Prof. Haim Shalit
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Office Address: | Department of Economics Ben-Gurion University of the Negev P.O. Box 653 Beer-Sheva 84105, Israel |
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| Tel: | +972-8-647-2299 | ||
| Fax: | +972-8-647-2941 | ||
| E-Mail: | shalit@bgu.ac.il |
Haim Shalit's research is directed toward the use of the mean-Gini model, the Lorenz curve, and Marginal Conditional Stochastic Dominance in financial markets.
- Haim Shalit and Shlomo Yitzhaki, “How Does Beta Explain Stochastic Dominance Efficiency?Abstract:
Stochastic dominance rules provide necessary and sufficient conditions for characterizing efficient portfolios that suit all expected utility maximizers. For the finance practitioner, though, these conditions are not easy to apply or interpret. Portfolio selection models like the mean-variance model offer intuitive investment rules that are easy to understand, as they are based on parameters of risk and return. We present stochastic dominance rules for portfolio choices that can be interpreted in terms of simple financial concepts of systematic risk and mean return. Stochastic dominance is expressed in terms of Lorenz curves, and systematic risk is expressed in terms of Gini. To accommodate risk aversion differentials across investors, we expand the conditions using the extended Gini. ”, Review of Quantitative Finance and Accounting,Vol.35, No 4, November 2010, pp. 431-444 . - Haim Shalit, “Finding Better Securities while Holding PortfoliosAbstract:
Investment managers always look for securities to improve their portfolio performance, a common mechanism being the mean-variance (MV) model. As an alternative, I propose using Marginal Conditional Stochastic Dominance (MCSD). MCSD ensures that all risk-averse investors benefit from the selection process by establishing the relative preference among stocks conditional on holding a specific portfolio. I describe the basic MCSD rules and apply them to large porfolios. The resulting preferred stocks are compared to the selection obtained using the mean-variance criterion and the CAPM.”, Journal of Portfolio Management, Vol. 37, No. 1, Fall 2010, pp. 31–42. - Haim Shalit and Shlomo Yitzhaki, “Capital Market Equilibrium with Heterogeneous InvestorsAbstract:
As a two-parameter model that satisfies stochastic dominance, the mean-extended Gini model is used to build efficient portfolios. The model quantifies risk aversion heterogeneity in capital markets. In a simple Edgeworth box framework, we show how capital market equilibrium is achieved for risky assets. This approach provides a richer basis for analyzing the pricing of risky assets under heterogeneous preferences. Our main results are: (1) Identical investors, who use the same statistic to represent risk, hold identical portfolios of risky assets being equal to the market portfolio; and (2) heterogeneous investors as expressed by the variance or the extended Gini hold different risky assets in portfolios, and therefore, no one must hold the market portfolio. ”, Quantitative Finance, Vol.9, No 6, September 2009, p p. 757-766. - Sergio Ortobelli, Svetlozar Rachev, Haim Shalit, and Frank Fabozzi, “Orderings and Probability Functionals Consistent with PreferencesAbstract:
This paper unifies the classical theory of stochastic dominance and investor preferences with the recent literature on risk measures applied to the choice problem faced by investors. First we summarize the main stochastic dominance rules used in the finance literature. Then we discuss the connection with the theory of integral stochastic orders and we introduce orderings consistent with investors’ preferences. Thus, we classify them, distinguishing several categories of orderings associated with different classes of investors. Finally we show how we can use risk measures and orderings consistent with some preferences to determine the investors’ optimal choices. ”, Applied Mathematical Finance, Vol.16, No 1, 2009, pp. 81-102. - Sergio Ortobelli, Svetlozar Rachev, Haim Shalit, and Frank Fabozzi, “Orderings and Risk Probability Functionals in Portfolio TheoryAbstract:
”, Probability and Mathematical Statistics, Vol.28, No 2, 2008, pp. 203-234. - Dima Alberg, Haim Shalit, and Rami Yosef, “Estimating Stock Market Volatility using Asymmetric GARCH ModelsAbstract:
A comprehensive empirical analysis of the mean return and conditional variance of Tel Aviv Stock Exchange (TASE) indices is performed using various GARCH models. The prediction performance of these conditional changing variance models is compared to newer asymmetric GJR and APARCH models. We also quantify the day-of-the-week effect and the leverage effect and test for asymmetric volatility. Our results show that the asymmetric GARCH model with fat-tailed densities improves overall estimation for measuring conditional variance. The EGARCH model using a skewed Student-t distribution is the most successful for forecasting TASE indices.”, Applied Financial Economics, Vol.18, No 15, August 2008, pp. 1201- 1208. - Haim Shalit and Shlomo Yitzhaki, “The Mean-Gini Efficient Portfolio FrontierAbstract:
One main advantage of the mean-variance (MV) portfolio frontier is its simplicity and ease of derivation. A major shortcoming, however, lies in its familiar restrictions, such as the quadraticity of preferences or the normality of distributions. As a workable alternative to MV, we present the mean-Gini (MG) efficient portfolio frontier. Using an optimization algorithm, we compute MG and mean-extended Gini (MEG) efficient frontiers and compare the results to the MV frontier. MEG allows for the explicit introduction of risk-aversion in building the efficient frontier. For U.S. classes of assets, MG and MEG efficient portfolios constructed using Ibbotson monthly returns appear to be more diversified than MV portfolios. When short sales are allowed, distinct investor risk-aversions lead to different patterns of portfolio diversification, a result that is less obvious when short sales are foreclosed. Furthermore, we derive analytically the MG efficient portfolio frontier by restricting asset distributions. The MG frontier derivation is identical in structure to that of the MV-efficient frontier derivation. The penalty paid for simplifying the search for the MG efficient frontier is the loss of some information about the distribution of assets. ”, The Journal of Financial Research, 28, No 1, Spring 2005, pp. 59-75. - Haim Shalit and Shlomo Yitzhaki, “An Asset Allocation Puzzle: CommentAbstract:
This note looks at the rationale behind popular advice on portfolio allocation among cash, bonds, and stocks and proposes an additional solution to the asset allocation puzzle posed by Canner, Mankiw and Weil (1997) who show that popular advice contradicts financial theory. We offer a rational model based on stochastic dominance to demonstrate that most popular advice portfolios belong to the efficient set for all risk-averse investors. Hence, we cannot come to the conclusion that investment bankers are offering bad advice. On the contrary, our results show that advisors, acting as agents for numerous clients, recommend portfolios that are not inefficient for all risk- averse investors.”, American Economic Review, Vol. 93, No. 3, June 2003, pp. 1002-1008. - Haim Shalit and Shlomo Yitzhaki, “Estimating BetaAbstract:
This paper presents evidence that Ordinary Least Squares estimators of beta coefficients of major firms and portfolios are highly sensitive to observations of extremes in market index returns. This sensitivity is rooted in the inconsistency of the quadratic loss function in financial theory. By introducing considerations of risk aversion into the estimation procedure using alternative estimators derived from Gini measures of variability one can overcome this lack of robustness and improve the reliability of the results.”, Review of Quantitative Finance and Accounting, Vol. 18, No. 2 March 2002, pp. 95-118. - Russell Gregory-Allen and Haim Shalit, “The Estimation of Systematic Risk under Differentiated Risk Aversion: A Mean-Extended Gini ApproachAbstract:
This paper examines a mean-Gini model of systematic risk estimation that resolves some econometric problems with mean-variance beta estimation and allows for heterogeneous risk aversion across investors. Using the mean-extended Gini (MEG) model, we estimate systematic risks for different degrees of risk aversion. MEG betas are shown to be instrumental variable estimators that provide econometric solutions to biases generated by the estimation of mean-variance (MV) betas. When security returns are not normally distributed, MEG betas are proved to differ from MV betas. We design an econometric test that assesses whether these differences are significant. As an application using daily returns, we estimate MEG and MV betas for U.S. securities.”, Review of Quantitative Finance and Accounting,Vol 12, No. 2 , March 1999, pp. 135-157. - Haim Shalit, “Mean-Gini Hedging in Futures MarketsAbstract:
”, The Journal of Futures Markets, Vol 15, No. 6, September 1995, pp. 617-635. - Haim Shalit, “Mean-Gini Analysis of Stochastic Externalities: the Case of Groundwater ContaminationAbstract:
The mean-Gini model is used to analyze stochastic externalities generated by agricultural production. The model addresses the problem of groundwater pollution caused by excessive fertilizer application. Inherent in the mean-Gini approach to expected utility maximization is a two-fold value: the simplicity of the two-parameter mean-variance model and satisfaction of necessary and sufficient conditions for stochastic dominance. Price and quantity policy recommendations to control externalities are formulated based upon the relative assessment of uncertainty by the regulatory authority and the farmers. Using the Gini as a measure of risk allows for the quantification of control policy measures under differentiated risk aversion and multiple sources of pollution. The model shows that when producers underestimate uncertainty, quota policies restricting fertilizer are more efficient than tax policies in reducing groundwater contamination.”, Environmental and Resource Economics, Vol 6, No. 1, 1995, pp. 37-52. - Haim Shalit and Shlomo Yitzhaki, “Marginal Conditional Stochastic Dominance”, Management Science, Vol 40, No. 5, May 1994, pp. 670-684.
- Amos Golan and Haim Shalit, “Wine Quality Differentials in Hedonic Grape Pricing”, Journal of Agricultural Economics, Vol 44, No. 2, May 1993, pp. 311-321.
- Haim Shalit and Shlomo Yitzhaki, “Evaluating the Mean-Gini Approach to Portfolio Selection”, International Journal of Finance, Vol 1, No. 2, Spring 1989, pp. 15-31.
- Yakir Plessner and Haim Shalit, “Inflation, the Level of Investment, and Interest Rates”, European Economic Review, Vol. 30, No.4, December 1986, pp. 1169-1187.
- Haim Shalit, “Calculating the Gini Index of Inequality for Individual Data”, Oxford Bulletin of Economics and Statistics, Vol 47, No. 2, May 1985, pp. 185-189.
- Rafi Melnick and Haim Shalit, “Estimating the Market for Tomatoes”, American Journal of Agricultural Economics, Vol. 67, No. 3, August 1985, pp. 573-582.
- Haim Shalit and Shlomo Yitzhaki, “Mean-Gini, Portfolio Theory, and the Pricing of Risky Assets”, Journal of Finance, Vol. 39, No. 5, December 1984, pp. 1449-1468.
- Haim Shalit, “Does it Pay to Stabilize the Price of Vegetables?: An Empirical Evaluation of Agricultural Price Policies”, European Review of Agricultural Economics, Vol. 11, No. 1, 1984, pp. 1-16.
- Haim Shalit and Andrew Schmitz, “Farmland Price Behavior and Credit Allocation”, Western Journal of Agricultural Economics, Vol. 9, No. 2, December 1984, pp. 303-313.
- Shlomo Yitzhaki and Haim Shalit, “Efficient Portfolios on the Tel-Aviv Stock Exchange”, Bank of Israel Review, Vol. 58, August 1984, pp. 51-62, English Version August 1986.
- David Bigman and Haim Shalit, “Applied Welfare Analysis for Consumers with Commodity Income”, De Economist, Vol. 131, No. 1, April 1983, pp. 31-45.
- Uri Regev, Haim Shalit, and A.P. Gutierrez, “On the Optimal Allocation of Pesticides with Increasing Resistance: the Case of Alfalfa Weevil”, Journal of Environmental Economics and Management, Vol. 10, No. 1, March 1983, pp. 86-100.
- Haim Shalit and Andrew Schmitz, “Farmland Accumulation and Prices”, American Journal of Agricultural Economics, Vol. 64, No. 4, November 1982, pp. 710-719.
- Andrew Schmitz, Haim Shalit, and Stephen J. Turnovsky, “Producer Welfare and the Preference for Price Stability”, American Journal of Agricultural Economics, Vol. 63, No. 1, February 1981, pp. 157-160.
- Haim Shalit, “The Democratic Provision of Public and Private Goods from Exhaustible Resources”, Journal of Environmental Economics and Management, Vol. 7, No. 2, June 1980, pp. 81-89.
- Stephen J. Turnovsky, Haim Shalit, and Andrew Schmitz, “Consumer's Surplus, Price Instability, and Consumer WelfareAbstract:
”, Econometrica, Vol. 48, No. 1, January 1980, 135-152. - Andrew P. Gutierrez, Uri Regev, and Haim Shalit, “An Economic Optimization Model of Pesticide Resistance: Alfalfa and Egyptian Alfalfa Weevil- An Example”, Environmental Entomology, Vol. 8, 1979, pp. 101-107.
- Uri Regev, Haim Shalit, and Andrew P. Gutierrez, “Economic Conflicts in Plant Protection: the Problems of Pesticide Resistance”, Pest Management, G.A. Norton and C.S. Holling, eds., Pergamon Press, Oxford, England, 1979, pp. 281-299.
- רפי מלניק וחיים שליט, ”ניתוח שוק העגבניות בישראל ואומדנו“, עיונים בכלכלה, 1981, עמ'283-292.
- שלמה יצחקי וחיים שליט, ”על תיקי השקעה יעילים בבורסה בתל אביב“, סקר בנק ישראל, מס'58, 1984, עמ'51-62.
- עמוס גולן וחיים שליט, ”תמחור הענבים על פי איכות היין“, , עיונים בכלכלה בעריכת איתן ברגלס ומרדכי פיין , 1986, עמ' 225-240.
- דימה אלברג, רמי יוסף וחיים שליט, ”אמידה וחיזוי תנודתיות מדדי מניות ת"א 25 ות"א 100“, רבעון לבנקאות, מס' 164, יוני 2008, עמ' 77-93.
- Arie Preminger and Haim Shalit, “Normality Is a Necessary and Sufficient Condition for OLS to Yield Robust ResultsAbstract:
Yitzhaki (1996) showed that the OLS estimator is a weighted average of the slopes defined by adjacent observations. The weights depend only on the distribution of the independent variable. In this note, we show that equal weights can only be obtained if, and only if the independent variable is normally distributed. This may serve as the basis for a new test for normality.”, 2002. - Doron Greenberg and Haim Shalit, “Hedging with Stock Index Options: A Mean-Extended Gini ApproachAbstract:
One of the more efficient methods to hedge portfolios of securities whose put options are not traded is to use stock index options. We use the mean-extended Gini (MEG) model to derive the optimal hedge ratios for stock index options. We calculate the minimum-variance hedge ratios and compare them to the mean-extended Gini ratios for some main stocks traded on the Tel Aviv Stock Exchange. For each value of risk aversion, MEG hedge ratios combine systematic risk with basis risk Our results show that increasing risk aversion reduces the size of the hedge ratio, implying that less put options are needed to hedge each and every security.”, 2009. - Haim Shalit, “Using OLS to Test for NormalityAbstract:
Yitzhaki (1996) showed that the OLS estimator of the slope coefficient in a simple regression is a weighted average of the slopes delineated by adjacent observations. The weights depend only on the distribution of the independent variable. In this paper I demonstrate that equal weights can only be obtained if and only if the independent variable is normally distributed. This necessary and sufficient condition is used to develop a new test for normality which is distribution free and not sensitive to outliers. The test is compared with standard normality tests, in particular, the popular Jarque-Bera test. It is shown that the new test provides a better power for testing normality against all classes of alternative distributions. Finally, the test is applied to check normality in time-series data from major international financial markets.”, 2009. - Haim Shalit, “Portfolio Risk Management Using the Lorenz CurveAbstract:
This paper compiles the risk measures associated with the Lorenz curve. The Lorenz curve is the main tool in economics for measuring income distribution and inequality. For the past decades some of the Lorenz curve spin-offs have been used in risk analysis and finance. In particular, the Lorenz curve addresses the concepts of second degree stochastic dominance, Gini’s mean difference, Conditional Value-at-Risk, and the extended Gini in portfolio theory and in investment practice. Because the Lorenz curve can be estimated from asset returns, the risk measures are easy to implement and use.”, 2010.