Financial Econometrics I ( FAP, CIMA)

See semester study programme.

The main goal of the course is to present a basic range of econometric tools used in financial data modeling. Special attention will be paid to the concept of financial time series. The issues cover tools for testing the market efficiency hypothesis, building and estimation of univariate linear and non-linear models used for the description of conditional mean and variance of return time series, as well as methods of high frequency data modeling. Additional target of the lecture is to present empirical examples of the models with the application of selected econometric software packages.

Financial econometrics is concerned with the application of quantitative methods in finance. The main aim of the course is to present econometric tools applied to description and forecasting of price and return time series of financial instruments and indices. These are often recorded at a very high frequency, are non-stationary, autocorrelated and characterized by a time-varying variance. The methods presented during the course serve as a starting point for more complex research, e.g., on financial efficiency, portfolio analysis, risk valuation or pricing of financial instruments.

Literatura podstawowa:

Zbigniew Krysiak, Financial Engineering in the Project Development, Warsaw School of Economics, June 2015; Roman Kozhan, Financial Econometrics With EViews, Roman Kozhan & Ventus Publishing, 2010; Zbigniew Krysiak, Portfolio Development at Risk, Warsaw School of Economics, June 2015;

Literatura uzupełniająca:

J.Brzeszczyński, R.Kelm, Ekonometryczne modele rynków finansowych, WIG Press, Warszawa 2002; J.Y.Campbell, A.W.Lo, A.C.MacKinlay, The Econometrics of Financial Markets, Princeton University Press, New Jersey 1997; E.Elton, M.Gruber, S.Brown, W.Goetzman, Modern Portfolio Theory and Investment Analysis, John Wiley&Sons 2007; J.D.Hamilton, Time Series Analysis, Princeton University Press 1994; M.Rubaszek, D.Serwa, W.Marcinkowska-Lewandowska (red. nauk.), Analiza kursu walutowego, Wyd. Beck, Warszawa 2009; R.S.Tsay, Analysis of Financial Time Series, Wiley 2005. G. Loeffler, P. Posch (2011), Credit Risk Modelling Using Excel and VBA with DVD, Wiley. Lando D. (2004) Credit risk modeling, Princeton Series in Finance. Matuszyk A. (2008) Credit scoring, CeDeWu.

Kryteria oceniania:

egzamin testowy: 100.00%

See semester study programme.

The main goal of the course is to present a basic range of econometric tools used in financial data modeling. Special attention will be paid to the concept of financial time series. The issues cover tools for testing the market efficiency hypothesis, building and estimation of univariate linear and non-linear models used for the description of conditional mean and variance of return time series, as well as methods of high frequency data modeling. Additional target of the lecture is to present empirical examples of the models with the application of selected econometric software packages.

Financial econometrics is concerned with the application of quantitative methods in finance. The main aim of the course is to present econometric tools applied to description and forecasting of price and return time series of financial instruments and indices. These are often recorded at a very high frequency, are non-stationary, autocorrelated and characterized by a time-varying variance. The methods presented during the course serve as a starting point for more complex research, e.g., on financial efficiency, portfolio analysis, risk valuation or pricing of financial instruments.

Literatura podstawowa:

Zbigniew Krysiak, Financial Engineering in the Project Development, Warsaw School of Economics, June 2015; Roman Kozhan, Financial Econometrics With EViews, Roman Kozhan & Ventus Publishing, 2010; Zbigniew Krysiak, Portfolio Development at Risk, Warsaw School of Economics, June 2015;

Literatura uzupełniająca:

J.Brzeszczyński, R.Kelm, Ekonometryczne modele rynków finansowych, WIG Press, Warszawa 2002; J.Y.Campbell, A.W.Lo, A.C.MacKinlay, The Econometrics of Financial Markets, Princeton University Press, New Jersey 1997; E.Elton, M.Gruber, S.Brown, W.Goetzman, Modern Portfolio Theory and Investment Analysis, John Wiley&Sons 2007; J.D.Hamilton, Time Series Analysis, Princeton University Press 1994; M.Rubaszek, D.Serwa, W.Marcinkowska-Lewandowska (red. nauk.), Analiza kursu walutowego, Wyd. Beck, Warszawa 2009; R.S.Tsay, Analysis of Financial Time Series, Wiley 2005. G. Loeffler, P. Posch (2011), Credit Risk Modelling Using Excel and VBA with DVD, Wiley. Lando D. (2004) Credit risk modeling, Princeton Series in Finance. Matuszyk A. (2008) Credit scoring, CeDeWu.

Kryteria oceniania:

egzamin testowy: 100.00%

See semester study programme.

The main goal of the course is to present a basic range of econometric tools used in financial data modeling. Special attention will be paid to the concept of financial time series. The issues cover tools for testing the market efficiency hypothesis, building and estimation of univariate linear and non-linear models used for the description of conditional mean and variance of return time series, as well as methods of high frequency data modeling. Additional target of the lecture is to present empirical examples of the models with the application of selected econometric software packages.

Financial econometrics is concerned with the application of quantitative methods in finance. The main aim of the course is to present econometric tools applied to description and forecasting of price and return time series of financial instruments and indices. These are often recorded at a very high frequency, are non-stationary, autocorrelated and characterized by a time-varying variance. The methods presented during the course serve as a starting point for more complex research, e.g., on financial efficiency, portfolio analysis, risk valuation or pricing of financial instruments.

Literatura podstawowa:

Zbigniew Krysiak, Financial Engineering in the Project Development, Warsaw School of Economics, June 2015; Roman Kozhan, Financial Econometrics With EViews, Roman Kozhan & Ventus Publishing, 2010; Zbigniew Krysiak, Portfolio Development at Risk, Warsaw School of Economics, June 2015;

Literatura uzupełniająca:

J.Brzeszczyński, R.Kelm, Ekonometryczne modele rynków finansowych, WIG Press, Warszawa 2002; J.Y.Campbell, A.W.Lo, A.C.MacKinlay, The Econometrics of Financial Markets, Princeton University Press, New Jersey 1997; E.Elton, M.Gruber, S.Brown, W.Goetzman, Modern Portfolio Theory and Investment Analysis, John Wiley&Sons 2007; J.D.Hamilton, Time Series Analysis, Princeton University Press 1994; M.Rubaszek, D.Serwa, W.Marcinkowska-Lewandowska (red. nauk.), Analiza kursu walutowego, Wyd. Beck, Warszawa 2009; R.S.Tsay, Analysis of Financial Time Series, Wiley 2005. G. Loeffler, P. Posch (2011), Credit Risk Modelling Using Excel and VBA with DVD, Wiley. Lando D. (2004) Credit risk modeling, Princeton Series in Finance. Matuszyk A. (2008) Credit scoring, CeDeWu.

Kryteria oceniania:

egzamin testowy: 100.00%

See semester study programme.

The main goal of the course is to present a basic range of econometric tools used in financial data modeling. Special attention will be paid to the concept of financial time series. The issues cover tools for testing the market efficiency hypothesis, building and estimation of univariate linear and non-linear models used for the description of conditional mean and variance of return time series, as well as methods of high frequency data modeling. Additional target of the lecture is to present empirical examples of the models with the application of selected econometric software packages.

Financial econometrics is concerned with the application of quantitative methods in finance. The main aim of the course is to present econometric tools applied to description and forecasting of price and return time series of financial instruments and indices. These are often recorded at a very high frequency, are non-stationary, autocorrelated and characterized by a time-varying variance. The methods presented during the course serve as a starting point for more complex research, e.g., on financial efficiency, portfolio analysis, risk valuation or pricing of financial instruments.

Literatura podstawowa:

Zbigniew Krysiak, Financial Engineering in the Project Development, Warsaw School of Economics, June 2015; Roman Kozhan, Financial Econometrics With EViews, Roman Kozhan & Ventus Publishing, 2010; Zbigniew Krysiak, Portfolio Development at Risk, Warsaw School of Economics, June 2015;

Literatura uzupełniająca:

J.Brzeszczyński, R.Kelm, Ekonometryczne modele rynków finansowych, WIG Press, Warszawa 2002; J.Y.Campbell, A.W.Lo, A.C.MacKinlay, The Econometrics of Financial Markets, Princeton University Press, New Jersey 1997; E.Elton, M.Gruber, S.Brown, W.Goetzman, Modern Portfolio Theory and Investment Analysis, John Wiley&Sons 2007; J.D.Hamilton, Time Series Analysis, Princeton University Press 1994; M.Rubaszek, D.Serwa, W.Marcinkowska-Lewandowska (red. nauk.), Analiza kursu walutowego, Wyd. Beck, Warszawa 2009; R.S.Tsay, Analysis of Financial Time Series, Wiley 2005. G. Loeffler, P. Posch (2011), Credit Risk Modelling Using Excel and VBA with DVD, Wiley. Lando D. (2004) Credit risk modeling, Princeton Series in Finance. Matuszyk A. (2008) Credit scoring, CeDeWu.

Kryteria oceniania:

egzamin testowy: 100.00%