantile regression, but is more flexible and broader in scope. An application is illustrated on prediction of NDX and SPX index returns data, while controlling the magnitude of a fraction of worst losses.     

A global consistency result for the two-dimensional Pareto distribution in the presence of misspecified inflation

A global consistency result for the ML estimator of a misspecified two-parameter Pareto distribution is proved. The misspecification is due to the assumption of a wrong inflation rate, which violates the i.i.d. assumption in the model. We also investigate how far away from the true parameters one finds the ML estimator of the misspecified model (asymptotically for a small misspecification r). Finally, for the case where the misspecification depends on the number of observations n, i.e., r=r _n , and where $r_{n}\stackrel{n\to \infty}{\longrightarrow}0A global consistency result for the ML estimator of a misspecified two-parameter Pareto distribution is proved. The misspecification is due to the assumption of a wrong inflation rate, which violates the i.i.d. assumption in the model. We also investigate how far away from the true parameters one finds the ML estimator of the misspecified model (asymptotically for a small misspecification r). Finally, for the case where the misspecification depends on the number of observations n, i.e., r=r _n , and where r_n? ^{n? ￥}0r_{n}\stackrel{n\to \infty}{\longrightarrow}0, we prove a central limit theorem for the ML estimator.     

Tests in contingency tables as regression tests

Applied researchers often use tests based on contingency tables, especially in preliminary data analysis and diagnostic testing. We show that many such tests may be alternatively implemented by testing for coefficient restrictions in linear regression systems.     

The effect of language use on the financial performance of microfinance banks: Evidence from cross-border activities in 74 countries

This multi-year study examines the relationship between financial performance and language use, observing 405 partnerships between microfinance banks and their international financial partners in 74 countries. Drawing on language research in international business, we find that microfinance banks based in English-speaking, French-speaking, and Spanish-speaking countries have higher performance. Furthermore, the linguistic distance between the home country of a microfinance bank and the home country of its international partner(s) is negatively related to its financial performance. Our large-scale study confirms the effect of language use on organization-level financial performance and extends research on language in multinationals from intra-firm to inter-firm relationships.     

Fine-Tailored for the Cartel-Favoritism in Procurement

In this paper, we investigate the interaction between two firms, which are involved in a repeated procurement relationship modeled as a multiple criteria auction, and an auctioneer (a government employee) who has discretion in devising the selection criteria. Our main result is that favoritism substantially facilitates collusion. It increases the gains from collusion and contributes to solving basic implementation problems for a cartel of bidders operating in a stochastically changing environment. A most simple allocation rule where firms take turns in winning, independently of stochastic social preferences and firms’ costs, achieves full cartel efficiency (including price, production, and design efficiency). In each period the selection criteria is fine-tailored to the in-turn winner: the “environment” adapts to the cartel. This result holds true when the expected punishment is a fixed cost. When the cost varies with the magnitude of the distortion of the selection criteria (compared to the true social preferences), favoritism only partially shelters the cartel from the environment. We thus find that favoritism generally facilitates collusion at a high cost for society. Our analysis suggests some anti-corruption measures that could be effective in curbing favoritism and collusion in public markets. It also suggests that the much-advocated rotation of officials is likely to be counter-productive.     

Individually rational, budget-balanced mechanisms and allocation of surplus

We investigate the issue of implementation via individually rational ex-post budget-balanced Bayesian mechanisms. We show that all decision rules generating a nonnegative expected social surplus are implementable via such mechanisms if and only if the probability distribution of the agents’ type profiles satisfies two conditions: the well-known condition of Crémer and McLean [1988. Full extraction of the surplus in Bayesian and dominant strategy auctions, Econometrica 56, 1247–1257] and the Identifiability condition introduced in this paper. We also show that these conditions are necessary for ex-post efficiency to be attainable with budget balance and individual rationality, and that the expected social surplus in these mechanisms can be distributed in any desirable way. Lastly, we demonstrate that, like Crémer–McLean condition, the Identifiability condition is generic if there are at least three agents.