Quantitative methods in management 2600-MFBRdz1MIZF
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)
Type of course
Learning outcomes
After completing the course, the student:
In terms of knowledge:
• EW_1:
correctly uses terminology related to quantitative research methodology in financial management. (K_W01)
• EW_2:
distinguishes between types of financial time series, including stock market indices. (K_W01)
• EW_3:
explains the characteristics of return rates and their significance in the analysis of financial time series. (K_W01)
• EW_4:
explains the concept of stochastic processes and the differences between strict and weak stationarity. (K_W01)
• EW_5:
identifies purely random processes within financial time series. (K_W01)
• EW_6:
explains the concept of volatility in financial time series. (K_W01)
In terms of skills:
• EU_1:
calculates return rates for selected time series. (K_U01)
• EU_2:
analyzes market data in the context of financial time series. (K_U02)
• EU_3:
computes ex post forecast errors in the context of financial time series. (K_U02)
• EU_4:
estimates future values of financial time series using exponential smoothing models. (K_U01)
• EU_5:
interprets the impact of analyses and quantitative research on the functioning of financial management in organizations. (K_U02)
• EU_6:
interprets the results of financial time series forecasts based on analytical methods. (K_U02)
• EU_7: Interprets the results of stationarity tests, taking structural changes in the data into account. (K_U02)
• EU_8:
analyzes complex and atypical problems related to business finance and accounting, such as forecasting financial time series and examining volatility. (K_U06)
• EU_9:
analyzes regression results in terms of long-term relationships. (K_U06)
• EU_10:
presents the results of conducted financial analyses.(K_U06)
In terms of social competencies:
• EK_1:
assesses complex phenomena related to the functioning of finance and accounting in organizations using business data analyses. (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
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: