Quantitative methods in management 2600-MFBRz1MIZF

1) Characteristics of financial time series (stock market indicators as a specific type of financial time series, rate of return - its characteristics and properties).

2) Adaptive forecasting methods for financial time series (naïve methods, moving averages, ex-post errors)

3) Exponential smoothing models (Brown's model, Holt's model, Winter’s model - choice of parameters in models)

4) Using regression models to estimate forecast of values in financial time series (fitting an appropriate analytical form of model, ex-ante errors)

5) Time series decomposition (seasonality indicators in additive and multiplicative approach using binary variables)

6) The concept of a stochastic process. Stationary and non-stationary economic time series (types of stationarity, autocorrelation functions, non-stationarity - test methods: DF test, ADF, Hasza DF, KPSS, Philips - Peron test with structural changes),

7) Selected econometric time series models (random walk process, random walk with drift, random walk with drift and trend, autoregressive process, autoregressive moving average process, autoregressive integrated moving average process).

8) Forecasting based on autoregressive models (estimation and verification of AR, MA, ARMA, ARIMA models).

9) Testing the long-run relationship between selected financial time series and spurious regression.

10) Analysis of volatility in financial time series: GARCH family models (testing the ARCH effect, modifications of the GARCH model)

Term 2025Z:

1 Characteristics of financial time series (stock market indices as a specific type of
financial time series, rate of return and its characteristics, analysis of return properties in financial time series).
2 Adaptive forecasting methods for financial time series (naïve methods, moving averages, ex post forecast errors, determination and optimization of weights using Solver).
3 Exponential smoothing models (Brown, Holt, and Winters models; parameter selection).
4 Application of analytical methods to forecasting values in financial time series (cause-and-effect models, selection of appropriate functional forms, ex ante forecast errors).
5 Time series decomposition (seasonality indices in additive and multiplicative models using dummy variables).
6 The concept of a stochastic process. Stationary and non-stationary economic time series (strict and weak stationarity, autocorrelation function properties, non-stationarity testing: DF, ADF, DF-GLS, KPSS, Phillips-Perron tests including structural breaks).
7 Selected economic time series models (white noise process, random walk, moving average process, autoregressive process, ARMA, ARIMA).
8 Forecasting based on autoregressive models (construction, estimation, and validation of AR, MA, ARMA, and ARIMA models).
9 Analysis of long-run relationships between selected time series (spurious regression).
10 Volatility analysis in financial time series: GARCH family models (ARCH effect testing and GARCH model modifications).

Course coordinators

Rafał Zbyrowski

Type of course

obligatory courses

Learning outcomes

Correctly uses terminology related to quantitative research methodology in financial management (K_W01).
Distinguishes different types of financial time series, including stock market indices (K_W01).
Explains the characteristics of rates of return and their significance in financial time series analysis (K_W01).
Explains the concept of a stochastic process and the differences between strict and weak stationarity (K_W01).
Identifies a white noise process within financial time series (K_W01).
Explains the concept of volatility in financial time series (K_W01).

Calculates rates of return for selected time series (K_U01).
Analyzes market data in the context of financial time series (K_U02).
Calculates ex post forecast errors for financial time series (K_U02).
Estimates future values of financial time series using exponential smoothing models (K_U01).
Interprets the impact of quantitative analyses and research on financial management in organizations (K_U02).
Interprets forecasts obtained from analytical methods applied to financial time series (K_U02).
Interprets stationarity test results including structural changes in data (K_U02).
Analyzes complex and non-standard problems related to business finance and accounting, such as forecasting financial time series and volatility modeling (K_U06).
Analyzes regression results with regard to long-run relationships (K_U06).
Presents the results of financial analyses (K_U06).

Evaluates complex phenomena related to finance and accounting in organizations using business data analysis (K_K01).

Assessment criteria

Lectures: T - Final examination,
Classes: pass/fail

Bibliography

Compulsory literature:

1. Mills T.C.: The econometric Modeling of Financial Time Series. Cambridge University Press. Cambridge 2004
2. Brooks C: Introductory Econometrics for Finance. Second Edition, Cambridge University Press. Cambridge, 2008
3. Witkowska D.,Matuszewska A.,Kompa K.: Wprowadzenie do ekonometrii dynamicznej i finansowej. Wydawnictwo SGGW. Warszawa 2008
4. Borkowski B, Dudek H., Szczesny W.: Ekonometria. Wybrane zagadnienia, PWN. Warszawa 2017
5. Lipiec-Zajchowska M. (red.), Wspomaganie Procesów Decyzyjnych, tom 2, C.H. Beck, Warszawa 2003

Recommended literature:

6. Osińska M: Ekonometria finansowa, PWE, Warszawa 2006
7. Box G.E.P., Jenkins G.M.: Analiza szeregów czasowych. Prognozowanie i sterowanie. PWN, Warszawa 1983
8. Maddala G.S.: Ekonometria. PWN. Warszawa 2006
9. Clemens M.P., D.F. Hendry: Forcasting economic time series. Cambridge University Press , Cambridge 2004

Term 2025Z:

Primary literature:
1. Mills T.C.: The Econometric Modelling of Financial Time Series. Cambridge University Press. Cambridge 2004.
2. Brooks C.: Introductory Econometrics for Finance. Second Edition, Cambridge University Press. Cambridge, 2008.
3. Witkowska D., Matuszewska A., Kompa K.: Wprowadzenie do ekonometrii dynamicznej i finansowej. Wydawnictwo SGGW. Warszawa 2008.
4. Borkowski B., Dudek H., Szczęsny W.: Ekonometria. Wybrane zagadnienia, PWN. Warszawa 2017.
5. Marianna Lipiec-Zajchowska (ed.), Wspomaganie Procesów Decyzyjnych, vol. 2, C.H. Beck, Warszawa 2003.

Supplementary literature:

6. Osińska M.: Ekonometria finansowa, PWE, Warszawa 2006.
7. Box G.E.P., Jenkins G.M.: Analiza szeregów czasowych. Prognozowanie i sterowanie. PWN, Warszawa 1983.
8. Maddala G.S.: Ekonometria. PWN. Warszawa 2006.
9. Clements M.P., D.F. Hendry: Forecasting Economic Time Series. Cambridge University Press, Cambridge 2004