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FIN 203 Quantitative Finance integrates finance, analytics, and programming. Across six sections, students learn to measure investment risk, construct portfolios, analyze asset prices, and value options using data and models.
Historical Returns and Statistics
Students analyze historical market returns to measure reward and risk using descriptive statistics, probability distributions, and correlation. They use these tools to compare investments and understand diversification and tail risk.
Portfolio Theory and Optimization
Students combine assets into portfolios and explore how diversification changes the trade-off between risk and return. They use numerical optimization to construct efficient portfolios under practical investment constraints.
Simple Linear Regression and the CAPM
Students use simple linear regression to estimate a security’s sensitivity to market returns and interpret beta in the capital asset pricing model. They distinguish systematic from idiosyncratic risk and evaluate the statistical evidence behind their estimates.
Multiple Linear Regression and Factor Pricing Models
Students extend regression analysis to multiple sources of investment risk using factor pricing models such as the Fama–French three-factor model. They interpret factor exposures and alpha to distinguish compensation for risk from investment performance.
Decision Trees and Binomial Option Pricing
Students use decision trees to organize uncertain outcomes and build the binomial option pricing model. They value call and put options, explore early exercise, and connect option prices to replication and hedging.
Monte Carlo Simulation and Option Pricing
Students simulate uncertain outcomes to estimate option values and assess how results change with model inputs. They apply Monte Carlo methods to European and Asian options and use sensitivity analysis to interpret their results.
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