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Managing risk using Quantitative Risk Management is a vital task across the banking, insurance, and asset management industries. It’s essential that financial risk analysts, regulators, and actuaries can quantitatively balance rewards against their exposure to risk. This course introduces you to financial portfolio risk management through an examination of the 2007—2008 financial crisis and its effect on investment banks such as Goldman Sachs and J.P. Morgan. You’ll learn how to use Python to calculate and mitigate risk exposure using the Value at Risk and Conditional Value at Risk measures, estimate risk with techniques like Monte Carlo simulation, and use cutting-edge technologies such as neural networks to conduct real time portfolio rebalancing.
Risk and return recapFree
Risk management begins with an understanding of risk and return. We’ll recap how risk and return are related to each other, identify risk factors, and use them to re-acquaint ourselves with Modern Portfolio Theory applied to the global financial crisis of 2007-2008.Welcome!50 xpPortfolio returns during the crisis100 xpAsset covariance and portfolio volatility100 xpRisk factors and the financial crisis50 xpFrequency resampling primer100 xpVisualizing risk factor correlation100 xpLeast-squares factor model100 xpModern portfolio theory50 xpPractice with PyPortfolioOpt: returns100 xpPractice with PyPortfolioOpt: covariance100 xpBreaking down the financial crisis100 xpThe efficient frontier and the financial crisis100 xp
Goal-oriented risk management
Now it’s time to expand your portfolio optimization toolkit with risk measures such as Value at Risk (VaR) and Conditional Value at Risk (CVaR). To do this you will use specialized Python libraries including pandas, scipy, and pypfopt. You’ll also learn how to mitigate risk exposure using the Black-Scholes model to hedge an options portfolio.Measuring Risk50 xpVaR for the Normal distribution100 xpComparing CVaR and VaR100 xpWhich risk measure is "better"?50 xpRisk exposure and loss50 xpWhat's your risk appetite?50 xpVaR and risk exposure100 xpCVaR and risk exposure100 xpRisk management using VaR & CVaR50 xpVaR from a fitted distribution100 xpMinimizing CVaR100 xpCVaR risk management and the crisis100 xpPortfolio hedging: offsetting risk50 xpBlack-Scholes options pricing100 xpOptions pricing and the underlying asset100 xpUsing options for hedging100 xp
Estimating and identifying risk
In this chapter, you’ll estimate risk measures using parametric estimation and historical real-world data. You'll then discover how Monte Carlo simulation can help you predict uncertainty. Lastly, you’ll learn how the global financial crisis signaled that randomness itself was changing, by understanding structural breaks and how to identify them.Parametric Estimation50 xpParameter estimation: Normal100 xpParameter estimation: Skewed Normal100 xpHistorical and Monte Carlo Simulation50 xpHistorical Simulation100 xpMonte Carlo Simulation100 xpStructural breaks50 xpCrisis structural break: I100 xpCrisis structural break: II100 xpCrisis structural break: III100 xpVolatility and extreme values50 xpVolatility and structural breaks100 xpExtreme values and backtesting100 xp
Advanced risk management
It's time to explore more general risk management tools. These advanced techniques are pivotal when attempting to understand extreme events, such as losses incurred during the financial crisis, and complicated loss distributions which may defy traditional estimation techniques. You’ll also discover how neural networks can be implemented to approximate loss distributions and conduct real-time portfolio optimization.Extreme value theory50 xpBlock maxima100 xpExtreme events during the crisis100 xpGEV risk estimation100 xpKernel density estimation50 xpKDE of a loss distribution100 xpWhich distribution?50 xpCVaR and loss cover selection100 xpNeural network risk management50 xpSingle layer neural networks100 xpAsset price prediction100 xpReal-time risk management100 xpWrap-up and Future Steps50 xp
In the following tracksApplied Finance
PrerequisitesIntroduction to Portfolio Analysis in Python
Jamsheed Shorish is CEO and founder of Shorish Research in Belgium, providing computational business services to startups, SMEs and enterprises. He received his Ph.D. from the Tepper School of Business at Carnegie Mellon University, focusing upon mathematical and computational modeling, is an honorary associate professor at the Australian National University and an affiliated researcher with the Research Institute for Cryptoeconomics at the Vienna University of Economics and Business. He’s taught extensively on subjects such as game theory, mechanism design and mathematical optimization, and his current focus is on the digital economy and platforms, and on the foundations and analysis of distributed ledger technologies such as blockchain.