Meminimalkan CVaR

Latihan ini akan memberi Anda praktik menggunakan alat PyPortfolioOpt untuk meminimalkan CVaR sebagai tujuan manajemen risiko.

Anda akan memuat modul pypfopt.efficient_frontier dan mengambil kelas EfficientCVaR, lalu membuat instance kelas tersebut menggunakan aset bank investasi untuk periode 2005–2010.

Selanjutnya, Anda akan menggunakan metode min_cvar() dari instance tersebut untuk menemukan bobot portofolio optimal yang meminimalkan CVaR.

Return aset portofolio berada dalam vektor returns—latihan ini juga menggunakan kamus names untuk memetakan bobot portofolio ke nama bank.

Latihan ini adalah bagian dari kursus

Manajemen Risiko Kuantitatif dengan Python

Petunjuk latihan

Impor kelas EfficientCVaR dari pypfopt.efficient_frontier.
Buat instance kelas EfficientCVaR bernama ec menggunakan returns; perhatikan Anda tidak memerlukan expected_returns, karena fungsi objektifnya berbeda dari optimisasi mean-variance.
Temukan dan tampilkan portofolio optimal menggunakan metode .min_cvar() milik ec.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Import the EfficientCVaR class
from pypfopt.____ import EfficientCVaR

# Create the efficient frontier for CVaR minimization
ec = ____(None, ____)

# Find the cVaR-minimizing portfolio weights at the default 95% confidence level
optimal_weights = ec.____()

# Map the values in optimal_weights to the bank names
optimal_weights = { names[i] : optimal_weights[i] for i in optimal_weights}

# Display the optimal weights
print(optimal_weights)

Edit dan Jalankan Kode

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: Mengukur Risiko Exercise 2: VaR untuk distribusi Normal Exercise 3: Membandingkan CVaR dan VaR Exercise 4: Ukuran risiko mana yang "lebih baik"?Exercise 5: Eksposur risiko dan kerugian Exercise 6: Seberapa besar selera risiko Anda?Exercise 7: VaR dan eksposur risiko Exercise 8: CVaR dan eksposur risiko Exercise 9: Manajemen risiko menggunakan VaR & CVaR Exercise 10: VaR dari distribusi terestimasi Exercise 11: Meminimalkan CVaR

Latihan Saat Ini

Exercise 12: Manajemen risiko CVaR dan krisis Exercise 13: Lindung nilai portofolio: mengimbangi risiko Exercise 14: Penetapan harga opsi Black-Scholes Exercise 15: Penetapan harga opsi dan aset dasar Exercise 16: Menggunakan opsi untuk lindung nilai

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