Estimasi parameter: Normal

Estimasi parameter adalah metode terkuat untuk mengestimasi VaR karena mengasumsikan bahwa kelas sebaran kerugian sudah diketahui. Parameter diestimasi agar data sesuai dengan sebaran tersebut, lalu dilakukan inferensi statistik.

Dalam latihan ini, Anda akan mengestimasi VaR 95% dari sebaran Normal yang dipasangkan ke data bank investasi tahun 2007–2009. Anda akan menggunakan sebaran norm dari scipy.stats, dengan asumsi bahwa kelas sebaran ini paling sesuai.

Apakah sebaran Normal cocok? Anda akan mengujinya dengan uji Anderson-Darling scipy.stats.anderson. Jika hasil uji berbeda secara statistik dari nol, ini mengindikasikan data tidak berdistribusi Normal. Anda akan menanganinya pada latihan berikutnya.

losses portofolio untuk periode 2005–2010 tersedia.

Latihan ini adalah bagian dari kursus

Manajemen Risiko Kuantitatif dengan Python

Petunjuk latihan

Impor norm dan anderson dari scipy.stats.
Pasangkan data losses ke sebaran Normal menggunakan metode .fit(), lalu simpan parameter sebaran ke params.
Hasilkan dan tampilkan estimasi VaR 95% dari sebaran yang telah dipasangkan.
Uji hipotesis nol bahwa losses berdistribusi Normal menggunakan uji Anderson-Darling anderson().

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Import the Normal distribution and skewness test from scipy.stats
from ____ import norm, anderson

# Fit portfolio losses to the Normal distribution
params = ____.fit(____)

# Compute the 95% VaR from the fitted distribution, using parameter estimates
VaR_95 = norm.____(0.95, *params)
print("VaR_95, Normal distribution: ", VaR_95)

# Test the data for Normality
print("Anderson-Darling test result: ", anderson(____))

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: Estimasi Parametrik Exercise 2: Estimasi parameter: Normal

Latihan Saat Ini

Exercise 3: Estimasi parameter: Skewed Normal Exercise 4: Simulasi Historis dan Monte Carlo Exercise 5: Simulasi Historis Exercise 6: Simulasi Monte Carlo Exercise 7: Pemutusan struktural Exercise 8: Pemutusan struktural krisis: I Exercise 9: Pemutusan struktural krisis: II Exercise 10: Patah struktural saat krisis: III Exercise 11: Volatilitas dan nilai ekstrem Exercise 12: Volatilitas dan break struktural Exercise 13: Nilai ekstrem dan 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