Financial Econometrics

Financial econometrics integrates finance, economics, probability, statistics, and applied mathematics to analyze complex financial market data. The course addresses a field shaped by decades of technological innovation, trade globalization, and the development of numerous new financial products: all of which have made rigorous quantitative analysis increasingly central to understanding modern markets.

Technological innovation and trade globalization have led us into a new era of financial markets. Over the past three decades, numerous new financial products have been developed. Financial econometrics is a dynamic field that integrates finance, economics, probability, statistics, and applied mathematics to decipher the complex financial market data patterns into strategic financial insights.

The course aims to equip students with the theoretical knowledge, practical skills, and analytical tools necessary for effectively understanding and analysing financial data. It covers various econometric techniques for finance, including volatility modelling, multivariate time series analysis, cointegration and error correction methods, capital asset pricing models, modern portfolio theory, and financial risk analysis. Additionally, the course introduces high-frequency financial data and the methods for modelling and analysing such data.

Students are expected to be familiar with the stylized facts of financial return series. They will be able to model the volatility in the financial return series data by way of familiarising with the concepts such as realized and implied volatility. They will be able to estimate the basic models of volatility using software. One of the other learning outcomes of this course will be familiarization with mean-variance analysis of portfolios. The students are expected to learn and apply factor models. They will also learn some of the basic properties and modelling aspects of high frequency financial data.

• Gourieroux, C and Jasiak, J (2001), Financial Econometrics: Problems, Models and Methods, Princeton University Press.
• J Y Campbell, A W Lo, and A C MacKinlay (1997), The Econometrics of Financial Markets, Princeton University Press
• Tsay, R S (2002): Analysis of Financial Time Series, Wiley Series in Probability and Statistics. Wiley, New York

• Brooks, C (2002), Introductory Econometrics for Finance, CUP
• Chakrabarty, SP and Kanaujiya, A (2023). Mathematical Portfolio Theory and Analysis, Birkhauser
• Christoffersen, P (2004), Elements of Financial Risk Management, Academic Press
• Fan, J (2004), An Introduction to Financial Econometrics, Preprint Notes
• Hautsch, N (2011).  Econometrics of Financial High-Frequency Data, Springer
• Hull, JC (2021), Options, Futures and other Derivatives, 11th Edition, Pearson
• Hull, JC (2023). Risk Management and Financial Institutions, 6th Edition
• Jorion, P(2007). Value at Risk: The New Benchmark for Managing Financial Risk, 3rd Ed., McGraw Hill
• Lee, C, Chen, H and Lee, J (2019). Financial Econometrics, Mathematics and Statistics: Theory, Method and Application, Springer
• Lee, Cheng-Few and Lee, JC (Editors) (2020). Handbook of Financial Econometrics, Mathematics, Statistics and Machine Learning, Vol I to IV, World Scientific
• Linton, O (2020), Financial Econometrics; Models and Methods, CUP
• McNeil, A J, Frey, R and  Embrechts, P (2005), Quantitative Risk Management: Concepts, Techniques and Tools, Princeton University Press
• Ruppert, D (2004), Statistics and Finance: An Introduction, Springer
• Wang, Pijie (2003), Financial Econometrics: Methods and Models, Routledge.