Introduction to Portfolio Risk Management in Python
Evaluate portfolio risk and returns, construct market-cap weighted equity portfolios and learn how to forecast and hedge market risk via scenario generation.Start Course for Free
4 Hours13 Videos51 Exercises19,166 Learners4250 XPApplied Finance in Python Track
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This course will teach you how to evaluate basic portfolio risk and returns like a quantitative analyst on Wall Street. This is the most critical step towards being able to fully automate your portfolio construction and management processes. Discover what factors are driving your portfolio returns, construct market-cap weighted equity portfolios, and learn how to forecast and hedge market risk via scenario generation.
Univariate Investment Risk and ReturnsFree
Learn about the fundamentals of investment risk and financial return distributions.Financial returns50 xpFinancial timeseries data100 xpCalculating financial returns100 xpReturn distributions100 xpMean, variance, and normal distribution50 xpFirst moment: Mu100 xpSecond moment: Variance100 xpAnnualizing variance100 xpSkewness and kurtosis50 xpThird moment: Skewness100 xpFourth moment: Kurtosis100 xpStatistical tests for normality100 xp
Level up your understanding of investing by constructing portfolios of assets to enhance your risk-adjusted returns.Portfolio composition and backtesting50 xpCalculating portfolio returns100 xpEqual weighted portfolios100 xpMarket-cap weighted portfolios100 xpCorrelation and co-variance50 xpThe correlation matrix100 xpThe co-variance matrix100 xpPortfolio standard deviation100 xpMarkowitz portfolios50 xpThe efficient frontier50 xpSharpe ratios100 xpThe MSR portfolio100 xpThe GMV portfolio100 xp
Learn about the main factors that influence the returns of your portfolios and how to quantify your portfolio's exposure to these factors.The Capital Asset Pricing model50 xpExcess returns100 xpCalculating beta using co-variance100 xpCalculating beta with CAPM100 xpAdjusted R-squared50 xpAlpha and multi-factor models50 xpThe Fama French 3-factor model100 xpp-values and coefficients100 xpEconomic intuition in factor modeling50 xpThe efficient market and alpha100 xpExpanding the 3-factor model50 xpThe 5-factor model100 xpAlpha vs R-squared50 xp
Value at Risk
In this chapter, you will learn two different methods to estimate the probability of sustaining losses and the expected values of those losses for a given asset or portfolio of assets.Estimating tail risk50 xpHistorical drawdown100 xpHistorical value at risk100 xpHistorical expected shortfall100 xpVaR extensions50 xpChanging VaR and CVaR quantiles100 xpParametric VaR100 xpScaling risk estimates100 xpRandom walks50 xpA random walk simulation100 xpMonte Carlo simulations100 xpMonte Carlo VaR100 xpUnderstanding risk50 xp
In the following tracksApplied Finance in Python
DatasetsAll returns (2017)Efficient Frontier PortfoliosFama-French factorsMicrosoft pricesETF of oil prices (UFO)
Quantitative Analyst and Founder of QuantCourse.com
Dakota Wixom is a quantitative finance analyst at Yewno, where he applies AI to create innovative financial products. Dakota founded QuantCourse.com and has also worked in quantitative risk management and investment banking roles in New York City and San Francisco. He has a B.S. in Quantitative Finance and a M.S. in Financial Analytics from the Stevens Institute of Technology.
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