Non-parametric Econometric Models in Risk Analysis

• Autorzy: Dominik Krężołek
• Języki publikacji: EN

Abstrakty

EN

Econometric modelling is a statistical method used to estimate and analysethe relationships between economic variables. It is an important tool for empirical research in economics, finance, and business, and can be used to forecast future economic trends, evaluate policy interventions, and understand the behaviour of economic agents. Econometric models typically involve estimating the relationship between a dependent variable and one or more independent variables, using data collected over time or across individuals. These models can take a variety of forms, depending on the nature of the data and the research question being addressed [Madalla, 2006]. One common type of econometric model is a regression model, which estimates the relationship between a dependent variable and one or more independent variables using a linear or nonlinear equation. (fragment of text)

Słowa kluczowe

EN

Econometric models; Econometric modeling; Nonparametric estimation; Statistical models

PL

Modele ekonometryczne; Modelowanie ekonometryczne; Estymacja nieparametryczna; Modele statystyczne

• Czasopismo: Scientific Publications / University of Economics in Katowice
• Rocznik: 2023
• Tom: Multidimensional Data Modelling and Risk Analysis
• Strony: 40--59

Twórcy

autor

Dominik Krężołek

• Uniwersytet Ekonomiczny w Katowicach

Bibliografia

• Acerbi C., Tasche D. (2002), Expected Shortfall: A Natural Coherent Alternative to Value at Risk, "Economic Notes", Vol. 31(2), pp. 379-388.
• Artzner P., Delbaen F., Eber J.-M., Heath D. (1999), Coherent Measures of Risk, "Mathematical Finance", Vol. 9(3), pp. 203-228.
• Davidson R., MacKinnon J.G. (2004), Econometric Theory and Methods, Oxford University Press, Oxford.
• Denault M. (2001), Coherent Risk Measures and Their Applications in Financial Risk Management, "Risk Analysis", Vol. 21(3), pp. 433-447.
• Dowd K. (2005), Measuring Market Risk (2nd ed.), John Wiley & Sons, Chichester. Fan J., Yao Q. (2005), Nonlinear Time Series, Springer Series in Statistics, Springer, New York.
• Gilboa I. (2009), Theory of Decision under Uncertainty, Cambridge University Press, New York.
• Greene W.H. (2017), Econometric Analysis, Pearson Education Limited. Hayashi F. (2000), Econometrics, Princeton University Press.
• Henderson D.J., Parmeter C.F. (2015), Applied Nonparametric Econometrics, Cambridge University Press, Cambridge.
• Horowitz J.L. (1998), Semiparametric Methods in Econometrics, Springer-Verlag, New York.
• Ichimura H. (1993), Semiparametric Least Squares (SLS) and Weighted SLS Estimation of Single-index Models, "Journal of Econometrics", Vol. 58(1-2), pp. 71-120.
• Jajuga K. (2018), Zarządzanie ryzykiem, Wydawnictwo Naukowe PWN, Warszawa. Lee T.H., González-Rivera G. (2008), Nonparametric Estimation of Value-at-Risk Based on Extreme Value Theory, "Journal of Econometrics", Vol. 147(1), pp. 23-35.
• Li Q., Racine J.S. (2007), Nonparametric Econometrics: Theory and Practice, Princeton University Press, Princeton.
• Maddala G.S. (2006), Ekonometria, Wydawnictwo Naukowe PWN, Warszawa.
• McNeil A.J., Frey R., Embrechts P. (2015), Quantitative Risk Management: Concepts, Techniques and Tools-Revised Edition, Princeton University Press, Princeton.
• Pagan A., Ullah A. (1999), Nonparametric Econometrics, Cambridge University Press, New York.
• Parzen E. (1962), On Estimation of a Probability Density Function and Mode, "The Annals of Mathematical Statistics", No. 33, pp. 1065-1076.
• Poon S.-H., Granger C.W.J. (2003), Forecasting Volatility in Financial Markets: A Review, "Journal of Economic Literature", Vol. 41, No. 2, pp. 478-539.
• Rockafellar R.T., Uryasev S. (2002), Conditional Value-at-Risk for General Loss Distributions, "Journal of Banking and Finance", Vol. 26(7), pp. 1443-1471
• Rosenblatt M. (1956), Remarks on Some Nonparametric Estimates of a Density Function,"Annals of Mathematical Statistics", No. 27, pp. 832-377.
• Scott D.W. (2015), Multivariate Density Estimation: Theory, Practice, and Visualization, John Wiley & Sons, New York.
• Sheather S.J. (2004), Density Estimation, "Statistical Science", Vol. 19(4), pp. 588-597.
• Wand M.P., Jones M.C. (1995), Kernel Smoothing, (Vol. 60), CRC Press.
• Wang S., Wang X. (2013), The Statistical Properties of Value at Risk, "Journal of Financial Econometrics", Vol. 11(2), pp. 449-478.
• Xia Y., Tong H., Li W.K. (2012), A Review on Semiparametric Regression, "Annual Review of Statistics and Its Application", pp. 311-352.
• Yang F., Härdle W.K. (2007), Nonparametric Risk Management with General Risk Function, "Journal of Econometrics", Vol. 141(2), pp. 492-516.

Identyfikatory

DOI

10.22367/uekat.9788378758686