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Curs

GARCH Models in R

AvansatNivel de competențe

Actualizat 08.2024

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

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RApplied Finance

4 h

16 videoclipuri

60 Exerciții

4,550 XP

8,776

Certificat de realizare

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Descrierea cursului

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.

Cerințe prealabile

Time Series Analysis in R Manipulating Time Series Data in R

1

The Standard GARCH Model as the Workhorse Model

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 volatility

Computing returns

Standard deviation on subsamples

Roll, roll, roll

The GARCH equation for volatility prediction

GARCH(1,1) reaction to one-off shocks

Prediction errors

The recursive nature of the GARCH variance

The rugarch package

Specify and taste the GARCH model flavors

Out-of-sample forecasting

Volatility targeting in tactical asset allocation

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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.

Non-normality of standardized returns

Skewed student t distribution parameters

Estimation of non-normal GARCH model

Standardized returns

Leverage effect

News impact curve

Estimation of GJR garch model

Predicting returns

The AR(1)-GJR GARCH dynamics of MSFT returns

Effect of mean model on volatility predictions

Avoid unnecessary complexity

Modeling choices

Fixing GARCH parameters

Parameter bounds and impact on forecasts

Variance targeting

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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.

Statistical significance

Significance testing

Analyzing estimation output

A better model for EUR/USD returns

Goodness of fit

Mean squared prediction errors

Comparing likelihood and information criteria

Diagnosing absolute standardized returns

Validation of GARCH model assumptions

Correlogram and Ljung-Box test

Change estimation sample

Backtesting using ugarchroll

ugarchroll arguments

In-sample versus rolling sample vol

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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.

Value-at-risk

Comovement between predicted vol and VaR

Sensitivity of coverage to distribution model

For production and simulation

Actual versus simulated returns

Use in production

Use in simulation

Starting values

GARCH covariance

Estimation of beta

Minimum variance portfolio weights

Congratulations

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GARCH Models in R

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