Search results

The purpose of this paper is to investigate the return performance of different investment strategies in the hedge fund sector, with a particular emphasis on the recent US financial crisis of 2007‐2010. Additionally, the paper aims to investigate the comovement of hedge fund index returns.

The paper identifies broad hedge fund investment strategies using data from the Dow Jones Credit Suisse Hedge Fund Database. It examines the return comovement using the cross‐sectional volatility, covariance, and correlation metrics proposed in Adrian (2007). In addition, the paper examines whether correlations and covariance are important determinants of future volatility via traditional time‐series regressions.

The paper finds that the majority of the broad hedge fund investment strategies incurred record level losses and gains during the 2007‐2010 period. In addition, it finds that the crisis period was preceded by high correlations, attributed primarily to a rise in cross‐sectional hedge fund covariances. However, during the crisis period, a decrease in average correlations, stemming from an increase in hedge fund volatility, is documented. The time‐series regressions are supportive of a strong relationship between cross‐sectional covariances and subsequent volatility, suggesting that systemic risk occurs in the hedge fund sector when returns move significantly in dollar terms.

This study is one of the first investigations that focus on the comovement and volatility of hedge fund index returns during the US financial crisis of 2007‐2010.

We examine the incremental cross-sectional role of several common risk factors related to size, book-to-market, and momentum in size-and-momentum-sorted portfolios. Unlike the existing literature, which focuses on the conditional mean specification only, we evaluate the common risk factors’ incremental explanatory power in the cross-sectional characterization of both average return and conditional volatility. We also investigate the role of ex-ante market risk in the cross-section. The empirical results demonstrate that the size-and-momentum-based risk factors explain a significant portion of the cross-sectional average returns and cross-sectional conditional volatility of the benchmark equity portfolios. We find that the Fama–French (1993) factors and the ex-ante market risk are priced in the cross-sectional conditional volatility. We conclude that the size-and-momentum-based factors provide a source of risk that is independent of the Fama–French factors as well as ex-post and ex-ante market risk. Our results bolster the risk-based explanation of the size and momentum effects.

This paper investigates the role of economic disagreement in the cross-sectional pricing of individual stocks. Economic disagreement is quantified with ex ante measures of cross-sectional dispersion in economic forecasts from the Survey of Professional Forecasters (SPF), determining the degree of disagreement among professional forecasters over changes in economic fundamentals.

The authors introduce a broad index of economic disagreement based on the innovations in the cross-sectional dispersion of economic forecasts for output, inflation and unemployment so that the index is a shock measure that captures different aspects of disagreement over economic fundamentals and also reflects unexpected news or surprise about the state of the aggregate economy. After building the broad index of economic disagreement, the authors test out-of-sample performance of the index in predicting the cross-sectional variation in future stock returns.

Univariate portfolio analyses indicate that decile portfolios that are long in stocks with the lowest disagreement beta and short in stocks with the highest disagreement beta yield a risk-adjusted annual return of 7.2%. The results remain robust after controlling for well-known pricing effects. The results are consistent with a preference-based explanation that ambiguity-averse investors demand extra compensation to hold stocks with high disagreement risk and the investors are willing to pay high prices for stocks with large hedging benefits. The results also support the mispricing hypothesis that the high disagreement beta provides an indirect way to measure dispersed opinion and overpricing.

Most literature measures disagreement about individual stocks with the standard deviation of earnings forecasts made by financial analysts and examines the cross-sectional relation between this measure and individual stock returns. Unlike prior studies, the authors focus on disagreement about the economy instead of disagreement about earnings growth. The authors' argument is that disagreement about the economy is a major factor that would explain disagreement about stock fundamentals. The authors find that disagreement in economic forecasts does indeed have a significant impact on the cross-sectional pricing of individual stocks.

A new method of hollowing rapid prototype models based on STL models and their cross‐sectional contours is presented to meet the demands of hollowed prototypes in casting and rapid prototype manufacturing. Offsetting along the Z‐axis and cross‐sectional contour offsetting are employed to perform the hollowing operation. The process performs two‐dimensional Boolean operations on the polygons made by the offset contours of cross‐sectional contours instead of three‐dimensional offsetting of the STL models. This hollowing operation is especially suitable for hollowing STL models with free‐form surfaces. Detailed algorithms are described to generate the correct offset contours of an STL model. Adopting this method, the hollowing process is dramatically simplified and becomes more efficient. This method has been verified by practical case studies, and it is proved that this simplified hollowing operation can reduce the prototype build time and cost.

This paper aims to study the yarn cross-section shape which is a very important yarn physical parameter and has a dominant effect on the physical structure of the yarn. Four factors affecting the yarn cross section, i.e. twist multiplier, Roving hank, spinning system and doubling technique, were investigated.

In past researches, the yarn cross-sectional area was calculated by considering any one yarn radius giving the approximate yarn cross-sectional area by assuming the yarn as a circular one.

In this study, a testing instrument is fabricated as shown in Plates 1 and 2 for yarn cross-section measurement and a novel method for calculating the correct yarn cross-sectional area of the yarn was developed.

In the past, no such studies have been conducted on the yarn cross-section studies because of the various limitations of the yarn cross-section measuring or testing instruments.

This study examines the roles of cross-sectional dependence, asymmetric structure and country-to-country policy variations in the inflation-poverty reduction causal nexus in selected sub-Saharan African (SSA) countries from 1981 to 2019.

To account for cross-sectional dependence, heterogeneity and policy variations across countries in the inflation-poverty reduction causal nexus, this study uses robust Hatemi-J data decomposition procedures and a battery of second-generation techniques. These techniques include cross-sectional dependency tests, panel unit root tests, slope homogeneity tests and the Dumitrescu-Hurlin panel Granger non-causality approach.

Unlike existing studies, the panel and country-specific findings exhibit several dimensions of asymmetric causality in the inflation-poverty nexus. Positive inflationary shocks Granger-causes poverty reduction through investment and employment opportunities that benefit the impoverished in SSA. These findings align with country-specific analyses of Botswana, Cameroon, Gabon, Mauritania, South Africa and Togo. Also, a decline in poverty causes inflation to increase in the Congo Republic, Madagascar, Nigeria, Senegal and Togo. All panel and country-specific analyses reveal at least one dimension of asymmetric causality or another.

All stakeholders and policymakers must pay adequate attention to issues of asymmetric structures, nonlinearities and country-to-country policy variations to address country-specific issues and the socioeconomic problems in the probable causal nexus between the high incidence of extreme poverty and double-digit inflation rates in most SSA countries.

Studies on the inflation-poverty nexus are not uncommon in economic literature. Most existing studies focus on inflation’s effect on poverty. Existing studies that examine the inflation-poverty causal relationship covertly assume no asymmetric structure and nonlinearity. Also, the issues of cross-sectional dependence and heterogeneity are unexplored in the causal link in existing studies. All panel studies covertly impose homogeneous policies on countries in the causality. This study relaxes this supposition by allowing policies to vary across countries in the panel framework. Thus, this study makes three-dimensional contributions to increasing understanding of the inflation-poverty nexus.

One crucial but sometimes overlooked fact regarding the difference between observation in the cross-section and observation over time must be stated before proceeding further. Tempting though it is to draw conclusions about the dynamics of a process from cross-sectional observations taken as a snapshot of that process, it is a fallacious practice except under a very precise condition that is highly unlikely to obtain in processes of interest to the social scientist. That condition is known as ergodicity.

This paper analyzes the roles of idiosyncratic risk and firm-level conditional skewness in determining cross-sectional returns. It is shown that the traditional EGARCH estimates of conditional idiosyncratic volatility may bring significant finite sample estimation bias in the presence of non-Gaussianity. We propose a new estimator that has more robust sampling performance than the EGARCH MLE in the presence of heavy-tail or skewed innovations. Our cross-sectional portfolio analysis demonstrates that the idiosyncratic volatility puzzle documented by Ang, Hodrick, Xiang, and Zhang (2006) exists intertemporally. We conduct further analysis to solve the puzzle. We show that two factors idiosyncratic variance and individual conditional skewness play important roles in determining cross-sectional returns. A new concept, the “expected windfall,” is introduced as an alternate measure of conditional return skewness. After controlling for these two additional factors, we solve the major piece of this puzzle: Our cross-sectional regression tests identify a positive relationship between conditional idiosyncratic volatility and expected returns for over 99% of the total market capitalization of the NYSE, NASDAQ, and AMEX stock exchanges.

Portfolios of mortgage loans played an important role in the Great Recession and continue to compose a material part of bank assets. This chapter investigates how cross-sectional dependence in the underlying properties flows through to the loan returns, and thus, the risk of the portfolio. At one extreme, a portfolio of foreclosed mortgage loans becomes a portfolio of real estate whose returns exhibit substantial cross-sectional and spatial dependence. Near the other extreme, almost all loans perform and yield constant returns, which do not correlate with other performing loan returns. This suggests that loan performance effectively censors the random returns of the underlying properties. Following the statistical properties of the correlations among censored variables, the authors build off this foundation and show how the loan return correlations will rise as economic conditions deteriorate and the defaulting loans reveal the underlying housing correlations. In this chapter, the authors (1) adapt tools from spatial statistics to document substantial cross-sectional dependence across house price returns and examine the spatial structure of this dependence, (2) investigate the nonlinear nature of correlations among loan returns as a function of the default rate and the underlying house price correlations, and (3) conduct a simulation exercise using parameters from the empirical data to show the implications for holding a portfolio of mortgages.

This chapter advances a panel vector autoregressive/vector error correction model (PVAR/PVECM) framework for purposes of examining the sources and determinants of cross-country variations in macroeconomic performance using large cross-country data sets. Besides capturing the simultaneity of the potential determinants of cross-country variations in macroeconomic performance and carefully separating short- from long-run dynamics, the PVAR/PVECM framework advanced allows to capture a variety of other features typically present in cross-country macroeconomic data, including model heterogeneity and cross-sectional dependence. We use the PVAR/PVECM framework we advance to reexamine the dynamic interrelation between investment in physical capital and output growth. The empirical findings for an unbalanced panel of 90 countries over the time period from at most 1950 to 2000 suggest for most regions of the world surprisingly strong support for a long-run relationship between output and investment in physical capital that is in line with neoclassical growth theory. At the same time, the notion that there would be even a long-run (let alone short-run) causal relation between investment in physical capital and output (or vice versa) is strongly refuted. However, the size of the feedback from output growth to investment growth is estimated to strongly dominate the size of the feedback from investment growth to output growth.