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Finance I: Portfolio Design
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Deterministic Cash Flows: Interest, present and future value, internal rate of return. |
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Fixed Income Securities: Bonds, prices and yields, duration, immunization, term structure of interest rates. |
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Random Cash Flows: Asset return, portfolio return, random returns, portfolio mean return and variance, diversification, portfolio diagram, feasible set, Markowitz model, Two fund theorem, One fund theorem. |
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Capital Asset Pricing Model: Capital market line, CAPM, betas of stocks and portfolios, Security market line, Use of CAPM in investment analysis and as a pricing formula.
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Finance II: Financial Derivatives
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Forwards and Futures: Forward and futures prices and values, hedging, stock index futures, currency futures. |
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Options: Factors influencing options premia, Put-call parity, Binomial option pricing model (BOPM), dynamic hedging, pricing of American options. |
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Black-Scholes Model: Modelling of stock prices, analogy with BOPM, delta hedging, hedging parameters – “The Greeks”. |
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Option Spreads: Spreads, butterflies, straddles, and strangles. |
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Value at Risk (VaR): Estimating VaR by linear and quadratic models, Monte Carlo Simulation.
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Probability
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Basic Probability |
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Random variables: Discrete and continuous random variables, expectation and variance, binomial, normal, lognormal and chi-square variables. |
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Multivariate distributions: Conditional probability and distributions, independence, covariance, conditional expectation.
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Statistics and Econometrics
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Sampling: Sample mean and variance, large sample approximations, sampling distributions. |
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Point Estimation: Bias, efficiency, mean squared error, method of moments, method of maximum likelihood. |
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Interval estimation: Confidence intervals for mean, variance, difference between means, and ratio of variances. |
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Hypothesis Testing: Null and alternative hypotheses, test statistic and critical region, size and power of a test. |
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Linear Regression: Gauss-Markov assumptions, ordinary least squares estimation, maximum likelihood estimation, multiple regression, hypothesis tests on coefficients, problems of multicollinearity, heteroscedasticity and autocorrelation.
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Numerical Techniques (Lab)
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The practical work will be based on Microsoft Excel and VBA. These will be taught as part of the course. |
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