Changing default risk dependencies during the subprime crisis: DJ iTraxx subindices and goodness-of-fit-testing for copulas

This paper tests whether (and to what extent) default risk dependencies changed during the subprime crisis in 2007 and 2008. This is done by applying a Goodness-of-fit test, based on the Rosenblatt transformation, to test various null hypotheses with respect to the copula function that describes the stochastic dependence between daily returns of six sector-specific subindices of the Dow Jones iTraxx Credit Default Swap index for Europe. Overall, the results suggest that in the bivariate case, the t-copula is a better approximation to the true copula of returns of DJ iTraxx subindices than the normal copula or the generalized Clayton copula. On average, the number of degrees of freedom of the bivariate t-copula tends to decrease during the crisis. As expected, the correlation between the returns of the subindices increases significantly during the crisis. However, the multivariate analysis reveals that it is only before the crisis that the null hypothesis of a six-dimensional t-copula is not rejected. During the crisis, the multivariate stochastic dependence between the sector-specific DJ iTraxx subindices seems to change in such a complex way that it is no longer sufficiently described by a multivariate t-copula.     

It’s Not What You Know, but How You Use It: Statistical Knowledge and Adolescent Problem Gambling

This paper examined the nature of irrational gambling-related cognitions in a sample of 926 adolescents (mean age = 14.5 years) sampled from Australian schools. Students were differentiated according to gambling status and administered a series of items that assessed their understanding of objective odds, the nature of randomness, the role of skill in gambling, and the perceived profitability of gambling. The results confirmed previous findings that problem gamblers tend to be more irrational in their perceptions, as indicated by stronger beliefs in the role of skilful play in chance activities, and that gambling is a potentially profitable activity. However, counter intuitively, problem gamblers did not appear to have any poorer understanding of objective probabilities. These results are discussed in terms of Sevigny and Ladouceur’s (2004) concept of cognitive switching as well as psychological research concerning the role of emotional and motivational factors in the development of an illusion of control. The implications of these findings for gambling education programs are discussed.

Why are Women Better Decoders of Nonverbal Language?

Women have consistently been found to be better decoders of nonverbal language compared to men. It is unknown however, whether this sex difference is biological or non-biological in nature. The current study sought to test for the first time the effect emotional intelligence (EI), trait dominance, and psychological gender had on nonverbal decoding accuracy. 86 undergraduate university students (43 males and 43 female), mean age 20.86 years old, completed the mini-Profile of Nonverbal Sensitivity, the Mayer–Salovey–Caruso Emotional Intelligence Test, the Dominance Subscale of The Sixteen Personality Factor Questionnaire, and the Bem Sex-Role Inventory. Overall women were found to score 4.35 points significantly higher than men in nonverbal ability, 95 % CI [?2.31, 2.31], and 7.15 points significantly higher than men in EI, 95 % CI [?5.97, 5.97]. Higher EI scores were also found to predict significantly greater nonverbal decoding accuracy. These findings suggest that EI rather than biological sex appears to be the salient factor in an individual’s nonverbal decoding accuracy. Trait dominance, and participants’ masculinity and femininity scores however, were not found to be significantly correlated with nonverbal decoding ability, thus the non-biological model of EI, trait dominance, and psychological gender did not account for significantly more unique variance in decoding accuracy, compared to the biological model when biological sex alone was considered.     

Optimal minimax squared error risk estimation of the mean of a multivariate normal distribution

Minimax squared error risk estimators of the mean of a multivariate normal distribution are characterized which have smallest Bayes risk with respect to a spherically symmetric prior distribution for (i) squared error loss, and (ii) zero-one loss depending on whether or not estimates are consistent with the hypothesis that the mean is null. In (i), the optimal estimators are the usual Bayes estimators for prior distributions with special structure. In (ii), preliminary test estimators are optimal. The results are obtained by applying the theory of minimax-Bayes-compromise decision problems.     

A bivariate normal example where the locally most powerful and distantly most powerful test statistics are beaten everywhere from a median p-value standpoint

In the bivariate normal, n=2 case, when testing H₀:μ_x=μ_y=0,σ² _x=σ² _y=1, ρ=0 vs. H₁:μ_x=μ_y=0,σ² _x=σ² _y=1, 0<ρ<1, it is shown that the median p-values given by the locally most powerful test and the distantly most powerful test are both beaten everywhere by the median of a third test.     

Modeling the Distribution of Price Sensitivity and Implications for Optimal Retail Pricing

This article focuses on the distribution of price sensitivity across consumers. We employ a random-coefficient logit model in which brand-specific intercepts and price-slope coefficients are allowed to vary across households. The model is estimated with panel data for two product categories. The implications of the estimated model are deduced through an optimal retail pricing analysis that combines the panel data with chain-level cost figures. We test parametric distributional assumptions using semiparametric density estimates based on series expansions.     

Refined Inference on Long Memory in Realized Volatility

There is an emerging consensus in empirical finance that realized volatility series typically display long range dependence with a memory parameter (d) around 0.4 (Andersen et al., 2001 Andersen , T. G. , Bollerslev , T. , Diebold , F. X. , Labys , P. ( 2001 ). The distribution of realized exchange rate volatility . Journal of the American Statistical Association 96 ( 453 ): 42 – 55 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]; Martens et al., 2004 Martnes , M. , Van Dijk , D. , De Pooter , M. ( 2004 ). Modeling and forecasting S&P 500 volatility: Long memory, structural breaks and nonlinearity. Tinbergen Institute Discussion Paper 2004-067/4 . [Google Scholar]). The present article provides some illustrative analysis of how long memory may arise from the accumulative process underlying realized volatility. The article also uses results in Lieberman and Phillips (2004 Lieberman , O. , Phillips , P. C. B. ( 2004 ). Expansions for the distribution of the maximum likelihood estimator of the fractional difference parameter . Econometric Theory 20 ( 3 ): 464 – 484 . [Google Scholar], 2005 Lieberman , O. , Phillips , P. C. B. ( 2005 ). Expansions for approximate maximum likelihood estimators of the fractional difference parameter . The Econometrics Journal 8 : 367 – 379 . [Google Scholar]) to refine statistical inference about d by higher order theory. Standard asymptotic theory has an O(n ^?1/2) error rate for error rejection probabilities, and the theory used here refines the approximation to an error rate of o(n ^?1/2). The new formula is independent of unknown parameters, is simple to calculate and user-friendly. The method is applied to test whether the reported long memory parameter estimates of Andersen et al. (2001 Andersen , T. G. , Bollerslev , T. , Diebold , F. X. , Labys , P. ( 2001 ). The distribution of realized exchange rate volatility . Journal of the American Statistical Association 96 ( 453 ): 42 – 55 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) and Martens et al. (2004 Martnes , M. , Van Dijk , D. , De Pooter , M. ( 2004 ). Modeling and forecasting S&P 500 volatility: Long memory, structural breaks and nonlinearity. Tinbergen Institute Discussion Paper 2004-067/4 . [Google Scholar]) differ significantly from the lower boundary (d = 0.5) of nonstationary long memory, and generally confirms earlier findings.     

A bayesian analysis of trend determination in economic time series

In this paper we provide a comprehensive Bayesian posterior analysis of trend determination in general autoregressive models. Multiple lag autoregressive models with fitted drifts and time trends as well as models that allow for certain types of structural change in the deterministic components are considered. We utilize a modified information matrix-based prior that accommodates stochastic nonstationarity, takes into account the interactions between long-run and short-run dynamics and controls the degree of stochastic nonstationarity permitted. We derive analytic posterior densities for all of the trend determining parameters via the Laplace approximation to multivariate integrals. We also address the sampling properties of our posteriors under alternative data generating processes by simulation methods. We apply our Bayesian techniques to the Nelson-Plosser macroeconomic data and various stock price and dividend data. Contrary to DeJong and Whiteman (1989a,b,c), we do not find that the data overwhelmingly favor the existence of deterministic trends over stochastic trends. In addition, we find evidence supporting Perron's (1989) view that some of the Nelson and Plosser data are best construed as trend stationary with a change in the trend function occurring at 1929.     

The asymptotic equivalence between the iterated improved 2sls estimator and the 3sls estimator

In this paper we show that the 3SLS estimator of a system of equations is asymptotically equivalent to an iterative 2SLS estimator applied to each equation, augmented with the residuals from the other equations. This result is a natural extension of Telser (1964).