Financial Econometrics Course Outline 2020 2 final-4 PDF

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Download Financial Econometrics Course Outline 2020 2 final-4 PDF

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Financial Econometrics (ECOM40004/ECOM90011) The overall aim of this subject is to learn about the properties of financial data using specific methods of analysis. This subject also provides an advanced discussion of the main techniques used in financial econometric analysis. Basic econometric tools are presented for the analysis of data such as stock returns, realized volatilities, spot exchange rate, and credit default swaps (CDS). Applications of univariate and multivariate econometric models in finance include volatility modelling, calculating value at risk, and modelling financial network. A special focus is put on modelling and forecasting of returns and volatility of financial assets. The computer software used are mostly EViews and occasionally MATLAB. On successful completion of this subject students should be able to: • • • •

Apply the main techniques that are used in Financial Econometric analysis; Discuss and/or test the financial theory behind each technique; Identify the main pitfalls in applying the techniques; Discuss how the techniques used relate to financial theory.

Class times: Semester 2 2020 •

Online learning: recorded lectures will be uploaded on CANVAS prior to class times and lecturer will be available online, via Zoom, during class times for Q&A and tutorials. Zoom link will be emailed to class prior to class times.

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Tuesday 12:30 – 14:00pm (in the Spot-3010, if campus reopens) Friday 14:00 – 15:30pm (in the Spot-3010, if campus reopens)

Assessment: Assessments in this subject have been amended from the 2020 Handbook (in accordance with the University's transition to full online delivery), as a result of changes made due to the COVID-19 pandemic. • •

Assignments: 40% (2 assignments, due in weeks 7 and 12) Examination: 60% (open book at-home exam, 2 hour exam) (students will be given 3 hours to complete and submit)

Note there is a hurdle requirement of a mark of 50% or greater in the final exam to pass the course. Course Coordinator: Professor Guay Lim, [email protected], 6.18 FBE Lecturers: Weeks 1-6: Sam Tsiaplias, [email protected], 6.11 FBE. Weeks 7-12: Viet Nguyen, [email protected], 6.15 FBE.

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Week 1 (4-7 Aug)

2 (11-14 Aug)

3 (18-21 Aug)

Topic Introduction and stylised properties

Details Introduction. Stylised statistical properties of financial returns.

Stationary time series models

Stationary time series, review of key concepts, and ARMA models.

Non-stationarity

Non-stationary time series. ARIMA models. Unit roots. Variance Ratio Tests. Cointegration. Examples: - Testing the efficient market hypothesis

Event studies

Structure and financial applications. Example: - Stock splits Self-Exciting Threshold Autoregressive (SETAR) and Smooth-threshold Autoregressive (STAR) models.

Introduction to non-linear time series models: Threshold models & Regime Switching

Lecturer Sam

Sam

Sam

Example: Real exchange Markov-switching models. Examples: - Exchange rate forecasting - Bull and bear markets Assignment 1 distributed on 24-August

24-Aug 4 (25-28 Aug)

Models of volatility Review of GARCH models

5 (1-4 Sep)

Volatility with multiple assets

Heteroscedasticity

Sam

Identifying and testing for time-varying volatility

The (G)ARCH family of models Application: Estimating a volatility model for S&P500 stock market returns Covariances and dependencies between asset returns

Sam

mGARCH Application: Estimating and interpreting an mGARCH model for exchange rates Key features of volatility models 6 (8-11 Sep)

Forecasting volatility

Densities and risk

ML estimation Asymmetry & News Impact Curves Point forecasts of volatility and realized volatility Application: Comparing realized volatility and GARCH volatility forecasts Predictive densities Risk measures & Value at Risk (VaR) Application: Calculating densities and VaR for S&P500 returns

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Sam

Assignment 1 due 5pm on 14-September

14-Sep 7 (15-18 Sep)

Multivariate Conditional Mean Model

• • •

8 (22-25 Sep)

Modelling Financial Network

• • • •

9 (29-Sep 2-Oct)

Non-stationary Multivariate Modelling

5-Oct

Non-Teaching Week

• • •

FAVAR and Elastic Net Model

• • •

11 (20-23 Oct)

Multivariate Time Series Forecasting and Evaluation

• • •

Viet

Factor-Augmented VAR (FAVAR) Elastic Net Model Application: Global Equity and Global Credit Risk Data Multivariate Time Series Forecasting Forecast Evaluation Application: Global Equity Data

Viet

Viet

Assignment 2 due 5pm on 26-October

26-Oct

2-6 Nov 9-27 Nov

Viet

Assignment 2 distributed on 5-October

10 (13-16 Oct)

12 (27-30 Oct)

Vector Auto-Regression (VAR) Model Dynamic Analysis in VAR (Impulse Response Function and Forecast Error Variance Decomposition) Application: Returns and Volatility Linkages in Global Equity Markets Modelling Financial Network Spillover Measures and Spillover Density Time-varying Network and Spillover Measures Application: Global Equity Network, Global Credit Risk Network Vector Error-Correction (VECM) Model Cointegration Application: Relationship between Price and Quantity (Trading Orders) in Major Currency Spot Markets

Nonlinear Conditional Mean Model

SWOT Vac EXAM

• • •

Nonlinear Auto-Regressive Distributed Lags (NARDL) Model Asymmetric Cointegrating Relation Application: Asymmetric Relationship between Price and Quantity (Trading Orders) in Major Currency Spot Markets

Viet

Exam date to be confirmed

Reading Guide The main references will be the lecture notes and journal articles as specified in class. The following book chapters and journal articles may also be useful. We note that they constitute optional reading.

Univariate Time Series Analysis Campbell, J. Y. and R. Shiller, (1988) "The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors," Review of Financial Studies, 1, Fall, 195-228.

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Cont R. (2001) “Empirical Properties of Asset Returns: Stylized Facts and Statistical Issues”, Quantitative Finance, 1, 223-236. Diebold F. X. (2017) Forecasting in Economics, Business, Finance and Beyond, Department of Economics, University of Pennsylvania, https://www.sas.upenn.edu/~fdiebold/Teaching221/Forecasting.pdf Engel, C. & Hamilton, J. D. (1990) "Long Swings in the Dollar: Are They in the Data and Do Markets Know It?," American Economic Review, vol. 80(4), September, 689-713. Engel, C., (1994) "Can the Markov Switching Model Forecast Exchange Rates?" Journal of International Economics, vol. 36(1-2), February, 151-165. Meese R. A. and K. Rogoff (1983) “Empirical Exchange Rate Models of the Seventies: Do they Fit Out of Sample?” Journal of International Economics, 14, 1-2, 3024. Hansen L. and Singleton (1982) “Generalized Instrumental Variables Estimation of Nonlinear Rational Expectations Models”, Econometrica, 50, 5, Sept., 1269-1286. Pagan A. R. and K. A. Sossounov (2003) “A Simple Framework for Analysing Bull and Bear Markets”, Journal of Applied Econometrics, 18, 23-46. Taylor M. P., D. A. Peel and L. Sarno (2001) “Nonlinear Mean-Reversion in Real Exchange Rates: Toward a Solution to the Purchasing Power Puzzles”, International Economic Review, 42, 4, Nov., 1014-1042.

Volatility Tsay R. S. (2010) Analysis of Financial Time Series, John Wiley & Sons, 3rd edition (Chapter 3 on conditional heteroscedastic models, Chapter 7 on Extreme Values, Quantiles and Value at Risk, Chapter 10 on multivariate volatility models). Linton, O. & Park, S. (2012), Realized Volatility: Theory and Application, Handbook of Volatility Models and Their Applications, Chapter 13. Engle, R.F. (2001), GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics, Journal of Economic Perspectives, vol 15(4), pages 157-168. Engle, R.F. & Ng. V.K. (1993). Measuring and Testing the Impact of News on Volatility, Journal of Finance, Vol. 48, pp. 1749-1778. Bauwens, L., Laurent, S. & Kombouts, J.V.K. (2006), Multivariate GARCH models: A survey, Journal of Applied Econometrics, 21, pp. 79-109.

Multivariate analysis Evans, M., & Lyons, R. (2002). Order Flow and Exchange Rate Dynamics. Journal of Political Economy, 110(1), 170-180. Hamilton, J.D. (1994). Time Series Analysis. Princeton University Press, Princeton: Chapter 11 Vector Autoregressions Stock, J.H., Watson, M.W. (2001). Vector Autoregressions. Journal of Economic Perspectives, 15, 101-115. Pesaran, M., Shin, Y. (1998). Generalized Impulse Response Analysis in Linear Multivariate Models. Economics Letters, 58, 17-29.

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Diebold, F., Yilmaz, K. (2009). Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets. Economic Journal, 119, 158-171. Johansen, S. (1995). Likelihood-based inference in cointegrated vector autoregressive models. Oxford University Press, Oxford. Shin, Y., Yu, B. and Greenwood-Nimmo, M.J. (2014). "Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework". In William C. Horrace and Robin C. Sickles (Eds.), Festschrift in Honor of Peter Schmidt: Econometric Methods and Applications, pp. 281-314. New York (NY): Springer Science & Business Media. Stock, J.H., Watson, M.W. (2005). Implications of Dynamic Factor Models for VAR Analysis National Bureau of Economic Research, Cambridge, Working Paper 11467.

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