Master of Science in Finance and Banking

Updated A.Y. 2022-2023

PROGRAMME

1. Introduction

Asset returns. Stylized facts: asymmetry, kurtosis and volatility clustering. Stochastic processes: stationarity, purely random processes (white noise). Random walks and martingales.  Review of prediction theory. Optimal prediction. Forecasting with nonstationary models: exponential smoothing.

2.  Volatility measurement and analysis

Autoregressive Conditional Heteroscedasticity (ARCH) models: specification, properties, maximum likelihood estimation, prediction. Extensions: ARCH in mean. Generalized ARCH models, Integrated GARCH, Exponential GARCH models, GJR GARCH.

3. Review of multivariate distribution theory. Multivariate GARCH models. VEC and BEKK. Conditional correlation models: CCC, DCC. Factor models: Factor GARCH, O-GARCH. High-dimensional covariance estimation.

4. Stochastic volatility models. Pseudo-maximum likelihood inference. State space models. The Kalman filter.

5. Realized volatility. Market microstructure noise. Long memory.

6. Risk measurement: Value at Risk and expected shortfall. Copulae and tail dependence.

Textbook references

• Campbell, J., Lo, A. and MacKinlay, A. (1999). The Econometrics of Financial Markets. Princeton University Press: New Jersey.
• Fan J. and Yao, Q. (2017). The Elements of Financial Econometrics. Cambride University Press.
• Franke, J., Haerdle, W.K. and Hafner, C.M. (2012). Statistics of Financial Markets. An Introduction. Third Edition. Springer.
• Linton O. (2019). Financial Econometrics: Models and Methods. Cambridge University Press.
• McNeil, A.J., Frey, R. and Embrechts, P. (2005). Quantitative Risk Management, Princeton Series in Finance.
• Taylor, S. J. (2005). Asset Price Dynamics, Volatility, and Prediction. Princeton University Press.
• Tsay, R.S. (2010). Analysis of Financial Time Series, Third Edition. Wiley.

Updated A.Y. 2021-2022

PROGRAMME

1. Introduction
    Asset returns. Stylized facts: asymmetry, kurtosis and volatility clustering. Stochastic processes: stationarity, purely random processes (white noise). Random walks and martingales.
    Review of prediction theory. Optimal prediction. Forecasting with nonstationary models: exponential smoothing.

2.  Volatility measurement and analysis
    Autoregressive Conditional Heteroscedasticity (ARCH) models: specification, properties, maximum likelihood estimation, prediction. Extensions: ARCH in mean.
    Generalized ARCH models, Integrated GARCH, Exponential GARCH models, GJR GARCH.

3. Review of multivariate distribution theory. Multivariate GARCH models. VEC and BEKK. Conditional correlation models: CCC, DCC. Factor models: Factor GARCH, O-GARCH. High-dimensional covariance estimation.

4. Stochastic volatility models. Pseudo-maximum likelihood inference. State space models. The Kalman filter.

5. Realized volatility. Market microstructure noise. Long memory.

6. Risk measurement: Value at Risk and expected shortfall. Copulae and tail dependence.

Textbook references

    Campbell, J., Lo, A. and MacKinlay, A. (1999). The Econometrics of Financial Markets. Princeton University Press: New Jersey.
    Fan J. and Yao, Q. (2017). The Elements of Financial Econometrics. Cambride University Press.
    Franke, J., Haerdle, W.K. and Hafner, C.M. (2012). Statistics of Financial Markets. An Introduction. Third Edition. Springer.
    Linton O. (2019). Financial Econometrics: Models and Methods. Cambridge University Press.
    McNeil, A.J., Frey, R. and Embrechts, P. (2005). Quantitative Risk Management, Princeton Series in Finance.
    Taylor, S. J. (2005). Asset Price Dynamics, Volatility, and Prediction. Princeton University Press.
    Tsay, R.S. (2010). Analysis of Financial Time Series, Third Edition. Wiley.

Updated A.Y. 2019-2020

PROGRAMME

1. Introduction
    Asset returns. Stylized facts: asymmetry, kurtosis and volatility clustering. Stochastic processes: stationarity, purely random processes (white noise). Random walks and martingales.
    Review of prediction theory. Optimal prediction. Forecasting with nonstationary models: exponential smoothing.

2.  Volatility measurement and analysis
    Autoregressive Conditional Heteroscedasticity (ARCH) models: specification, properties, maximum likelihood estimation, prediction. Extensions: ARCH in mean.
    Generalized ARCH models, Integrated GARCH, Exponential GARCH models, GJR GARCH.

3. Review of multivariate distribution theory. Multivariate GARCH models. VEC and BEKK. Conditional correlation models: CCC, DCC. Factor models: Factor GARCH, O-GARCH. High-dimensional covariance estimation.

4. Stochastic volatility models. Pseudo-maximum likelihood inference. State space models. The Kalman filter.

5. Realized volatility. Market microstructure noise. Long memory.

6. Risk measurement: Value at Risk and expected shortfall. Copulae and tail dependence.

Textbook references

    Campbell, J., Lo, A. and MacKinlay, A. (1999). The Econometrics of Financial Markets. Princeton University Press: New Jersey.
    Fan J. and Yao, Q. (2017). The Elements of Financial Econometrics. Cambride University Press.
    Franke, J., Haerdle, W.K. and Hafner, C.M. (2012). Statistics of Financial Markets. An Introduction. Third Edition. Springer.
    Linton O. (2019). Financial Econometrics: Models and Methods. Cambridge University Press.
    McNeil, A.J., Frey, R. and Embrechts, P. (2005). Quantitative Risk Management, Princeton Series in Finance.
    Taylor, S. J. (2005). Asset Price Dynamics, Volatility, and Prediction. Princeton University Press.
    Tsay, R.S. (2010). Analysis of Financial Time Series, Third Edition. Wiley.

Updated A.Y. 2018-2019

PROGRAMME

1. Introduction
    Asset returns. Stylized facts: asymmetry, kurtosis and volatility clustering. Stochastic processes: stationarity, purely random processes (white noise). Random walks and martingales.
    Review of prediction theory. Optimal prediction. Forecasting with nonstationary models: exponential smoothing.

2.  Volatility measurement and analysis
    Autoregressive Conditional Heteroscedasticity (ARCH) models: specification, properties, maximum likelihood estimation, prediction. Extensions: ARCH in mean.
    Generalized ARCH models, Integrated GARCH, Exponential GARCH models, GJR GARCH.

3. Review of multivariate distribution theory. Multivariate GARCH models. VEC and BEKK. Conditional correlation models: CCC, DCC. Factor models: Factor GARCH, O-GARCH. High-dimensional covariance estimation.

4. Stochastic volatility models. Pseudo-maximum likelihood inference. State space models. The Kalman filter.

5. Realized volatility. Market microstructure noise. Long memory.

6. Risk measurement: Value at Risk and expected shortfall. Copulae and tail dependence.

Textbook references

    Campbell, J., Lo, A. and MacKinlay, A. (1999). The Econometrics of Financial Markets. Princeton University Press: New Jersey.
    Fan J. and Yao, Q. (2017). The Elements of Financial Econometrics. Cambride University Press.
    Franke, J., Haerdle, W.K. and Hafner, C.M. (2012). Statistics of Financial Markets. An Introduction. Third Edition. Springer.
    Linton O. (2019). Financial Econometrics: Models and Methods. Cambridge University Press.
    McNeil, A.J., Frey, R. and Embrechts, P. (2005). Quantitative Risk Management, Princeton Series in Finance.
    Taylor, S. J. (2005). Asset Price Dynamics, Volatility, and Prediction. Princeton University Press.
    Tsay, R.S. (2010). Analysis of Financial Time Series, Third Edition. Wiley.

Updated A.Y. 2017-2018

PROGRAMME

1. Introduction

    Asset returns. Stylized facts: asymmetry, kurtosis and volatility clustering. Stochastic processes: stationarity, purely random processes (white noise). Random walks and martingales.
    Review of prediction theory. Optimal prediction. Forecasting with nonstationary models: exponential smoothing.

2.  Volatility measurement and analysis

    Autoregressive Conditional Heteroscedasticity (ARCH) models: specification, properties, maximum likelihood estimation, prediction. Extensions: ARCH in mean.
    Generalized ARCH models, Integrated GARCH, Exponential GARCH models, GJR GARCH.

3. Review of multivariate distribution theory. Multivariate GARCH models. VEC and BEKK. Conditional correlation models: CCC, DCC. Factor models: Factor GARCH, O-GARCH. High-dimensional covariance estimation.

4. Realized volatility. Market microstructure noise. Long memory.

5. Risk measurement: Value at Risk and expected shortfall.

Textbook references

- Campbell, J., Lo, A. and MacKinlay, A. (1999). The Econometrics of Financial Markets. Princeton University Press: New Jersey.
- Franke, J., Haerdle, W.K. and Hafner, C.M. (2012). Statistics of Financial Markets. An Introduction. Third Edition. Springer.
- McNeil, A.J., Frey, R. and Embrechts, P. (2005). Quantitative Risk Management, Princeton Series in Finance.
- Taylor, S. J. (2005). Asset Price Dynamics, Volatility, and Prediction. Princeton University Press.
- Tsay, R.S. (2010). Analysis of Financial Time Series, Third Edition. Wiley.

Updated A.Y. 2016-2017

PROGRAMME

1. Introduction

    Asset returns. Stylized facts: asymmetry, kurtosis and volatility clustering.
    Stochastic processes: stationarity, purely random processes (white noise). Random walks and martingales.
    Review of prediction theory. Optimal prediction. Forecasting with nonstationary models: exponential smoothing.

2.  Volatility measurement and analysis

    Autoregressive Conditional Heteroscedasticity (ARCH): model specification, properties, maximum likelihood estimation, prediction. Extensions: ARCH in mean.
    Generalized ARCH models, Integrated GARCH, Exponential GARCH models.
    Multivariate GARCH models. VEC and BEKK. Conditional correlation models: CCC, DCC. Factor models: Factor GARCH, O-GARCH 2.4 Realized volatility.
    Risk measurement: Value at Risk and expected shortfall.

Textbook references

- Campbell, J., Lo, A. and MacKinlay, A. (1999). The Econometrics of Financial Markets. Princeton University Press: New Jersey.
- Franke, J., Haerdle, W.K. and Hafner, C.M. (2012). Statistics of Financial Markets. An Introduction. Third Edition. Springer.
- McNeil, A.J., Frey, R. and Embrechts, P. (2005). Quantitative Risk Management, Princeton Series in Finance.
- Taylor, S. J. (2005). Asset Price Dynamics, Volatility, and Prediction. Princeton University Press.
- Tsay, R.S. (2010). Analysis of Financial Time Series, Third Edition. Wiley.

Updated A.Y. 2014-2015

PROGRAMME

1. Introduction

    Asset returns. Stylized facts: asymmetry, kurtosis and volatility clustering.
    Stochastic processes: stationarity, purely random processes (white noise). Random walks and martingales.
    Review of prediction theory. Optimal prediction. Forecasting with nonstationary models: exponential smoothing.

2.  Volatility measurement and analysis

    Autoregressive Conditional Heteroscedasticity (ARCH): model specification, properties, maximum likelihood estimation, prediction. Extensions: ARCH in mean.
    Generalized ARCH models, Integrated GARCH, Exponential GARCH models.
    Multivariate GARCH models. VEC and BEKK. Conditional correlation models: CCC, DCC. Factor models: Factor GARCH, O-GARCH 2.4 Realized volatility.
    Risk measurement: Value at Risk and expected shortfall.

Textbook references

- Campbell, J., Lo, A. and MacKinlay, A. (1999). The Econometrics of Financial Markets. Princeton University Press: New Jersey.
- Franke, J., Haerdle, W.K. and Hafner, C.M. (2012). Statistics of Financial Markets. An Introduction. Third Edition. Springer.
- McNeil, A.J., Frey, R. and Embrechts, P. (2005). Quantitative Risk Management, Princeton Series in Finance.
- Taylor, S. J. (2005). Asset Price Dynamics, Volatility, and Prediction. Princeton University Press.
- Tsay, R.S. (2010). Analysis of Financial Time Series, Third Edition. Wiley.