Kejadian ekstrem selama krisis

Anda dapat menggunakan sebaran Generalized Extreme Value (GEV) untuk menelaah nilai-nilai ekstrem pada kerugian General Electric (GE) selama krisis keuangan tahun 2008 dan 2009.

Periode ini bertepatan dengan krisis likuiditas GE, dan pada akhirnya memerlukan investasi darurat sebesar $3 miliar dari Warren Buffett (Berkshire Hathaway) untuk mencegah gagal bayar atas kewajiban commercial paper-nya.

losses GE dan kerugian maksimum mingguan weekly_max tersedia, demikian pula sebaran GEV genextreme dari scipy.stats.

Latihan ini adalah bagian dari kursus

Manajemen Risiko Kuantitatif dengan Python

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Plot the log daily losses of GE over the period 2007-2009
losses.____()

# Find all daily losses greater than 10%
extreme_losses = losses[____]

# Scatter plot the extreme losses
____.plot(style='o')
plt.show()

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Latihan ini adalah bagian dari kursus

Manajemen Risiko Kuantitatif dengan Python

SkillTag.level.advancedSkillTag.label

4.8+

Mulai Kursus Gratis

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: Measuring Risk Exercise 2: VaR for the Normal distribution Exercise 3: Comparing CVaR and VaR Exercise 4: Which risk measure is "better"?Exercise 5: Risk exposure and loss Exercise 6: What's your risk appetite?Exercise 7: VaR and risk exposure Exercise 8: CVaR and risk exposure Exercise 9: Risk management using VaR & CVaR Exercise 10: VaR from a fitted distribution Exercise 11: Minimizing CVaR Exercise 12: CVaR risk management and the crisis Exercise 13: Portfolio hedging: offsetting risk Exercise 14: Black-Scholes options pricing Exercise 15: Options pricing and the underlying asset Exercise 16: Using options for 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: Teori nilai ekstrem Exercise 2: Maksimum blok Exercise 3: Kejadian ekstrem selama krisis

Latihan Saat Ini

Exercise 4: Estimasi risiko GEV Exercise 5: Estimasi kepadatan kernel Exercise 6: KDE dari suatu distribusi kerugian Exercise 7: Sebaran yang mana?Exercise 8: CVaR dan pemilihan penutup kerugian Exercise 9: Manajemen risiko dengan neural network Exercise 10: Jaringan neural satu lapis Exercise 11: Prediksi harga aset Exercise 12: Manajemen risiko real-time Exercise 13: Ringkasan dan Langkah Selanjutnya