Recent Academic Research
Volatility forecasting with Bitcoin, trading the VIX with volatility forecasts, and the impact of U.S. monetary policy uncertainty
Welcome back to another issue of Recent Academic Research! This week’s focus is on volatility forecasting.
Let’s get into it.
Volatility Forecasting with Bitcoin
Bitcoin overnight returns significantly enhance forecasts of equity market volatility.
Traditional sentiment measures rely heavily on equity market data, but Bitcoin’s 24-hour trading offers a unique, continuous gauge of investor mood, especially during U.S. market off-hours. The authors identify a clear overnight trading pattern: Bitcoin often rises during the night, only to reverse those gains during the following day's regular trading hours.
This pattern of overnight price increases and daytime reversals aligns closely with heightened retail investor sentiment and attention. By integrating these Bitcoin overnight returns into volatility prediction models, the authors substantially enhance forecasts of next-day VIX movements.
Moreover, strategies that leverage these improved volatility forecasts achieve superior risk-adjusted returns compared to traditional volatility strategies, highlighting the practical economic benefits of incorporating Bitcoin data into forecasting frameworks.
The predictive power remains robust across various tests, including during pandemic and non-pandemic periods and across multiple volatility indices globally.
These results underscore Bitcoin’s growing integration with traditional financial markets and its untapped potential as a real-time indicator of investor sentiment beyond conventional equity signals.
Gu, Ming and Lin, Juan, Beyond Conventional Sentiment Indicators: Bitcoin's Hidden Potential in VIX Forecasting (September 19, 2024). Available at SSRN: https://ssrn.com/abstract=5186527 or http://dx.doi.org/10.2139/ssrn.5186527
VIX Trading with Longer Term Forecasts
Forecasting and trading horizons don’t need to match, as 22-day-ahead volatility forecasts generate superior profits for next-day trades.
This study challenges the standard approach in volatility forecasting, which aligns forecast horizons with trading horizons. Instead, the authors evaluate whether s-day-ahead forecasts of implied volatility can enhance 1-day-ahead trading decisions. Using a series of HAR-based models, they test this framework on both VIX and S&P 500 futures.
The results are consistent and striking—the highest next-day profits occur when trading based on 22-day-ahead forecasts, not 1-day-ahead ones. This insight holds even after adjusting for risk and across a post-out-of-sample period that includes the COVID-19 shock.
The most effective model combines the standard Heterogeneous Autoregressive (HAR) framework with the 10-year U.S. Treasury Volatility Index (TYVIX). This captures macro-level uncertainty that spills into equity markets, helping traders make more informed volatility bets.
The findings invite a broader rethink of trading model design. Forecast horizons do not need to match trading timeframes, and in some cases, separating the two may unlock better performance.
Degiannakis, Stavros and Delis, Panagiotis and Filis, George and Giannopoulos, George, Trading VIX on Volatility Forecasts: Another Volatility Puzzle? (February 10, 2025). Bank of Greece Working Paper No. 336, Available at SSRN: https://ssrn.com/abstract=5220722 or http://dx.doi.org/10.2139/ssrn.5220722
Impact of U.S. Monetary Policy Uncertainty
When U.S. monetary policy uncertainty rises, countries with flexible exchange rates experience deeper recessions than those with fixed regimes.
Traditionally, flexible exchange rates are believed to cushion economies from foreign shocks. But this study finds the opposite in the case of U.S. monetary policy uncertainty. The authors use a structural shock to the U.S. Monetary Policy Uncertainty Index, which captures sudden spikes in uncertainty around future interest rate policy. They show that this shock triggers a stronger economic contraction in countries with flexible exchange rate regimes.
The chart shows how floating regimes (red lines) experience sharper and more persistent declines in consumption and investment. While exports remain statistically similar across regimes, imports fall more in flexible regimes, likely reflecting collapsing domestic demand.
The reason, the authors argue, lies in risk dynamics. In response to U.S. uncertainty, capital flows out of countries with floating exchange rates, causing their currencies to depreciate against the U.S. dollar. This depreciation increases exchange rate volatility and economic policy uncertainty, which discourages spending and investment. Sovereign credit risk also rises: CDS spreads widen significantly more in flexible regimes following the shock.
A small open economy New Keynesian model supports this finding. In simulations, floating regimes exposed to direct spillovers in U.S. interest rate volatility experience a stronger drop in output and investment than fixed regimes. Without this direct volatility transmission, the contraction is far milder.
The takeaway: when the Fed gets unpredictable, countries with flexible exchange rates may actually become more vulnerable, not less.
Kim, Soyoung and Jung, Yongseung and Yun, Yeonggyu, US Monetary Policy Uncertainty Spillover and the Role of Exchange Rate Regime. Available at SSRN: https://ssrn.com/abstract=5208971 or http://dx.doi.org/10.2139/ssrn.5208971
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