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Are you curious about the rhythm of the financial market's heartbeat? Do you want to know when a stable market becomes turbulent? In this course on GARCH models you will learn the forward looking approach to balancing risk and reward in financial decision making. The course gradually moves from the standard normal GARCH(1,1) model to more advanced volatility models with a leverage effect, GARCH-in-mean specification and the use of the skewed student t distribution for modelling asset returns. Applications on stock and exchange rate returns include portfolio optimization, rolling sample forecast evaluation, value-at-risk forecasting and studying dynamic covariances.
The standard GARCH model as the workhorse modelFree
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.Analyzing volatility50 xpComputing returns100 xpStandard deviation on subsamples100 xpRoll, roll, roll100 xpThe GARCH equation for volatility prediction50 xpGARCH(1,1) reaction to one-off shocks50 xpPrediction errors100 xpThe recursive nature of the GARCH variance100 xpThe rugarch package50 xpSpecify and taste the GARCH model flavors100 xpOut-of-sample forecasting100 xpVolatility targeting in tactical asset allocation100 xp
Improvements of the normal GARCH model
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.Non-normality of standardized returns50 xpSkewed student t distribution parameters50 xpEstimation of non-normal GARCH model100 xpStandardized returns100 xpLeverage effect50 xpNews impact curve50 xpEstimation of GJR garch model100 xpMean model50 xpPredicting returns50 xpThe AR(1)-GJR GARCH dynamics of MSFT returns100 xpEffect of mean model on volatility predictions100 xpAvoid unnecessary complexity50 xpModeling choices50 xpFixing GARCH parameters100 xpParameter bounds and impact on forecasts100 xpVariance targeting100 xp
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.Statistical significance50 xpSignificance testing50 xpAnalyzing estimation output100 xpA better model for EUR/USD returns100 xpGoodness of fit50 xpParsimony50 xpMean squared prediction errors100 xpComparing likelihood and information criteria100 xpDiagnosing absolute standardized returns50 xpValidation of GARCH model assumptions50 xpCorrelogram and Ljung-Box test100 xpChange estimation sample100 xpBacktesting using ugarchroll50 xpugarchroll arguments50 xpIn-sample versus rolling sample vol100 xpHorse race100 xp
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.Value-at-risk50 xpVaR plot50 xpComovement between predicted vol and VaR100 xpSensitivity of coverage to distribution model100 xpFor production and simulation50 xpActual versus simulated returns50 xpUse in production100 xpUse in simulation100 xpModel risk50 xpRobustniks50 xpStarting values100 xpGARCH covariance50 xpEstimation of beta50 xpMinimum variance portfolio weights100 xpGARCH & Co100 xpCongratulations50 xp
In the following tracksApplied Finance
DatasetsDaily EUR/USD returnsDaily Microsoft returnsS&P 500 pricesS&P 500 returnsSimulated return data
Professor of Finance and Econometrics at VUB and VUA
Kris Boudt is professor of finance and econometrics at Ghent University, Vrije Universiteit Brussel and Amsterdam. He teaches the courses "GARCH models in R" and "Introduction to portfolio analysis in R" at DataCamp. He is a member of the Sentometrics organization. He is also affiliated with the KU Leuven and an invited lecturer at the University of Illinois in Chicago, Renmin University, Sichuan University, SWUFE and the University of Aix-Marseille. Kris Boudt obtained his PhD in 2008 for his developments in the modelling and estimation of financial risk under non-normal distribution. He has published his research in the Journal of Banking and Finance, Journal of Econometrics, Journal of Portfolio Management, Journal of Financial Econometrics, and the Review of Finance, among others. Kris Boudt received several awards for outstanding research and refereeing and is an active contributor to the open source community.
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