De AR(1)-GJR GARCH-dynamiek van MSFT-rendementen

In de video heb je gezien dat het teken van de autoregressieve parameter in het AR(1)-model afhangt van de marktreactie op nieuws

Een positieve waarde van \(\rho\) past bij de interpretatie dat markten onderreageren op nieuws, wat leidt tot momentum in rendementen. Een negatieve waarde van \(\rho\) past bij de interpretatie dat markten overreageren op nieuws, wat leidt tot een omkeer in rendementen.

Worden de dagelijkse Microsoft-rendementen gekenmerkt door momentum of juist een omkeereffect in hun AR(1)-dynamiek? Laten we dit uitzoeken door de parameters van het AR(1)-GJR GARCH-model te schatten met de dagelijkse Microsoft-rendementen in msftret.

Deze oefening maakt deel uit van de cursus

GARCH-modellen in R

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Oefeninstructies

armaOrder = c(1,2) komt overeen met een ARMA(1,2)-model. Een AR(1)-model is hetzelfde als ARMA(1,0).
Maak het argument mean.model in ugarchspec af om het te gebruiken AR(1)-model te specificeren.
Schat het model.
Print de eerste twee coëfficiënten van het geschatte GARCH-model.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# 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)]

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

GARCH-modellen in R

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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: Niet-normaliteit van gestandaardiseerde rendementen Exercise 2: Parameters van de scheve student-t-verdeling Exercise 3: Schatting van een niet-normaal GARCH-model Exercise 4: Gestandaardiseerde rendementen Exercise 5: Leverage-effect Exercise 6: News impact curve Exercise 7: Schatting van het GJR-GARCH-model Exercise 8: Gemiddeldemodel Exercise 9: Rendementen voorspellen Exercise 10: De AR(1)-GJR GARCH-dynamiek van MSFT-rendementen

Huidige oefening

Exercise 11: Effect van het mean-model op volatiliteitsvoorspellingen Exercise 12: Vermijd onnodige complexiteit Exercise 13: Modelleerveuzes Exercise 14: GARCH-parameters vastzetten Exercise 15: Parametergrenzen en impact op voorspellingen 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