FIN 4380: Computational Finance

3 Credit Hours

Prerequisite: FIN 3100
A course designed to develop knowledge of financial data analysis using programming languages commonly used in finance. Students will learn how to apply widely used econometric modeling techniques such as linear regression and time series analysis for analyzing financial data. Topics include financial portfolio analysis, credit risk management, and cash flow valuation.

Course Learning Outcomes
Students who successfully complete this course will be able to:

Use open-source programming languages such as Python or R to analyze financial data and display findings.
Work with real financial data at different frequencies>
Conduct ordinary least squares (OLS) regression with single and multiple regressors.
Outline and critically assess the assumptions and limitation of OLS.
Evaluate the regression results and conduct hypothesis testing.
Identify and control for omitted variable biases and endogeneity.
Test for independence, normality, homoscedasticity, and symmetry for returns.
Analysis of ar, ma, arma, arima, arch, garch, and stochastic volatility time series models applied to exchange rates, indexes, interest rates, and returns.
Conduct company valuation under different cash flow scenarios.
Estimate betas from financial data over various time horizons.
Model efficient frontier in portfolio analysis under short selling and riskless borrowing and lending, optimal portfolio under single index and multi-index models, principal components analysis, stability tests of betas from auxiliary data.
Simulation and Monte-Carlo analysis, bootstrapping standard errors for scenario analysis and option pricing.
Credit risk modeling: use probit/logit models for binary outcomes such as default, identify predictors of default, Altman Z-Score, Moody’s distance to default.
Gain a basic understanding of machine learning techniques, textual data analysis.