Pengantar resampling frekuensi

Model faktor risiko sering kali bergantung pada data dengan frekuensi yang berbeda. Contoh umum adalah saat menggunakan data makroekonomi triwulanan, seperti harga, tingkat pengangguran, dan sebagainya, bersama data keuangan yang umumnya harian (bahkan intra-harian). Untuk menggunakan kedua sumber data dalam model yang sama, data berfrekuensi lebih tinggi perlu di-resample agar sesuai dengan data berfrekuensi lebih rendah.

Objek Pandas DataFrame dan Series memiliki metode bawaan .resample() yang menentukan frekuensi yang lebih rendah. Metode ini dirangkai dengan metode untuk membuat statistik frekuensi lebih rendah, seperti .mean() untuk rata-rata data dalam periode frekuensi baru, atau .min() untuk nilai minimum dari data.

Dalam latihan ini Anda akan berlatih mengonversi data returns harian ke frekuensi mingguan dan triwulanan.

Latihan ini adalah bagian dari kursus

Manajemen Risiko Kuantitatif dengan Python

Petunjuk latihan

Konversi returns ke rata-rata frekuensi triwulanan returns_q menggunakan metode .resample() dan .mean().
Periksa bagian awal returns_q, perhatikan bahwa metode .resample() otomatis menangani indeks tanggal untuk Anda.
Sekarang konversi returns ke nilai minimum frekuensi mingguan returns_w, menggunakan metode .min().
Periksa bagian awal returns_w.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Convert daily returns to quarterly average returns
returns_q = returns.____('Q').____

# Examine the beginning of the quarterly series
print(returns_q.____)

# Now convert daily returns to weekly minimum returns
returns_w = ____.resample('W').____

# Examine the beginning of the weekly series
print(returns_w.____)

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: Selamat datang!Exercise 2: Imbal hasil portofolio selama krisis Exercise 3: Kovarians aset dan volatilitas portofolio Exercise 4: Faktor risiko dan krisis keuangan Exercise 5: Pengantar resampling frekuensi

Latihan Saat Ini

Exercise 6: Memvisualisasikan korelasi faktor risiko Exercise 7: Model faktor least-squares Exercise 8: Teori portofolio modern Exercise 9: Latihan dengan PyPortfolioOpt: return Exercise 10: Latihan dengan PyPortfolioOpt: kovarians Exercise 11: Menguraikan krisis keuangan Exercise 12: Efficient frontier dan krisis keuangan

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: 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