Igor Martins, Hedibert Freitas Lopes
arXiv 25 Nov 2024 · Finance — Statistical Finance · publishedThe Quarterly Review of Economics and Finance (2025) · 1 citations (OpenAlex)
arXiv:2411.16244 · PDF · DOI · OpenAlex · Extracted main text
This paper expands on stochastic volatility models by proposing a data-driven method to select the macroeconomic events most likely to impact volatility. The paper identifies and quantifies the effects of macroeconomic events across multiple countries on exchange rate volatility using high-frequency currency returns, while accounting for persistent stochastic volatility effects and seasonal components capturing time-of-day patterns. Given the hundreds of macroeconomic announcements and their lags, we rely on sparsity-based methods to select relevant events for the model. We contribute to the exchange rate literature in four ways: First, we identify the macroeconomic events that drive currency volatility, estimate their effects and connect them to macroeconomic fundamentals. Second, we find a link between intraday seasonality, trading volume, and the opening hours of major markets across the globe. We provide a simple labor-based explanation for this observed pattern. Third, we show that including macroeconomic events and seasonal components is crucial for forecasting exchange rate volatility. Fourth, our proposed model yields the lowest volatility and highest Sharpe ratio in portfolio allocations when compared to standard SV and GARCH models.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | J. R. Stroud and M. S. Johannes (2014) Bayesian modeling and forecasting of 24-hour high-frequency volatility | 1.000 | 5 | 4 | 100% |
| 2 | L. Bauwens, W. B. Omrane, and P. Giot (2005) News announcements, market activity and volatility in the euro/dollar foreign exchange market | 0.737 | 3 | 2 | 100% |
| 3 | J. Y. Campbell and R. H. Clarida (1987) The dollar and real interest rates | 0.737 | 3 | 2 | 100% |
| 4 | T. Ito and Y. Hashimoto (2006) Intraday seasonality in activities of the foreign exchange markets: Evidence from the electronic broking system | 0.644 | 4 | 1 | 100% |
| 5 | J. Geweke (1996) Variable selection and model comparison in regression | 0.644 | 3 | 2 | 67% |
| 6 | C. A. Abanto-Valle, H. S. Migon, and H. F. Lopes (2010) Bayesian modeling of financial returns: A relationship between volatility and trading volume self | 0.644 | 2 | 2 | 100% |
| 7 | V. Bhansali (2007) Volatility and the carry trade | 0.644 | 2 | 2 | 100% |
| 8 | H. Lustig, N. Roussanov, and A. Verdelhan (2011) Common risk factors in currency markets | 0.644 | 2 | 2 | 100% |
| 9 | L. Ederington and J. H. Lee (2001) Intraday volatility in interest-rate and foreign-exchange markets: Arch, announcement, and seasonality effects | 0.585 | 3 | 1 | 100% |
| 10 | S. Kim, N. Shephard, and S. Chib (1998) Stochastic volatility: likelihood inference and comparison with arch models | 0.511 | 2 | 2 | 50% |
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