Wijzig de schattingssteekproef

Er kunnen meerdere redenen zijn om de geldigheid van het GARCH-model te verwerpen. Het kan liggen aan een onjuiste aanname over het gemiddelde, de variantie of de verdeling. Het kan ook zo zijn dat de tijdreeks van rendementen niet door één set GARCH-parameters kan worden beschreven. Gezien de dynamiek van financiële markten is het zelfs realistisch om te verwachten dat GARCH-parameters in de tijd veranderen. Laten we daarom ons GARCH-model opnieuw schatten op de 2500 meest recente EUR/USD-rendementen, in plaats van de analyse te doen op alle 4961 rendementen.

Deze oefening maakt deel uit van de cursus

GARCH-modellen in R

Cursus bekijken

Oefeninstructies

Gebruik de functie tail() om het GARCH-model te schatten op de laatste 2500 observaties
Bereken de gestandaardiseerde rendementen
Voer de Ljung-Box-toets uit dat alle autocorrelaties van orde 1,…,22 nul zijn.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Estimate the model on the last 2500 observations
tgarchspec <- ___( mean.model = list(armaOrder = c(0,0)),
                        variance.model = list(model = "sGARCH"),
                        distribution.model = "std")
tgarchfit <- ___( data = ___(EURUSDret, ___) , spec = tgarchspec)

# Compute standardized returns
stdEURUSDret <- ___(tgarchfit, standardize = TRUE)

# Do the Ljung-Box test on the absolute standardized returns
___(abs(stdEURUSDret), 22, type = "Ljung-Box")

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

GARCH-modellen in R

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Begin de cursus 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: Statistische significantie Exercise 2: Significantietoetsing Exercise 3: De uitkomst van de schatting analyseren Exercise 4: Een beter model voor EUR/USD-rendementen Exercise 5: Modelpassing (goodness of fit)Exercise 6: Zuinigheid Exercise 7: Mean Squared Prediction Errors Exercise 8: Likelihood en informatiecriteria vergelijken Exercise 9: Diagnose van absolute gestandaardiseerde rendementen Exercise 10: Validatie van GARCH-modelaannames Exercise 11: Correlogram en Ljung-Box-toets Exercise 12: Wijzig de schattingssteekproef

Huidige oefening

Exercise 13: Backtesten met ugarchroll Exercise 14: Argumenten van ugarchroll Exercise 15: In-sample versus rolling sample vol Exercise 16: Paardenrace

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