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32
Do Hedge Fund Managers Misreport Returns? Evidence from the Pooled Distribution
 Journal of Finance
, 2009
"... Evidence from the Pooled Distribution We find a significant discontinuity in the pooled distribution of reported hedge fund returns: the number of small gains far exceeds the number of small losses. The discontinuity is present in live funds, defunct funds, and funds of all ages, suggesting that it ..."
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Cited by 42 (1 self)
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Evidence from the Pooled Distribution We find a significant discontinuity in the pooled distribution of reported hedge fund returns: the number of small gains far exceeds the number of small losses. The discontinuity is present in live funds, defunct funds, and funds of all ages, suggesting that it is not caused by database biases. The discontinuity is absent in the three months culminating in an audit, funds that invest in liquid assets, and hedge fund risk factors, suggesting that it is generated neither by the skill of managers to avoid losses nor by nonlinearities in hedge fund asset returns. A remaining explanation is that hedge fund managers avoid reporting losses to attract and retain investors. Hedge funds are currently attracting a great deal of attention from investors, academics, and regulators for a number of reasons, but primarily due to the returns that hedge fund managers report. Investors want to share in the riches, academics want to understand the underlying risk factors, and regulators are concerned about the potential for fraud. Some members of the SEC support additional regulation of hedge funds, and
2006: A novel nonparametric density estimator
 The University of Queensland
"... We present a novel nonparametric density estimator and a new datadriven bandwidth selection method with excellent properties. The approach is inspired by the principles of the generalized cross entropy method. The proposed density estimation procedure has numerous advantages over the traditional ..."
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Cited by 12 (0 self)
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We present a novel nonparametric density estimator and a new datadriven bandwidth selection method with excellent properties. The approach is inspired by the principles of the generalized cross entropy method. The proposed density estimation procedure has numerous advantages over the traditional kernel density estimator methods. Firstly, for the first time in the nonparametric literature, the proposed estimator allows for a genuine incorporation of prior information in the density estimation procedure. Secondly, the approach provides the first datadriven bandwidth selection method that is guaranteed to provide a unique bandwidth for any data. Lastly, simulation examples suggest the proposed approach outperforms the current state of the art in nonparametric density estimation in terms of accuracy and reliability.
Automatic Random Variate Generation For Simulation Input
, 2000
"... We develop and evaluate algorithms for generating random variates for simulation input. One group called automatic, or blackbox algorithms can be used to sample from distributions with known density. They are based on the rejection principle. The hat function is generated automatically in a setup s ..."
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Cited by 12 (0 self)
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We develop and evaluate algorithms for generating random variates for simulation input. One group called automatic, or blackbox algorithms can be used to sample from distributions with known density. They are based on the rejection principle. The hat function is generated automatically in a setup step using the idea of transformed density rejection. There the density is transformed into a concave function and the minimum of several tangents is used to construct the hat function. The resulting algorithms are not too complicated and are quite fast. The principle is also applicable to random vectors. A second group of algorithms is presented that generate random variates directly from a given sample by implicitly estimating the unknown distribution. The best of these algorithms are based on the idea of naive resampling plus added noise. These algorithms can be interpreted as sampling from the kernel density estimates. This method can be also applied to random vectors. There it can be interpreted as a mixture of naive resampling and sampling from the multinormal distribution that has the same covariance matrix as the data. The algorithms described in this paper have been implemented in ANSI C in a library called UNURAN which is available via anonymous ftp.
Nonparametric Estimation of the Limit Dependence Function of Multivariate Extremes
 Extremes
, 1999
"... Abstract. This paper presents a new estimation procedure for the limit distribution of the maximum of a multivariate random sample. This procedure relies on a new and simple relationship between the copula of the underlying multivariate distribution function and the dependence function of its maximu ..."
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Cited by 10 (1 self)
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Abstract. This paper presents a new estimation procedure for the limit distribution of the maximum of a multivariate random sample. This procedure relies on a new and simple relationship between the copula of the underlying multivariate distribution function and the dependence function of its maximum attractor. The obtained characterization is then used to define a class of kernelbased estimates for the dependence function of the maximum attractor. The consistency and the asymptotic distribution of these estimates are considered.
Variable kernel estimates: On the impossibility of tuning the parameters
 in: HighDimensional Probability II, (edited by
, 2000
"... ABSTRACT For the standard kernel density estimate, it is known that one can tune the bandwidth such that the expected L1 error is within a constant factor of the optimal L1 error (obtained when one is allowed to choose the bandwidth with knowledge ofthe density). In this paper, we pose the same prob ..."
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Cited by 9 (1 self)
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ABSTRACT For the standard kernel density estimate, it is known that one can tune the bandwidth such that the expected L1 error is within a constant factor of the optimal L1 error (obtained when one is allowed to choose the bandwidth with knowledge ofthe density). In this paper, we pose the same problem for variable bandwidth kernel estimates where the bandwidths are allowed to depend upon the location. We show in particular that for positive kernels on the real line, for any databased bandwidth, there exists a density for which the ratio of expected L1 error over optimal L1 error tends to infinity. Thus, the problem oftuning the variable bandwidth in an optimal manner is “too hard”. Moreover, from the class ofcounterexamples exhibited in the paper, it appears that placing conditions on the densities (monotonicity, convexity, smoothness) does not help. 1
Consistency of Asymmetric Kernel Density Estimators and Smoothed Histograms with Application to Income Data. DP 0306, Institut de Statistique, Universite Catholique de Louvain
, 2003
"... We consider asymmetric kernel density estimators and smoothed histograms when the unknown probability density function f is dened on [0;+1). Uniform weak consistency on each compact set in [0;+1) is proved for these estimators when f is continuous on its support. Weak convergence in L1 is also est ..."
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Cited by 6 (1 self)
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We consider asymmetric kernel density estimators and smoothed histograms when the unknown probability density function f is dened on [0;+1). Uniform weak consistency on each compact set in [0;+1) is proved for these estimators when f is continuous on its support. Weak convergence in L1 is also established. We further prove that the asymmetric kernel density estimator and the smoothed histogram converge in probability to innity at x = 0 when the density is unbounded at x = 0. Monte Carlo results and an empirical study of the shape of a highly skewed income distribution based on a large microdata set are nally provided. Key words and phrases: Asymmetric kernel, smoothed histogram, density estimation, weak convergence, L1 consistency, unbounded density, boundary bias, income distribution, inequality measurement. JEL Classication: C13,C14. MSC 2000: 62G07, 62G08 We would like to thank O. Linton and the three referees for constructive criticism, as well as M.
Inequalities For A New DataBased Method For Selecting Nonparametric Density Estimates
 in M.L. Puri (editor), Festschrift in Honour of George Roussas, VSP International Science Publishers
, 1998
"... We continue the development of a method for the selection of a bandwidth or a number of design parameters in density estimation. We provide explicit nonasymptotic densityfree inequalities that relate the L 1 error of the selected estimate with that of the best possible estimate, and study in parti ..."
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Cited by 5 (4 self)
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We continue the development of a method for the selection of a bandwidth or a number of design parameters in density estimation. We provide explicit nonasymptotic densityfree inequalities that relate the L 1 error of the selected estimate with that of the best possible estimate, and study in particular the connection between the richness of the class of density estimates and the performance bound. For example, our method allows one to pick the bandwidth and kernel order in the kernel estimate simultaneously and still assure that for all densities, the L 1 error of the corresponding kernel estimate is not larger than about three times the error of the estimate with the optimal smoothing factor and kernel plus a constant times p log n=n, where n is the sample size, and the constant only depends on the complexity of the family of kernels used in the estimate. Further applications include multivariate kernel estimates, transformed kernel estimates, and variable kernel estimates.
The Annals of Statistics
 Annals of Statistics
, 1997
"... this paper, a "cleaner" related estimate is proposed, and explicit nonasymptotic performance guarantees are provided that are uniform over all f. Received June 1996; revised June 1997 ..."
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Cited by 2 (0 self)
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this paper, a "cleaner" related estimate is proposed, and explicit nonasymptotic performance guarantees are provided that are uniform over all f. Received June 1996; revised June 1997