Michał Balcerek, Michał Wronka · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2609.25965 · cited by 0 works (OpenAlex) · open-access PDF
Wir untersuchen die bedingte Volatilitätsdynamik des USD 1Yx10Y forward swap Rates mithilfe von GARCH(1,1), GJR-GARCH(1,1), und einem zweistufigen Markov-switching GARCH (MSGARCH) Modell. Die Analyse verwendet tägliche Daten von 2007 bis 2023 und integriert marktbedingte Maßnahmen (ATM swaption volatility und den SRVIX in-dex) zusammen mit einer breiten Reihe von Diagnosetests. Standard GARCH und GJR-GARCH Modelle zeigen stabile Kurzlaufparameter, aber die Intercept ω variiert deutlich über Rollfenster, was zu Instabilität in der implizierten Langlaufvariabilität führt. Dieses Muster, bestätigt durch den Nyblom-Test, motiviert eine Reg
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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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