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GARCH Models in R

Specify and fit GARCH models to forecast time-varying volatility and value-at-risk.

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Course Description

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.

  1. 1

    The standard GARCH model as the workhorse model

    Free

    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.

    Play Chapter Now
    Analyzing volatility
    50 xp
    Computing returns
    100 xp
    Standard deviation on subsamples
    100 xp
    Roll, roll, roll
    100 xp
    The GARCH equation for volatility prediction
    50 xp
    GARCH(1,1) reaction to one-off shocks
    50 xp
    Prediction errors
    100 xp
    The recursive nature of the GARCH variance
    100 xp
    The rugarch package
    50 xp
    Specify and taste the GARCH model flavors
    100 xp
    Out-of-sample forecasting
    100 xp
    Volatility targeting in tactical asset allocation
    100 xp
  2. 2

    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.

    Play Chapter Now
    Non-normality of standardized returns
    50 xp
    Skewed student t distribution parameters
    50 xp
    Estimation of non-normal GARCH model
    100 xp
    Standardized returns
    100 xp
    Leverage effect
    50 xp
    News impact curve
    50 xp
    Estimation of GJR garch model
    100 xp
    Mean model
    50 xp
    Predicting returns
    50 xp
    The AR(1)-GJR GARCH dynamics of MSFT returns
    100 xp
    Effect of mean model on volatility predictions
    100 xp
    Avoid unnecessary complexity
    50 xp
    Modeling choices
    50 xp
    Fixing GARCH parameters
    100 xp
    Parameter bounds and impact on forecasts
    100 xp
    Variance targeting
    100 xp
  3. 3

    Performance evaluation

    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.

    Play Chapter Now
    Statistical significance
    50 xp
    Significance testing
    50 xp
    Analyzing estimation output
    100 xp
    A better model for EUR/USD returns
    100 xp
    Goodness of fit
    50 xp
    Parsimony
    50 xp
    Mean squared prediction errors
    100 xp
    Comparing likelihood and information criteria
    100 xp
    Diagnosing absolute standardized returns
    50 xp
    Validation of GARCH model assumptions
    50 xp
    Correlogram and Ljung-Box test
    100 xp
    Change estimation sample
    100 xp
    Backtesting using ugarchroll
    50 xp
    ugarchroll arguments
    50 xp
    In-sample versus rolling sample vol
    100 xp
    Horse race
    100 xp
  4. 4

    Applications

    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.

    Play Chapter Now
    Value-at-risk
    50 xp
    VaR plot
    50 xp
    Comovement between predicted vol and VaR
    100 xp
    Sensitivity of coverage to distribution model
    100 xp
    For production and simulation
    50 xp
    Actual versus simulated returns
    50 xp
    Use in production
    100 xp
    Use in simulation
    100 xp
    Model risk
    50 xp
    Robustniks
    50 xp
    Starting values
    100 xp
    GARCH covariance
    50 xp
    Estimation of beta
    50 xp
    Minimum variance portfolio weights
    100 xp
    GARCH & Co
    100 xp
    Congratulations
    50 xp

In the following tracks

Applied Finance

Datasets

Daily EUR/USD returnsDaily Microsoft returnsS&P 500 pricesS&P 500 returnsSimulated return data

Collaborators

Chester IsmayHadrien LacroixSara Billen

Prerequisites

Time Series Analysis in RManipulating Time Series Data with xts and zoo in R
Kris Boudt Headshot

Kris Boudt

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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