Estimasi risiko GEV

Misalkan Anda memegang saham GE senilai € 1.000.000 pada 1 Januari 2010. Anda ingin menutup kerugian maksimum yang diharapkan yang mungkin terjadi selama minggu berikutnya, berdasarkan data yang tersedia dari dua tahun sebelumnya, 2008–2009. Anda berasumsi bahwa kerugian maksimum mingguan untuk GE didistribusikan menurut distribusi Generalized Extreme Value (GEV).

Untuk memodelkan kerugian yang diharapkan, Anda akan mengestimasi CVaR pada tingkat kepercayaan 99% untuk distribusi GEV, dan menggunakannya untuk menghitung jumlah cadangan yang diperlukan guna menutup kerugian maksimum mingguan yang diharapkan selama Januari 2010.

Distribusi genextreme dari scipy.stats tersedia di ruang kerja Anda, demikian pula losses GE untuk periode 2008–2009.

Latihan ini adalah bagian dari kursus

Manajemen Risiko Kuantitatif dengan Python

Petunjuk latihan

Temukan nilai maksimum harga aset GE untuk panjang blok satu minggu.
Sesuaikan distribusi GEV genextreme ke data weekly_maxima.
Hitung VaR 99%, dan gunakan hasilnya untuk menemukan estimasi CVaR 99%.
Hitung jumlah cadangan yang diperlukan untuk menutup kerugian maksimum mingguan yang diharapkan.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Compute the weekly block maxima for GE's stock
weekly_maxima = losses.____("W").____()

# Fit the GEV distribution to the maxima
p = genextreme.____(____)

# Compute the 99% VaR (needed for the CVaR computation)
VaR_99 = genextreme.____(____, *p)

# Compute the 99% CVaR estimate
CVaR_99 = (1 / (1 - 0.99)) * genextreme.____(lambda x: x, 
           args=(p[0],), loc = p[1], scale = p[2], lb = ____)

# Display the covering loss amount
print("Reserve amount: ", 1000000 * CVaR_99)

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: 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 Exercise 4: Estimasi risiko GEV

Latihan Saat Ini

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