Newest papers

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

Chinese

我们使用GARCH(1,1),GJR-GARCH(1,1),以及两种模式的Markov交换GARCH(MSGARCH)模型来研究美元1Yx10Y前期交换率的条件波动力学。该分析使用了从2007年到2023年的每日数据,并结合了市场影响的测量(ATM交换波动性和SVRIXin-Dex)以及广泛的诊断测试。标准GARCH和GJR-GARCH模型显示了稳定的短跑参数,但拦截 ω在滚动窗口之间显著变化,导致暗示的长跑变动关系的不稳定性。这种模式,由Nyblom测试证实,通过保持MSGARCH和GJR-GARCH规格参数的稳定性,缓解了这一问题,通过指示

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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