Nonparametric Bayesian multiple testing for longitudinal performance stratification

This paper describes a framework for flexible multiple hypothesis testing of autoregressive time series. The modeling approach is Bayesian, though a blend of frequentist and Bayesian reasoning is used to evaluate procedures. Nonparametric characterizations of both the null and alternative hypotheses will be shown to be the key robustification step necessary to ensure reasonable Type-I error performance. The methodology is applied to part of a large database containing up to 50 years of corporate performance statistics on 24,157 publicly traded American companies, where the primary goal of the analysis is to flag companies whose historical performance is significantly different from that expected due to chance.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS252 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Cognition and Behavior in Two-Person Guessing Games: An Experimental Study

This paper reports experiments that elicit subjects' initial responses to 16 dominancesolvable two-person guessing games. The structure is publicly announced except for varying payoff parameters, to which subjects are given free access, game by game, through an interface that records their information searches. Varying the parameters allows strong separation of the behavior implied by leading decision rules and makes monitoring search a powerful tool for studying cognition. Many subjects' decisions and searches show clearly that they understand the games and seek to maximize their payoffs, but have boundedly rational models of others' decisions, which lead to systematic deviations from equilibrium.

On attitude polarization under Bayesian learning with non-additive beliefs

Ample psychological evidence suggests that people’s learning behavior is often prone to a "myside bias" or "irrational belief persistence" in contrast to learning behavior exclusively based on objective data. In the context of Bayesian learning such a bias may result in diverging posterior beliefs and attitude polarization even if agents receive identical information. Such patterns cannot be explained by the standard model of rational Bayesian learning that implies convergent beliefs. As our key contribution, we therefore develop formal models of Bayesian learning with psychological bias as alternatives to rational Bayesian learning. We derive conditions under which beliefs may diverge in the learning process despite the fact that all agents observe the same - arbitrarily large - sample, which is drawn from an "objective" i.i.d. process. Furthermore, one of our learning scenarios results in attitude polarization even in the case of common priors. Key to our approach is the assumption of ambiguous beliefs that are formalized as non-additive probability measures arising in Choquet expected utility theory. As a specific feature of our approach, our models of Bayesian learning with psychological bias reduce to rational Bayesian learning in the absence of ambiguity.Non-additive Probability Measures, Choquet Expected Utility Theory, Bayesian Learning, Bounded Rationality

Price Wars and Collusion in the Spanish Electricity Market

We analyze the time-series of prices in the Spanish electricity market by means of a time varying-transition-probability Markov switching model. Accounting for changes in demand and cost conditions (which re°ect changes in input costs, capacity avail- ability and hydro power), we show that the time-series of prices is characterized by two signi¯cantly di®erent price levels. Based on a Green and Porter (1984)'s type of model that introduces several institutional details, we construct trigger variables that a®ect the likelihood of starting a price war. By interpreting the signs of the triggers, we are able to infer some of the properties of the collusive strategy that ¯rms might have followed. We obtain more empirical support to Green and Porter's model than previous studies

Consumer reactions to self-expressive brand display

Brand names and other brand elements are often displayed on one’s body or clothes for the purpose of personal value expression. Despite the frequency of such brand displays in the marketplace, we know little about how consumers respond to seeing brands in this fashion. A recent view of consumer brand identification—the concept of brand engagement in self-concept (BESC)—provides a unique perspective from which to explore how consumers react when see-ing brands displayed by others. Across three experiments, we demonstrate a consistent pattern of findings indicating that consumers’ reactions to others ostentatiously displaying brands as means of value expression are strongest for those with high BESC levels and with a high value focus during brand exposure. The research highlights important variations in consumers’ responses to self-expressive brand stimuli associated with others; implications for branding practice and re-search are provided.Brand engagement; self-concept; advertising; brand management

Three essays on modeling energy prices with time-varying volatility and jumps

This thesis addresses the modeling of energy prices with time-varying volatility and jumps in three separate and self-contained papers: A. Modeling energy futures volatility through stochastic volatility processes with Markov chain Monte Carlo This paper studies the volatility dynamics of futures contracts on crude oil, natural gas and electricity. To accomplish this purpose, an appropriate Bayesian model comparison exercise between seven stochastic volatility (SV) models and their counterpart GARCH models is performed, with both classes of time-varying volatility processes being estimated through a Markov chain Monte Carlo technique. A comparison exercise for hedging purposes is also considered by computing the extreme risk measures (using the Conditional Value-at-Risk) of simulated returns from the SV model with the best performance - i.e., the SV model with a t-distribution - and the standard GARCH(1,1) model for the hedging of crude oil, natural gas and electricity positions. Overall, we find that: (i) volatility plays an important role in energy futures markets; (ii) SV models generally outperform their GARCH-family counterparts; (iii) a model with t-distributed innovations generally improves the fitting performance of both classes of time-varying volatility models; (iv) the maturity of futures contracts matters; and (v) the correct specification for the stochastic behavior of futures prices impacts the extreme market risk measures of hedged and unhedged positions. B. How does electrification under energy transition impact the portfolio management of energy firms? This paper presents a novel approach for structuring dependence between electricity and natural gas prices in the context of energy transition: a copula of meanreverting and jump-diffusion processes. Based on historical day-ahead prices of the Nord Pool electricity market and the Henry Hub natural gas market, a stochastic model is estimated via the maximum likelihood approach and considering the dependency structure between the innovations of these two-dimensional returns. Given the role of natural gas in the global policy for energy transition, different copula functions are fit to electricity and natural gas returns. Overall, we find that: (i) using an out-of-sample forecasting exercise, we show that it is important to consider both mean-reversion and jumps; (ii) modeling correlation between the returns of electricity and natural gas prices, assuring nonlinear dependencies are satisfied, leads us to the adoption of Gumbel and Student-t copulas; and (iii) without government incentive schemes in renewable electricity projects, the usual maximization of the risk-return trade-off tends to avoid a high exposure to electricity assets. C. Modeling commodity prices under alternative jump processes and fat tails dynamics The recent fluctuations in commodity prices affected significantly Oil Gas (O&G) companies’ returns. However, integrated O&G companies are not only exposed to the downturn of oil prices since a high level of integration allows these firms to obtain non-perfectly positive correlated portfolio. This paper aims to test several different stochastic processes to model the main strategic commodities in integrated O&G companies: brent, natural gas, jet fuel and diesel. The competing univariate models include the log-normal and double exponential jump-diffusion model, the Variance-Gamma process and the geometric Brownian motion with nonlinear GARCH volatility. Given the effect of correlation between these assets, we also estimate multivariate models, such as the Dynamic Conditional Correlation (DCC) GARCH, DCC-GJR-GARCH and the DCC-EGARCH models. Overall, we find that: (i) the asymmetric conditional heteroskedasticity model substantially improves the performance of the univariate jump-diffusion models; and (ii) the multivariate approaches are the best models for our strategic energy commodities, in particular the DCC-GJR-GARCH model