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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

Japanese

GARCH(1.1)、GJR-GARCH(1.1)、およびマルコフ・スイッチング・GARCH(MSGARCH)の2つのモデルを使用して、USD 1Yx10Y前進スワップ率の条件変動の動態を検討します。この分析では、2007年から2023年にかけての日々のデータを使用し、市場に影響を与えた測定値(ATMのスワップションの変動値とSVRIXのインデックス)と幅広い診断テストを組み合わせています。標準GARCHおよびGJR-GARCHモデルは安定した短期パラメータを示していますが、中断 ωはローリングウィンドウを通じて顕著に変化し、暗示された長期変動アンセにおける不安定性を引き起こします。この

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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