Galat prediksi kuadrat rata-rata (MSE)

Model GJR GARCH merupakan generalisasi dari model GARCH. Karena itu, model ini seharusnya memberikan kecocokan yang lebih baik dalam hal Mean Squared Errors (MSE) yang lebih rendah. Mari kita verifikasi pada imbal hasil Microsoft msftret, di mana garchfit adalah hasil estimasi dengan model GARCH(1,1) standar, sedangkan gjrfit adalah ketika model GJR digunakan. Ingat bahwa Anda dapat menghitung vektor galat prediksi \(e\) untuk rata-rata menggunakan metode residuals(). Galat prediksi untuk varians sama dengan selisih antara \(e^2\) dan varians GARCH yang diprediksi.

Latihan ini adalah bagian dari kursus

Model GARCH di R

Petunjuk latihan

Hitung vektor galat prediksi untuk rata-rata menggunakan metode residuals().
Lengkapi kode untuk menghitung MSE untuk keluaran estimasi garchfit.
Hitung MSE untuk keluaran estimasi gjrfit.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Compute prediction errors
garcherrors <- ___(garchfit)
gjrerrors  <- ___(gjrfit)

# Compute MSE for variance prediction of garchfit model
___((___(garchfit)___ - garcherrors^2)___)

# Compute MSE for variance prediction of gjrfit model
___

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: 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: Signifikansi statistik Exercise 2: Pengujian signifikansi Exercise 3: Menganalisis output estimasi Exercise 4: Model yang lebih baik untuk imbal hasil EUR/USD Exercise 5: Kecocokan Model Exercise 6: Parsimoni Exercise 7: Galat prediksi kuadrat rata-rata (MSE)

Latihan Saat Ini

Exercise 8: Membandingkan likelihood dan kriteria informasi Exercise 9: Mendiagnosis return tersandardisasi absolut Exercise 10: Validasi asumsi model GARCH Exercise 11: Korelogram dan uji Ljung-Box Exercise 12: Ubah sampel estimasi Exercise 13: Uji balik menggunakan ugarchroll Exercise 14: Argumen ugarchroll Exercise 15: Volatilitas in-sample versus rolling sample Exercise 16: Adu cepat

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