Dinamika AR(1)-GJR GARCH dari imbal hasil MSFT

Anda telah melihat di video bahwa tanda dari parameter autoregresif dalam model AR(1) bergantung pada reaksi pasar terhadap berita

Nilai positif \(\rho\) konsisten dengan interpretasi bahwa pasar kurang bereaksi terhadap berita sehingga memunculkan momentum dalam imbal hasil. Nilai negatif \(\rho\) konsisten dengan interpretasi bahwa pasar bereaksi berlebihan terhadap berita sehingga memunculkan pembalikan dalam imbal hasil.

Apakah imbal hasil harian Microsoft dicirikan oleh efek momentum atau pembalikan dalam dinamika AR(1)-nya? Mari kita cari tahu dengan mengestimasi parameter model AR(1)-GJR GARCH menggunakan imbal hasil harian Microsoft pada msftret.

Latihan ini adalah bagian dari kursus

Model GARCH di R

Petunjuk latihan

armaOrder = c(1,2) merepresentasikan model ARMA(1,2). Model AR(1) sama dengan ARMA(1,0).
Lengkapi argumen mean.model di ugarchspec untuk menetapkan model AR(1) yang akan digunakan.
Estimasikan model.
Cetak dua koefisien pertama dari model GARCH yang diestimasi.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Specify AR(1)-GJR GARCH model
garchspec <- ugarchspec(mean.model = list(armaOrder = ___ ),
                        variance.model = list(model = "gjrGARCH"),
                        distribution.model = "sstd")

# Estimate the model
garchfit <- ___

# Print the first two coefficients
___(___)[c(1:2)]

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Model GARCH di R

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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

Latihan Saat Ini

Exercise 11: Dampak model mean pada prediksi volatilitas Exercise 12: Hindari kompleksitas yang tidak perlu Exercise 13: Pilihan pemodelan Exercise 14: Menetapkan parameter GARCH 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