335 research outputs found

    The informativeness of the technical conversion factor for the price ratio of processing livestock

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    The technical conversion factor (TCF) is a survey-based estimate of the percentage of carcass weight obtained per unit of live weight. Practitioners and researchers have used it to predict the corresponding price ratio (PR). We use both in-sample regressions and out-of-sample forecasting analysis to test the validity of this approach in case of predicting the price effects of processing livestock in Europe. By regressing the PR on the inverse value of the corresponding TCF for a large panel of European countries and animal types, we find a significant positive relation between these variables, which also has economic value in terms of improving out-of-sample forecasting precision. This result is shown to be robust to animal type, year, and country fixed effects. The TCF therefore has predictive value about the corresponding PR.(3

    Robust M-estimation of multivariate conditionally heteroscedastic time series models with elliptical innovations.

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    This paper proposes new methods for the econometric analysis of outlier contaminated multivariate conditionally heteroscedastic time series. Robust alternatives to the Gaussian quasi-maximum likelihood estimator are presented. Under elliptical symmetry of the innovation vector, consistency results for M-estimation of the general conditional heteroscedasticity model are obtained. We also propose a robust estimator for the cross-correlation matrix and a diagnostic check for correct specification of the innovation density function. In a Monte Carlo experiment, the effect of outliers on different types of M-estimators is studied. We conclude with a financial application in which these new tools are used to analyse and estimate the symmetric BEKK model for the 1980-2006 series of weekly returns on the Nasdaq and NYSE composite indices. For this dataset, robust estimators are needed to cope with the outlying returns corresponding to the stock market crash in 1987 and the burst of the dotcombubble in 2000.Concitional heteroscedasticity; M-estimators; Multivariate time series; Outliers; Quasi-maximum likelihood; Robust methods;

    The Gaussian rank correlation estimator: Robustness properties.

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    The Gaussian rank correlation equals the usual correlation coefficient computed from the normal scores of the data. Although its influence function is unbounded, it still has attractive robustness properties. In particular, its breakdown point is above 12%. Moreover, the estimator is consistent and asymptotically efficient at the normal distribution. The correlation matrix based on the Gaussian rank correlation is always positive semidefinite, and very easy to compute, also in high dimensions. A simulation study confirms the good efficiency and robustness properties of the proposed estimator with respect to the popular Kendall and Spearman correlation measures. In the empirical application, we show how it can be used for multivariate outlier detection based on robust principal component analysis.Breakdown; Correlation; Efficiency; Robustness; Van der Waerden;

    Estimation and decomposition of downside risk for portfolios with non-normal returns.

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    We propose a new estimator for Expected Shortfall that uses asymptotic expansions to account for the asymmetry and heavy tails in financial returns. We provide all the necessary formulas for decomposing estimators of Value at Risk and Expected Shortfall based on asymptotic expansions and show that this new methodology is very useful for analyzing and predicting the risk properties of portfolios of alternative investments.Alternative investments; Component expected shortfall; Cornish-Fisher expansion; Downside risk; Expected shortfall; Portfolio; Risk contribution; Value at risk;

    Hedge fund portfolio selection with modified expected shortfall

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    Modified Value-at-Risk (VaR) and Expected Shortfall (ES) are recently introduced downside risk estimators based on the Cornish-Fisher expansion for assets such as hedge funds whose returns are non-normally distributed. Modified VaR has been widely implemented as a portfolio selection criterion. We are the first to investigate hedge fund portfolio selection using modified ES as optimality criterion. We show that for the EDHEC hedge fund style indices, the optimal portfolios based on modified ES outperform out-of-sample the EDHEC Fund of Funds index and have better risk characteristics than the equal-weighted and Fund of Funds portfolios.portfolio optimization, modified expected shortfall, non-normal returns

    Estimation and decomposition of downside risk for portfolios with non-normal returns.

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    Modied Value at Risk (VaR) is an estimator of VaR based on the Cornish-Fisher expansion. It is fast to compute and reliable for non-normal returns. In this paper, we introduce modified Expected Shortfall as a new analytical estimator for Expected Shortfall (ES), another popular measure of downside risk. We give all the necessary formulas for computing portfolio modified VaR and ES and for decomposing these risk measures into the contributions made by each of the portfolio holdings. This new methodology is shown to be very useful for analyzing the risk properties of portfolios of alternative investments.

    Robust estimation of intraweek periodicity in volatility and jump detection.

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    Opening, lunch and closing of financial markets induce a periodic component in the volatility of high-frequency returns. We propose a non-parametric weighted standard deviation and parametric truncated maximum likelihood estimation procedure for the periodic component in volatility and show that they are robust to price jumps. We also show that robust periodicity estimates can be used to increase the accuracy of jump detection methods. We compare the classical and robust methods for the 5-minute EUR/USD returns. The robust intraweek periodicity estimates are lower than the classical ones on Tuesday-Friday 8:30-8:35 EST and Monday-Friday 10:00-10:05 EST. The higher values for the non-robust estimates are likely to be due to jumps. Accounting for the periodicity in the volatility of high-frequency returns is especially important to detect the relatively small jumps occurring at times for which volatility is periodically low and to reduce the number of spurious jump detections at times of periodically high volatility.High-frequency data; Jump detection; Periodicity; Robust statistics;

    Econometrics meets sentiment : an overview of methodology and applications

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    The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software

    The Peer Performance of Hedge Funds

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    An essential component in the analysis of (hedge) fund returns is to measure its performance with respect to the group of peer funds. Through the analysis of risk-adjusted return percentiles an answer is given to the question how many funds are out-performed by the focal fund. In case all funds perform equally well, this approach will lead a random number between zero and one, depending on how lucky the fund is. We use the false discovery rate approach to construct relative performance ratios that account for the uncertainty in estimating the performance differential of two funds. Our application is on hedge funds, which leads us to develop a test for equality of the modified Sharpe ratio of two funds. The effectiveness of the method is illustrated with a Monte Carlo study and an empirical study is performed on the Hedge Fund Research database
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