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Modeling interest rate swap volatility with GARCH processes

Michał Balcerek, Michał Wronka · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2609.25965 · cited by 0 works (OpenAlex) · open-access PDF

Korean

우리는 GARCH(1,1), GJR-GARCH(1,1) 및 2 모드 마르코프 스위치 GARCH(MSGARCH) 모델을 사용하여 USD 1Yx10Y forward swap rate의 조건적 변동성 역학을 검사합니다. 이 분석은 2007년부터 2023년까지의 일일 데이터를 사용하여 시장에 영향을 미치는 측정 (ATM swaption volatility 및 SRVIX in-dex)와 함께 광범위한 진단 테스트를 사용합니다. 표준 GARCH 및 GJR-GARCH 모델은 안정적인 짧은 실행 매개 변수를 보여 주지만, 롤링 윈도우에 걸쳐 캡처 ω는 눈에 띄는 변동을 일으키며 암시된 긴 실행 변동 매개 변동에 불안정을 유발

Machine translation (Meta M2M100); the English original follows.

Abstract

We examine the conditional volatility dynamics of the USD 1Yx10Y forward swap rate using GARCH(1,1), GJR-GARCH(1,1), and a two-regime Markov-switching GARCH (MSGARCH) model. The analysis uses daily data from 2007 to 2023 and incorporates market-implied measures (ATM swaption volatility and the SRVIX in- dex) together with a broad set of diagnostic tests. Standard GARCH and GJR- GARCH models show stable short-run parameters, but the intercept ω varies markedly across rolling windows, causing instability in the implied long-run vari- ance. This pattern, confirmed by the Nyblom test, motivates adopting a regime- switching specification. MSGARCH mitigates this issue by keeping regime-specific parameters stable and capturing time variation through filtered regime probabili- ties. It delivers the highest log-likelihood and lowest AIC, whereas BIC favours the more parsimonious GJR-GARCH. One-step-ahead backtesting indicates comparable short-horizon accuracy across models, but MSGARCH offers a clearer structural in- terpretation by isolating high- and low-volatility regimes aligned with major market events.

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Topics: Econometrics, Volatility (finance), Autoregressive conditional heteroskedasticity, Interest rate swap, Swap (finance), Economics

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