"A Trinomial Test for Paired Data When There are Many Ties"

This paper develops a new test, the trinomial test, for pairwise ordinal data samples to improve the power of the sign test by modifying its treatment of zero diRerences between observations, thereby increasing the use of sample information. Simulations demonstrate the power superiority of the proposed trinomial test statis- tic over the sign test in small samples in the presence of tie observations. We also show that the proposed trinomial test has substantially higher power than the sign test in large samples and also in the presence of tie observations, as the sign test ignores information from observations resulting in ties.

Robust Estimation and Forecasting of the Capital Asset Pricing Model

In this paper, we develop a modified maximum likelihood (MML) estimator for the multiple linear regression model with underlying student t distribution. We obtain the closed form of the estimators, derive the asymptotic properties, and demonstrate that the MML estimator is more appropriate for estimating the parameters of the Capital Asset Pricing Model by comparing its performance with least squares estimators (LSE) on the monthly returns of US portfolios. The empirical results reveal that the MML estimators are more efficient than LSE in terms of the relative efficiency of one-step-ahead forecast mean square error in small samples.Maximum likelihood estimators; Modified maximum likelihood estimators; Student t family; Capital asset pricing model; Robustness

A Trinomial Test for Paired Data When There are Many Ties

This paper develops a new test, the trinomial test, for pairwise ordinal data samples to improve the power of the sign test by modifying its treatment of zero differences between observations, thereby increasing the use of sample information. Simulations demonstrate the power superiority of the proposed trinomial test statistic over the sign test in small samples in the presence of tie observations. We also show that the proposed trinomial test has substantially higher power than the sign test in large samples and also in the presence of tie observations, as the sign test ignores information from observations resulting in ties.Sign test; trinomial test; non-parametric test; ties; test statistics; hypothesis testing

Robust Estimation and Forecasting of the Capital Asset Pricing Model

In this paper, we develop a modified maximum likelihood (MML) estimator for the multiple linear regression model with underlying student t distribution. We obtain the closed form of the estimators, derive the asymptotic properties, and demonstrate that the MML estimator is more appropriate for estimating the parameters of the Capital Asset Pricing Model by comparing its performance with least squares estimators (LSE) on the monthly returns of US portfolios. The empirical results reveal that the MML estimators are more efficient than LSE in terms of the relative efficiency of one-step-ahead forecast mean square error in small samples.Maximum likelihood estimators, Modified maximum likelihood estimators, Student t family, Capital asset pricing model, Robustness.

"Investor Preferences for Oil Spot and Futures Based on Mean-Variance and Stochastic Dominance"

This paper examines investor preferences for oil spot and futures based on mean-variance (MV) and stochastic dominance (SD). The mean-variance criterion cannot distinct the preferences of spot and market whereas SD tests leads to the conclusion that spot dominates futures in the downside risk while futures dominate spot in the upside profit. It is also found that risk-averse investors prefer investing in the spot index, whereas risk seekers are attracted to the futures index to maximize their expected utilities. In addition, the SD results suggest that there is no arbitrage opportunity between these two markets. Market efficiency and market rationality are likely to hold in the oil spot and futures markets.

Market Efficiency of Oil Spot and Futures: A Mean-Variance and Stochastic Dominance Approach

This paper examines the market efficiency of oil spot and futures prices by using both mean-variance (MV) and stochastic dominance (SD) approaches. Based on the West Texas Intermediate crude oil data for the sample period 1989-2008, we find no evidence of any MV and SD relationships between oil spot and futures indices. This infers that there is no arbitrage opportunity between these two markets, spot and futures do not dominate one another, investors are indifferent to investing in spot or futures, and the spot and futures oil markets are efficient and rational. The empirical findings are robust to each sub-period before and after the crises for different crises, and also to portfolio diversification.Stochastic dominance; risk averter; oil futures market; market efficiency

Investor Preferences for Oil Spot and Futures Based on Mean-Variance and Stochastic Dominance

This paper examines investor preferences for oil spot and futures based on mean-variance (MV) and stochastic dominance (SD). The mean-variance criterion cannot distinct the preferences of spot and market whereas SD tests leads to the conclusion that spot dominates futures in the downside risk while futures dominate spot in the upside profit. It is also found that risk-averse investors prefer investing in the spot index, whereas risk seekers are attracted to the futures index to maximize their expected utilities. In addition, the SD results suggest that there is no arbitrage opportunity between these two markets. Market efficiency and market rationality are likely to hold in the oil spot and futures markets.Stochastic dominance; risk averter; risk seeker; futures market; spot market

A Methodology for Information Flow Experiments

Information flow analysis has largely ignored the setting where the analyst has neither control over nor a complete model of the analyzed system. We formalize such limited information flow analyses and study an instance of it: detecting the usage of data by websites. We prove that these problems are ones of causal inference. Leveraging this connection, we push beyond traditional information flow analysis to provide a systematic methodology based on experimental science and statistical analysis. Our methodology allows us to systematize prior works in the area viewing them as instances of a general approach. Our systematic study leads to practical advice for improving work on detecting data usage, a previously unformalized area. We illustrate these concepts with a series of experiments collecting data on the use of information by websites, which we statistically analyze

Photoelectron spectra in strong-field ionization by a high frequency field

We analyze atomic photoelectron momentum distributions induced by bichromatic and monochromatic laser fields within the strong field approximation (SFA), separable Coulomb-Volkov approximation (SCVA), and ab initio treatment. We focus on the high frequency regime -- the smallest frequency used is larger than the ionization potential of the atom. We observe a remarkable agreement between the ab initio and velocity gauge SFA results while the velocity gauge SCVA fails to agree. Reasons of such a failure are discussed.Comment: Completely rewritten paper. Ionization by a two-color field is adde