Qual è il tuo appetito per il rischio?

Il risk management valuta l’esposizione al rischio in base al profilo di rischio (o tolleranza al rischio) dell’investitore. Quale delle seguenti attività potresti svolgere se le tue preferenze fossero avverse al rischio?

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Gestione quantitativa del rischio in Python

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Gestione quantitativa del rischio in Python

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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.

Exercise 1: Welcome!Exercise 2: Portfolio returns during the crisis Exercise 3: Asset covariance and portfolio volatility Exercise 4: Risk factors and the financial crisis Exercise 5: Frequency resampling primer Exercise 6: Visualizing risk factor correlation Exercise 7: Least-squares factor model Exercise 8: Modern portfolio theory Exercise 9: Practice with PyPortfolioOpt: returns Exercise 10: Practice with PyPortfolioOpt: covariance Exercise 11: Breaking down the financial crisis Exercise 12: The efficient frontier and the financial crisis

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.

Exercise 1: Misurare il rischio Exercise 2: VaR per la distribuzione Normale Exercise 3: Confrontare CVaR e VaR Exercise 4: Quale misura di rischio è "migliore"?Exercise 5: Esposizione al rischio e perdita Exercise 6: Qual è il tuo appetito per il rischio?

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Exercise 7: VaR ed esposizione al rischio Exercise 8: CVaR ed esposizione al rischio Exercise 9: Gestione del rischio con VaR e CVaR Exercise 10: VaR da una distribuzione adattata Exercise 11: Minimizzare il CVaR Exercise 12: Gestione del rischio con CVaR e la crisi Exercise 13: Copertura del portafoglio: compensare il rischio Exercise 14: Prezzo delle opzioni con Black-Scholes Exercise 15: Prezzo delle opzioni e asset sottostante Exercise 16: Usare le opzioni per copertura (hedging)

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.

Exercise 1: Parametric Estimation Exercise 2: Parameter estimation: Normal Exercise 3: Parameter estimation: Skewed Normal Exercise 4: Historical and Monte Carlo Simulation Exercise 5: Historical Simulation Exercise 6: Monte Carlo Simulation Exercise 7: Structural breaks Exercise 8: Crisis structural break: I Exercise 9: Crisis structural break: II Exercise 10: Crisis structural break: III Exercise 11: Volatility and extreme values Exercise 12: Volatility and structural breaks Exercise 13: Extreme values and backtesting

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.

Exercise 1: Extreme value theory Exercise 2: Block maxima Exercise 3: Extreme events during the crisis Exercise 4: GEV risk estimation Exercise 5: Kernel density estimation Exercise 6: KDE of a loss distribution Exercise 7: Which distribution?Exercise 8: CVaR and loss cover selection Exercise 9: Neural network risk management Exercise 10: Single layer neural networks Exercise 11: Asset price prediction Exercise 12: Real-time risk management Exercise 13: Wrap-up and Future Steps