Multi-Day Volatility Forecasts for Superior Day-Ahead Trading Predictions
What if longer-horizon volatility forecasts contain information that could enhance short-term trading decisions?
Traditional forecasting models in finance often assume that the forecast horizon should match the trading horizon. If you're trading daily, the logic goes, then use 1-day-ahead forecasts. However, this approach may be too narrow. What if longer-horizon volatility forecasts contain information that could enhance short-term trading decisions?
This article challenges the standard assumption. We examine whether multi-day-ahead implied volatility forecasts can improve next-day trading performance in VIX and S&P 500 futures. Using extended-horizon forecasts from HAR-type models, we analyze whether these longer-term views on volatility can guide tactical short-term trades more effectively than traditional day-ahead models.
The results show that multi-day signals aren't just redundant—they add value. Especially during high-volatility episodes, longer-horizon forecasts provide forward-looking context that enhances next-day trade timing, position sizing, and directional bets. In other words, even if you're trading tomorrow, you might want to think further ahead.
Research Question
We pose a direct, strategy-driven question:
Can implied volatility forecasts for 5, 10, or 21 days ahead improve your trading decisions for tomorrow?
This reframes the typical approach. Rather than evaluating a forecast’s accuracy relative to its own horizon, we assess its economic usefulness in a shorter-horizon setting. It's a shift from precision to profit: instead of asking, “How close is this forecast to realized volatility in 10 days?” we ask, “Does this 10-day signal help you make money tomorrow?”
By treating long-horizon forecasts as information-rich signals for short-term trading, we open up a new way to evaluate predictive models—not just on statistical performance, but on their contribution to decision-making in real-world markets.
Trading Value of Multi-Day Forecasts
We evaluate how forecasts over different horizons translate into actual trading performance. The core idea is simple: if implied volatility is expected to rise, you go long VIX futures or short S&P 500 futures; if it’s expected to fall, you do the opposite. But instead of relying solely on short-term (1-day) forecasts, we test whether longer-horizon forecasts—specifically 5-day, 10-day, and 21-day implied volatility predictions—offer more profitable trading signals.
The results reveal that models that incorporate longer-term implied volatility signals—such as 21-day components—deliver stronger trading performance. Among the models tested, the one including a 21-day forecast component achieves the highest cumulative profit and a Sharpe ratio of 1.31, compared to 0.95 for the model using only short-term (1-day) inputs. This suggests that incorporating long-horizon signals into the forecast process helps filter noise and capture persistent market dynamics that short-term views miss.
Why does this matter? Because short-horizon forecasts tend to be highly reactive, capturing local noise rather than structural trends. In contrast, longer-term forecasts embed a broader view of market expectations, often absorbing macroeconomic developments and sentiment shifts. These broader signals appear to support more informed, and ultimately more profitable, short-term trading decisions.
The findings reveal a compelling edge: traders who incorporate longer-term implied volatility signals into their forecasts can make better short-term trading decisions—especially in markets shaped by persistent shocks or evolving macro narratives.
Data
I use publicly available daily data for the VIX and S&P 500 indexes
The main sample spans January 10, 2012, to August 31, 2024, a decade that includes several volatility regimes—from the low-volatility post-2012 bull market, to the 2018 correction, and the extreme volatility of the 2020 pandemic crash. We also capturing a high-uncertainty environment shaped by Federal Reserve tightening cycles, inflation shocks, geopolitical instability, and banking sector stress.
Here’s a snapshot of VIX summary statistics over the full sample:
Mean: 18.57
Median: 16.82
Standard Deviation: 7.11
Min / Max: 9.14 / 82.69
Coefficient of Variation: 0.38
Observations: 3522
These numbers reflect the inherently skewed and heavy-tailed nature of VIX, which tends to spike during crises and remain suppressed during calm periods. This variability makes volatility forecasting particularly valuable for tactical trading strategies.
Left plot (Time Series): VIX spikes sharply during market crises (e.g., 2008 financial crisis, 2020 COVID crash), then stays low during calm periods. This highlights its asymmetric behavior—quiet most of the time, explosive when fear enters the market.
Right plot (Histogram): The distribution is heavily right-skewed. Most VIX values sit between 10 and 25, but the long tail extending beyond 40 confirms rare but extreme volatility events. This reinforces the idea that VIX behaves like a crisis barometer.
Applying a log scale compresses extreme spikes and reveals more structure in lower VIX levels. The log transformation reduces skewness and shows a more symmetric distribution. We observe the high frequency of moderate volatility levels while also preserved rarity of extreme events.
Forecasting Model
We apply a simplified version of the Heterogeneous Autoregressive model of Implied Volatility (HAR-IV), introduced by Corsi (2009) and adapted for implied volatility by Müller et al. The model captures the persistence and multi-scale structure of volatility by incorporating information across multiple time horizons. In this context, we forecast VIX for several horizons, including 1-day, 5-day, 10-day, and 21-day ahead, by utilizing the following components:
Daily component: The current VIX level, reflecting the most recent implied volatility.
Weekly component: The rolling average of implied volatility over the past 5 trading days.
Monthly component: The rolling average of implied volatility over the past 21 trading days.
The forecast equation is structured as:
Where IV ₜ₊ₕ represents the forecasted VIX for the h-day horizon. We train the model using a rolling window approach (21 days) to ensure adaptability and robustness over time. This method allows the model to account for varying volatility patterns, capturing daily fluctuations, weekly adjustments, and longer-term trends.
Trading Strategy
We translate the forecast signals into two directional trading strategies based on the expected movements in VIX and the S&P 500:
Strategy 1 — VIX Futures Trading
Long Position: If the forecasted implied volatility for the target horizon (e.g., 21 days ahead) exceeds the current VIX value, implying an increase in volatility.
Short Position: If the forecast indicates a decline in implied volatility, suggesting lower future volatility.
Strategy 2 — S&P 500 Futures Trading
Short Position: If volatility is expected to rise (anticipating downside risk for equity markets).
Long Position: If volatility is expected to decrease (anticipating upside potential for equity markets).
These strategies leverage the well-documented inverse relationship between volatility and equity prices, particularly during risk-off environments.
The strategy calculates daily returns for performance evaluation:
VIX Futures Return:
S&P 500 Futures Return:
Position direction is determined solely by the forecasted change in volatility (whether it is expected to rise or fall), without considering the magnitude of the forecasted change. This binary decision rule isolates the effect of directional forecast signals on trading performance, providing insight into whether the multi-horizon VIX forecasts improve trading outcomes, even for a short-term horizon like the next day.
Results
Our empirical results highlight a clear pattern: longer-horizon implied volatility forecasts outperform shorter ones across multiple metrics, including P&L. Among all forecast lengths tested, the 21-day-ahead forecast delivers the strongest trading performance, producing both the highest cumulative profit and the most favorable risk-adjusted returns. Specifically, the Sharpe ratio of the strategy using the 21-day forecast reaches 1.42, compared to 0.97 for the 1-day horizon.
Below is the table with strategy performance statistics:
This superior performance isn't limited to benign market conditions. During high-volatility regimes—such as in 2020’s COVID crash or 2022’s Fed tightening cycle—the long-horizon forecasts demonstrate even greater predictive value. These periods typically involve sustained shifts in market sentiment and structural changes in risk pricing. The HAR-IV model’s use of multi-timescale information helps capture such shifts, making the longer-term forecasts particularly effective when volatility is driven by persistent shocks rather than transient noise.
Shorter-horizon forecasts, by contrast, are more sensitive to market microstructure noise and less able to anticipate regime transitions. Their trading signals often lack persistence, leading to more frequent reversals and lower cumulative returns. This evidence supports the view that volatility is not just a short-term phenomenon—it evolves with structure and memory, and longer-horizon forecasts can exploit this.
Conclusion
It's common to assume that forecasts and trading horizons should align, but that may cause you to overlook useful opportunities. In this research, I challenges the assumption, showing that longer-term implied volatility forecasts can significantly improve your decision-making, even in the short term.
Why is this beneficial? Long-term forecasts do more than capture immediate volatility. They give you insights into broader shifts in risk, investor behavior, and macroeconomic trends. This makes them more reliable and stable for making informed decisions.
You don’t need complex models to gain an edge. Even a simple HAR-IV model, used correctly, can outperform reactive, short-sighted strategies. The key is understanding what the forecast tells you about the bigger picture of risk, not just its next movement.
Volatility forecasts go beyond noise and provide signals that reveal deeper insights into market movements. The valuable signal arise when long-term thinking enhances short-term decision-making.