Volatilitas dan break struktural

Memvisualisasikan perubahan volatilitas membantu mengungkap kemungkinan titik break struktural pada deret waktu. Dengan mengidentifikasi kapan volatilitas tampak berubah, Anda dapat memilih titik break secara lebih tepat yang selanjutnya dapat digunakan untuk analisis statistik lanjutan (seperti uji Chow).

Anda akan menelaah dua visualisasi volatilitas untuk portofolio bank investasi dari 2008–2009, untuk dua bobot portofolio yang tersedia: weights_with_citi dan weights_without_citi. Keduanya masing-masing merepresentasikan portofolio berbobot sama dengan dan tanpa Citibank, yang (seperti yang Anda lihat di Bab 1) menunjukkan volatilitas tertinggi dari empat aset sepanjang periode tersebut.

Harga portofolio untuk 2008–2009 dengan Citibank tersedia sebagai prices_with_citi, dan tanpa Citibank sebagai prices_without_citi.

Latihan ini adalah bagian dari kursus

Manajemen Risiko Kuantitatif dengan Python

Petunjuk latihan

Temukan deret return untuk dua portofolio menggunakan weights_with_citi dan weights_without_citi.
Hitung deviasi standar rolling window 30 hari untuk kedua portofolio.
Gabungkan kedua objek Series Pandas menjadi satu objek DataFrame bernama "vol".
Plot isi objek vol untuk membandingkan volatilitas kedua portofolio dari waktu ke waktu.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Find the time series of returns with and without Citibank
ret_with_citi = prices_with_citi.____.dot(weights_with_citi)
ret_without_citi = prices_without_citi.pct_change().____(____)

# Find the average 30-day rolling window volatility as the standard deviation
vol_with_citi = ret_with_citi.____.std().dropna().rename("With Citi")
vol_without_citi = ret_without_citi.rolling(30).____.dropna().rename("Without Citi")

# Combine two volatilities into one Pandas DataFrame
vol = pd.concat([____, ____], axis=1)

# Plot volatilities over time
vol.____().set_ylabel("Losses")
plt.show()

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 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

Latihan Saat Ini

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