PL EN


2012 | 12 | 2 | 58-71
Article title

Selected Robust Methods for Camp Model Estimation

Title variants
Languages of publication
EN
Abstracts
EN
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 measures of variability we can overcome this lack of robustness and improve the reliability of the results.
Publisher
Year
Volume
12
Issue
2
Pages
58-71
Physical description
Dates
published
2012-12-01
online
2013-07-30
Contributors
  • University of Economics in Katowice, Faculty of Informatics and Communication Department of Demography and Economic Statistics Bogucicka 14, 40-226 Katowice, Poland, grazyna.trzpiot@ue.katowice.pl
References
  • Cornell, B. & Dietrich, J.K. (1978). Mean-Absolute-Deviation versus Least-Squares Regression Estimation of Beta Coefficients. Journal of Financial and Quantitative Analysis, 13, 123-131.
  • Fama, E. (1965). The Behavior of Stock Prices. Journal of Business, 38, 34-105.[Crossref]
  • Fox, J. (1991). Regression diagnostics. Newbury Park, C.A. Sage.
  • Huber, P. (1981). Robust Statistics. New York: John Wiley.
  • Koenker, R. (1982). Robust Methods in Econometrics. Econometric Reviews, 1, 213-255.
  • Koenker, R. & Bassett G. (1978). Regression Quantiles. Econometrica, 46, 33-50.
  • Kon, S. (1984). Models of Stock Returns - A Comparison. Journal of Finance, 39, 147-165.
  • Maddala, G.S. (2006), Ekonometria, Warszawa: Wydawnictwo Naukowe PWN.
  • Praetz, P. (1972). The Distribution of Share Price Changes. Journal of Business, 45, 49-55.
  • Rao, C.R. (1973). Linear Statistical Inference and Its Applications. New York: John Wiley.
  • Roll, R. (1988). R2. Journal of Finance, 43, 541-566.
  • Rousseeuw, P.J. & Leroy, A.M. (2003). Robust Regression and Outlier Detection, New York: John Wiley.
  • Ruppert, D. & Carroll, R. (1980). Trimmed Least Squares Estimation in the Linear Model. Journal of the American Statistical Association, 75, 828-838.
  • Sharpe, W. (1971). Mean-Absolute Deviation Characteristic Lines for Securities and Portfolios. Management Science, 18 B1-B13.
  • Trzpiot, G. (2011). Wybrane odporne metody estymacji beta. Studia Ekonomiczne 96, Uniwersytet Ekonomiczny w Katowicach, „Modelowanie preferencji a ryzyko ’11”, 133-148.
  • Trzpiot, G., (2008). Implementation of quantile regression methodology into VaR estimation. Studies and Papers No. 9, University of Szczecin, 316-323.
  • Trzpiot, G., (2007). Quantile regression and VaR estimation. Scientific Papers of Wroclaw Universityof Economics, 1176, 465-471.
  • Trzpiot, G. & Majewska, J. (2010). Estimation of Value at Risk: Extreme value and robust approaches. Operation Research and Decisions, Vol. 20, No. 1, Wrocław, 131-143.
  • Trzpiot, G. & Majewska, J. (2009). Sensitivity analysis of some robust estimators of volatility. Economics Studies 53, 91-108, Scientific Papers of Katowice Academy of Economics.
  • Welsh, A. (1987). The Trimmed Mean in the Linear Model. Annals of Statistics, 15, 20-36.
Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.doi-10_2478_v10031-012-0032-7
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.