Menetapkan parameter GARCH

Parameter model GARCH diestimasi dengan likelihood maksimum. Karena adanya ketidakpastian sampel, parameter hasil estimasi pasti mengandung galat estimasi. Jika kita mengetahui nilai parameter sebenarnya, maka sebaiknya nilai tersebut dipaksakan dan tidak diestimasi.

Mari kita terapkan pada return harian EUR/USD yang tersedia di konsol sebagai variabel EURUSDret, dan untuknya sebuah model AR(1)-GARCH dengan sebaran student t miring telah diestimasi dan tersedia sebagai objek ugarchfit bernama flexgarchfit.

Latihan ini adalah bagian dari kursus

Model GARCH di R

Petunjuk latihan

Cetak estimasi koefisien dari flexgarchfit.
Gunakan metode setfixed() untuk menetapkan pembatasan parameter ar1 = 0 dan skew = 1.
Estimasikan model dengan pembatasan parameter tersebut.
Lengkapi kode untuk memplot dua deret volatilitas dan perhatikan kemiripannya.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Print the flexible GARCH parameters
___

# Restrict the flexible GARCH model by impose a fixed ar1 and skew parameter
rflexgarchspec <- flexgarchspec
___(rflexgarchspec) <- list(___ = ___, ___ = ___)

# Estimate the restricted GARCH model
rflexgarchfit <- ugarchfit(data = ___,  spec = ___)

# Compare the volatility of the unrestricted and restriced GARCH models
plotvol <- plot(abs(EURUSDret), col = "grey")
plotvol <- addSeries(___(flexgarchfit), col = "black", lwd = 4, on=1 )
plotvol <- addSeries(___(rflexgarchfit), col = "red", on=1)
plotvol

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: Analyzing volatility Exercise 2: Computing returns Exercise 3: Standard deviation on subsamples Exercise 4: Roll, roll, roll Exercise 5: The GARCH equation for volatility prediction Exercise 6: GARCH(1,1) reaction to one-off shocks Exercise 7: Prediction errors Exercise 8: The recursive nature of the GARCH variance Exercise 9: The rugarch package Exercise 10: Specify and taste the GARCH model flavors Exercise 11: Out-of-sample forecasting Exercise 12: Volatility targeting in tactical asset allocation

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: Ketidaknormalan dari imbal hasil yang distandardisasi Exercise 2: Parameter sebaran student t menceng (skewed student t)Exercise 3: Estimasi model GARCH non-normal Exercise 4: Return terstandarisasi Exercise 5: Efek leverage Exercise 6: Kurva dampak berita Exercise 7: Estimasi model GJR GARCH Exercise 8: Model mean Exercise 9: Memprediksi return Exercise 10: Dinamika AR(1)-GJR GARCH dari imbal hasil MSFT Exercise 11: Dampak model mean pada prediksi volatilitas Exercise 12: Hindari kompleksitas yang tidak perlu Exercise 13: Pilihan pemodelan Exercise 14: Menetapkan parameter GARCH

Latihan Saat Ini

Exercise 15: Batas parameter dan dampaknya pada peramalan 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