This course provides a comprehensive introduction to financial econometric concepts and techniques. It covers financial time Series econometrics, regression models with cross- sectional financial data, Asset price volatility models, simultaneous-equation models in financial time series, and economic forecasting.
At the end of the course, students should be able to:
Unit I (3 weeks)
Financial time Series econometrics: Stationary stochastic processes, nonstationary Stochastic Processes, unit root stochastic processes, trend Stationary and difference Stationary stochastic processes. Tests of stationarity- graphical analysis and autocorrelation function (ACF) and correlogram statistical significance of autocorrelation coefficients. The unit root test – the augmented dickey-fuller (ADF) test. Transforming nonstationary financial time series – difference stationary processes and trend-Stationary process. Cointegration: regression of a unit root financial time series on another unit root financial time series, testing for cointegration and Cointegration and Error Correction Mechanism (ECM).
References:
Gujarati, N. Damodar. Basic Econometrics. New Delhi: McGraw Hill. [Chapter 21and 22]
Gujarati, N. Damodar. Econometrics by Examples. New Delhi: McGraw Hill.[Chapter 13 and 14]
Unit II (3 weeks)
Regression models with cross-sectional financial data: The logit and Probit models, multinomial regression models, Ordinal regression models, and Limited dependent variable regression models.
References:
Gujarati, N. Damodar. Basic Econometrics. New Delhi: McGraw Hill. [Chapter 15-17].
Gujarati, N. Damodar. Econometrics by Examples. New Delhi: McGraw Hill.[Chapter 8-11]
Unit III (4 weeks)
Asset price volatility: The ARCH and GARCH models. Extensions of the ARCH model. Simultaneous-equation models in financial time series: The nature of simultaneous-equation models, simultaneous-equation models, simultaneous-equation bias, inconsistency of OLS estimators. A test of simultaneity, tests for exogeneity. Simultaneous-Equation Methods – approaches to estimation, recursive models and ordinary least squares, estimation of a just identified equation, the method of indirect least squares (ILS), estimation of an overidentified equation: the method of two-stage least squares (2SLS)
References:
Gujarati, N. Damodar. Basic Econometrics. New Delhi: McGraw Hill. [ Chapter 17 – 20]
Gujarati, N. Damodar. Econometrics by Examples. New Delhi: McGraw Hill. [13 and 15]
Unit IV (2 weeks)
Economic forecasting: Forecasting with regression models. The Box–Jenkins methodology: ARIMA modeling. An ARMA model of companies daily closing prices. Vector autoregression (VAR), Testing causality using VAR: The Granger causality test
References:
Gujarati, N. Damodar. Basic Econometrics. New Delhi: McGraw Hill. [Chapter 22]
Gujarati, N. Damodar. Econometrics by Examples. New Delhi: McGraw Hill. [Chapter 16]
Recommendation Computer Package to be Used: Use of software like E Views, R and STATA solving real life problems.
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