EVT 및 CAViaR 모형에 의한 국고채 VaR 예측모형 분석
VaR Forecasts for Korea TreasuryBonds via EVT and CAViaR Models
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초록
본 논문은 시장리스크의 주된 측정수단인 VaR(value at risk)의 여러 예측모형을 한국의 국고채(1년, 5년, 10년 만기 할인채) 보유수익률에 적용하여 비교 분석하였다. 특히 EVT(extreme value theory)를 적용한 모형과 CAViaR(conditional autoregressive value at risk) 모형의 예측성과에 초점을 맞추었다. 실증 분석 결과, 채권 보유수익률을 GARCH(generalized autoregressive conditional heteroskedasticity) 모형에 필터링한 후 EVT를 적용한 EVT-GARCH 모형의 성과가 1% 및 5% VaR 예측 모두에게서 우수한 것으로 나타나, 기존에 널리 사용되고 있는 역사적 시뮬레이션이나 RiskMetrics 등을 포함한 본 논문이 고려하고 있는 여러 모형 중에서 가장 우수한 VaR 예측성과를 나타났다. 한편, CAViaR 모형들의 경우 일부 모형이 1% VaR에서 우수한 예측성과를 보였지만 EVT-GARCH 모형의 성과에는 미치지 못하였다.
Abstract
In this paper, we compare various forecasting models for VaR(value at risk), which is the main measure of market risk, by applying them to Korea Treasury Bonds (1-year, 5-year, and 10-year maturity discount bonds). In particular, we focus on the predictive performance of the extreme value theory(EVT) models and the conditional autoregressive value at risk(CAViaR) models. Our empirical analysis shows that the EVT-GARCH model, which uses the EVT after filtering bond returns through the GARCH(generalized autoregressive conditional heteroskedasticity) model, performs well in the 1% and 5% VaR forecasts. On the other hand, some CAViaR models show good forecasting performance at 1% VaR, but not sufficient to outperform the EVT-GARCH model.
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