This unit of teaching comprises of two parts:
1) Nonparametric econometrics
This course provides students with a good knowledge of the statistical and programming tools required for density and conditional mean estimation. The statistical technics are illustrated with computer codes (R language) and different types of data.
2) Quantitative Finance is structured around the following topics

• - Analysis of asset returns: autocorrelation, stationarity, predictability and prediction.
• - Volatility models: GARCH-type models, GARCH-M models, EGARCH model, GJR model, stochastic volatility
  model, long-range dependence.
• - High-frequency data analysis: duration models, logistic and ordered probit models for price changes, and
  realized volatility
• - Nonlinearities in financial data: simple nonlinear models, Markov switching and threshold models
• - Multivariate series: cross correlation matrices, simple vector AR models, co-integration and threshold co-
  integration, pairs trading, factor models and multivariate volatility models

- Understanding of the relationship between statistical theory and data generation process, and how to recover the data generating process from the data alone, while using flexible analytical tools.
- Choose statistical quantitative finance specifications which are suitable, both to the data and to the tackled questions
- Gather practical work experience with a statistical packages (R and Python) as preparation of an empirical master dissertation

Part 1 :

• - Henderson, D. and C. Parmeter, 2015, Applied Nonparametric Econometrics, Cambridge University Press.
• - Racine, J., 2019, An Introduction to the Advanced Theory and Practice of Nonparametric Econometrics: A
  Replicable Approach Using R, Cambridge University Press. Part 2:
• - Bauwens, Luc and Nikolaus Hautsch (2009), Econometric Modelling of Stock Market Intraday Activity, Springer.
• - Tsay, Ruey S. (2010), Analysis of Financial Time Series, 3rd edition, Wiley.