Peramalan out-of-sample

Deret garchvol adalah deret volatilitas yang diprediksi untuk setiap return dalam deret waktu teramati sp500ret. Untuk pengambilan keputusan, yang penting adalah volatilitas return masa depan (yang belum teramati). Anda memperolehnya dengan menerapkan fungsi ugarchforecast() pada keluaran dari ugarchfit(). Dalam peramalan, ini disebut peramalan volatilitas out-of-sample, karena mencakup prediksi return yang tidak digunakan saat mengestimasi model GARCH.

Latihan ini menggunakan objek garchfit dan garchvol yang Anda buat pada latihan sebelumnya. Jika Anda perlu memeriksa argumen apa saja yang diterima suatu fungsi, Anda dapat menggunakan ?name_of_function di Console untuk mengakses dokumentasi.

Latihan ini adalah bagian dari kursus

Model GARCH di R

Petunjuk latihan

Hitung volatilitas tanpa syarat menggunakan metode uncvariance().
Cetak volatilitas terestimasi untuk sepuluh return terakhir dalam sampel sp500ret.
Gunakan ugarchforecast() untuk meramalkan volatilitas lima hari ke depan.
Gunakan sigma() untuk memperoleh volatilitas yang diprediksi untuk lima hari ke depan dan cetak nilainya.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Compute unconditional volatility
___(___(garchfit))

# Print last 10 ones in garchvol
tail(___, ___)

# Forecast volatility 5 days ahead and add 
garchforecast <- ___(fitORspec = garchfit, 
                     ___ = ___)

# Extract the predicted volatilities and print them
print(___(___))

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Model GARCH di R

SkillTag.level.advancedSkillTag.label

4.8+

Mulai Kursus Gratis

We start off by making our hands dirty. A rolling window analysis of daily stock returns shows that its standard deviation changes massively through time. Looking back at the past, we thus have clear evidence of time-varying volatility. Looking forward, we need to estimate the volatility of future returns. This is essentially what a GARCH model does! In this chapter, you will learn the basics of using the rugarch package for specifying and estimating the workhorse GARCH(1,1) model in R. We end by showing its usefulness in tactical asset allocation.

Exercise 1: Menganalisis volatilitas Exercise 2: Menghitung return Exercise 3: Simpangan baku pada sub-sampel Exercise 4: Gulung, gulung, gulung Exercise 5: Persamaan GARCH untuk prediksi volatilitas Exercise 6: Reaksi GARCH(1,1) terhadap kejutan satu kali Exercise 7: Galat prediksi Exercise 8: Sifat rekursif dari ragam GARCH Exercise 9: Paket rugarch Exercise 10: Spesifikasikan dan cicipi berbagai varian model GARCH Exercise 11: Peramalan out-of-sample

Latihan Saat Ini

Exercise 12: Penetapan target volatilitas dalam alokasi aset taktis

Markets take the stairs up and the elevator down. This Wallstreet wisdom has important consequences for specifying a realistic volatility model. It requires to give up the assumption of normality, as well as the symmetric response of volatility to shocks. In this chapter, you will learn about GARCH models with a leverage effect and skewed student t innovations. At the end, you will be able to use GARCH models for estimating over ten thousand different GARCH model specifications.

Exercise 1: Non-normality of standardized returns Exercise 2: Skewed student t distribution parameters Exercise 3: Estimation of non-normal GARCH model Exercise 4: Standardized returns Exercise 5: Leverage effect Exercise 6: News impact curve Exercise 7: Estimation of GJR garch model Exercise 8: Mean model Exercise 9: Predicting returns Exercise 10: The AR(1)-GJR GARCH dynamics of MSFT returns Exercise 11: Effect of mean model on volatility predictions Exercise 12: Avoid unnecessary complexity Exercise 13: Modeling choices Exercise 14: Fixing GARCH parameters Exercise 15: Parameter bounds and impact on forecasts Exercise 16: Variance targeting

GARCH models yield volatility forecasts which serve as input for financial decision making. Their use in practice requires to first evaluate the goodness of the volatility forecast. In this chapter, you will learn about the analysis of statistical significance of the estimated GARCH parameters, the properties of standardized returns, the interpretation of information criteria and the use of rolling GARCH estimation and mean squared prediction errors to analyze the accuracy of the volatility forecast.

Exercise 1: Statistical significance Exercise 2: Significance testing Exercise 3: Analyzing estimation output Exercise 4: A better model for EUR/USD returns Exercise 5: Goodness of fit Exercise 6: Parsimony Exercise 7: Mean squared prediction errors Exercise 8: Comparing likelihood and information criteria Exercise 9: Diagnosing absolute standardized returns Exercise 10: Validation of GARCH model assumptions Exercise 11: Correlogram and Ljung-Box test Exercise 12: Change estimation sample Exercise 13: Backtesting using ugarchroll Exercise 14: ugarchroll arguments Exercise 15: In-sample versus rolling sample vol Exercise 16: Horse race

At this stage, you master the standard specification, estimation and validation of GARCH models in the rugarch package. This chapter introduces specific rugarch functionality for making value-at-risk estimates, for using the GARCH model in production and for simulating GARCH returns. You will also discover that the presence of GARCH dynamics in the variance has implications for simulating log-returns, the estimation of the beta of a stock and finding the minimum variance portfolio.

Exercise 1: Value-at-risk Exercise 2: VaR plot Exercise 3: Comovement between predicted vol and VaR Exercise 4: Sensitivity of coverage to distribution model Exercise 5: For production and simulation Exercise 6: Actual versus simulated returns Exercise 7: Use in production Exercise 8: Use in simulation Exercise 9: Model risk Exercise 10: Robustniks Exercise 11: Starting values Exercise 12: GARCH covariance Exercise 13: Estimation of beta Exercise 14: Minimum variance portfolio weights Exercise 15: GARCH & Co Exercise 16: Congratulations