The Conditional Distribution of Real Estate Returns: Are Higher Moments Time Varying?

Previous research has shown that the returns on individual properties and listed property securities are skewed. This claim is investigated in the context of listed U.K. property companies and U.S. REITs. In particular, the shape of the conditional distribution of total monthly returns is examined for a group of 20 U.K. companies and 20 REITs. Also investigated is the claim that the skewness found in property returns varies over time. Using the model of Hansen (1994), it is found that while a large portion of property security returns in the sample do exhibit skewness in the conditional distribution only in a few instances is there time variation in the skewness parameter. There is little evidence to suggest that skewness is associated with the economic cycle.     

一种新颖的基于偏度的非视距区分算法

在超宽带(UWB)定位系统中,非视距(NLOS)传播是降低通信与定位精度可靠性的主要原因。因此,区分NLOS环境对提高定位精度尤为重要。针对该问题,提出了一种新的基于信道统计特性——偏度(Skewness)的NLOS区分算法。该算法首先将偏度在IEEE 802.15.4a信道模型（特别是室内家居和办公环境）中建模为对数正态分布,然后对其概率密度函数(PDF)做似然比检验来区分视距(LOS)与NLOS环境。仿真结果表明：室内UWB定位系统中,偏度可以更好地区分信道状态,在室内办公环境中,正确区分NLOS环境的概率可达99.99%。在定位模块中融入所获得的区分NLOS的结果将有助于定位精度的进一步提升。     

Skewed exchange-rate forecasts

We used survey data on exchange-rate forecasts of the dollar/euro exchange rate and the yen/dollar exchange rate to analyze the correlation of the skewness of the distribution of heterogeneous forecasts with movements of the exchange rate. Using various measures of skewness, we found a negative correlation of skewness of 1-month-ahead forecasts with exchange-rate movements. In contrast, the correlation of skewness of 12-months-ahead forecast with exchange-rate movements is positive. The negative correlation arising in the case of 1-month-ahead forecasts is consistent with expected mean reversion in exchange rates. The positive correlation arising in the case of longer term forecasts, in turn, is consistent with longer term bandwagon effects.     

Are You All Normal? It Depends!

The assumption of normality has underlain much of the development of statistics, including spatial statistics, and many tests have been proposed. In this work, we focus on the multivariate setting and first review the recent advances in multivariate normality tests for i.i.d. data, with emphasis on the skewness and kurtosis approaches. We show through simulation studies that some of these tests cannot be used directly for testing normality of spatial data. We further review briefly the few existing univariate tests under dependence (time or space), and then propose a new multivariate normality test for spatial data by accounting for the spatial dependence. The new test utilises the union-intersection principle to decompose the null hypothesis into intersections of univariate normality hypotheses for projection data, and it rejects the multivariate normality if any individual hypothesis is rejected. The individual hypotheses for univariate normality are conducted using a Jarque–Bera type test statistic that accounts for the spatial dependence in the data. We also show in simulation studies that the new test has a good control of the type I error and a high empirical power, especially for large sample sizes. We further illustrate our test on bivariate wind data over the Arabian Peninsula.     

Skewness and index futures return

In this paper, we show that the individual skewness, defined as the average of monthly skewness across firms, performs very well at predicting the return of S&P 500 index futures. This result holds after controlling for the liquidity risk or for the current business cycle conditions. We also find that individual skewness performs very well at predicting index futures returns out-of-sample.     

CAPM, Higher Co‐moment and Factor Models of UK Stock Returns

In this paper we examine the variables that explain the cross‐section of UK stock returns. Previous studies have found that the CAPM beta has moderate or even insignificant explanatory power once the Fama French factors are included. However, we control for different realised risk premia in up and down markets by using the same methodology as Pettengill, Sundaram and Mathur (1995). Unlike previous work, we find that beta is highly significant in explaining the cross‐section of UK stock returns and more importantly remains significant even when the Fama French factors are included in the cross‐sectional regressions. We also investigate whether higher co‐moments (co‐skewness and co‐kurtosis) have any explanatory power but find that empirical support is weaker.     

Stationary-Increment Variance-Gamma and t Models: Simulation and Parameter Estimation

We detail a method of simulating data from long range dependent processes with variance-gamma or t distributed increments, test various estimation procedures [method of moments (MOM), product-density maximum likelihood (PMLE), non-standard minimum χ² and empirical characteristic function estimation] on the data, and assess the performance of each. The investigation is motivated by the apparent poor performance of the MOM technique using real data ( Tjetjep & Seneta, 2006 ); and the need to assess the performance of PMLE for our dependent data models. In the simulations considered the product-density method performs favourably.     

Risk and return in the Tehran stock exchange

This paper analyzes market index returns in the Tehran stock exchange (TSE) within the context of three variants of the Capital Asset Pricing Model: the static international; the constant-parameter intertemporal; and a Markov-switching intertemporal CAPM, which allows for time-varying degree of integration with regional and international equity markets. We find that TSE returns are CAPM-efficient at monthly frequency with respect to several international market indices. Moreover, we find evidence in support of international integration of the TSE with respect to international markets. In addition, we conduct an extensive investigation for the direction of causality between TSE returns, international market index returns, and those in neighboring countries.     

Performance Evaluation,Portfolio Selection,and HARA Utility

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The main goal of this work is the generalization of the approach of Jobson and Korkie for funds performance evaluation. Therefore, the paper considers the portfolio selection problem of an investor who faces short sales restrictions when choosing among F different investment funds and assumes the investor’s utility function to be of the HARA type. A performance measure is developed and its relationship to previously proposed measures is discussed. Particular attention is given to the special case of cubic utility implying skewness preferences. Findings are illustrated by an empirical example.     

A Test for Symmetry with Leptokurtic Financial Data

Most of the tests for symmetry are developed under the (implicitor explicit) null hypothesis of normal distribution. As is wellknown, many financial data exhibit fat tails, and thereforecommonly used tests for symmetry (such as the standard test based on sample skewness) are not valid fortesting the symmetry of leptokurtic financial data. In particular,the test uses third moment, which may not be robust in presence of gross outliers. In this article wepropose a simple test for symmetry based on the Pearson typeIV family of distributions, which take account of leptokurtosisexplicitly. Our test is based on a function that is boundedover the real line, and we expect it to be more well behavedthan the test based on sample skewness (third moment). Resultsfrom our Monte Carlo study reveal that the suggested test performsvery well in finite samples both in terms of size and power.Simulation results also support our conjecture of the teststo be well behaved and robust to excess kurtosis. We apply thetest to some selected individual stock return data to illustrateits usefulness.