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R에서의 GARCH 모델

고급기술 수준

업데이트됨 2024. 8.

변동하는 변동성과 VaR 예측을 위해 GARCH 모델을 정의하고 적합합니다.

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RApplied Finance

4시간

16 동영상

60 연습 문제

4,550 XP

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금융시장의 맥박이 어떻게 뛰는지 궁금하신가요? 안정적이던 시장이 언제 요동치기 시작하는지 알고 싶으신가요? 이 GARCH 모델 강의에서는 재무 의사결정에서 위험과 수익을 균형 있게 바라보는 미래지향적 접근을 배웁니다. 표준 정규 GARCH(1,1) 모델에서 시작해, 레버리지 효과가 있는 더 발전된 변동성 모델, 평균방정식에 GARCH를 포함하는 사양, 그리고 자산 수익률 모형화를 위한 왜도 있는 스튜던트 t 분포까지 차근차근 다룹니다. 주식 및 환율 수익률을 대상으로 포트폴리오 최적화, 롤링 샘플 예측 평가, Value-at-Risk 예측, 동적 공분산 분석 등의 응용 사례도 살펴봅니다.

선수 조건

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

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

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

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

R에서의 GARCH 모델

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