PART I: Time Series Analysis
(a) Financial Time Series and their characteristics
(b) Linear Time Series: AR, MA, and ARMA models
(c) Unit-Root Nonstationarity
(d) Conditional Heteroscedastic Models: ARCH and GARCH models
(e) Value-at-Risk, Risk Metrics, and Expected Shortfall

PART II: Applications in Financial Econometrics
(a) Event study analysis:
We review the standard approach of the event study and how it can be adapted to deal with heteroskedasticity.
i. Basic structure of the event study
ii. Nonparametric and robust tests
iii. Time Series Heteroskedasticity
iv. Fiordalisi, F., and Ricci, O., 2016, “Whatever it takes: an empirical assessment of the value of policy actions in banking”, Review of Finance
(b) Program evaluation using panel data:
We introduce program evaluation using panel data. Issues of measuring treatment effects are discussed, as are the pros and cons of panel data parametric, and nonparametric approach versus cross-sectional data.
i. Cross-sectional data approach: Matching observables in terms of propensity score method; Regression discontinuity design; Difference-in-Difference approach.
ii. Panel data approach: Regression method (PDA); Synthetic control method; Averging method.
iii. Deiana C. and Mazzarella G., “Does the road to happiness depend on the retirement decisions? Evidence from Italy”, book chapter in Crato N., and Paruolo P.
iv. Pesaran, M.H. and Yang, C.F., 2022, “ Matching theory and evidence on COVID-19 using a Stochastic Network SIR Model”, Journal of Applied Econometrics.
(c) Portfolio choice and testing the Capital Asset Pricing Model:
We cover the implementation of mean-variance portfolio choice based on a panel dataset of stock returns.
i. Portfolio choice
ii. Testing the CAPM
iii. Cross-sectional regression tests
iv. Portfolio grouping
v. Time-varying model
vi. Empirical evidence on the CAPM
(d) Multifactor Pricing Models:
We consider multifactor models for returns, We discuss some of the key concepts in the arbitrage pricing theory, and we discuss the testing of this theory.
i. Linear factor model
ii. Diversification
iii. Pervasive factors
iv. Statistical factor models
v. Fama E., and K.R. French, 2015, “A five-factor asset pricing model”, Journal of Financial Economics.
(e) Yield curve: We consider the measurement of interest rates and the time value of money.
This plays an important role in many applications: portfolio allocation, predicting future interest rates, future inflation, and national income, among others.
i. Discount function, yield curve, forward rates
ii. Estimation of the yield curve from coupon bonds
iii. Discrete time models of bond pricing
iv. Arbitrage and pricing kernels
v. Backus, D., Foresi, S., and Telmer C., 1998, “Discrete-time models of bond pricing”, NBER Working Paper.