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GARCH Models in R

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Updated 12/2024

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

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RApplied Finance4 hours16 videos60 exercises4,550 XP7,647Statement of Accomplishment

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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.

Prerequisites

Time Series Analysis in R Manipulating Time Series Data in R

1

The Standard GARCH Model as the Workhorse Model

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

2

Improvements of the Normal GARCH Model

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

3

Performance Evaluation

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

4

Applications

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

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GARCH Models in R

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