Governments invest heavily in seasonal climate forecasting, yet the practical value of these forecasts remains unclear. While agencies like NOAA and the European Centre for Medium-Range Weather Forecasts prioritize improving seasonal outlooks, evidence of their real-world impact is limited. Water resource managers, for example, have shown reluctance to rely on these forecasts. This study addresses this knowledge gap by directly assessing the value of seasonal forecasts to financial market participants. The existing literature has employed various methods to value forecasts, including model-based estimations of informational value, attributing unexplained market volatility to weather risk, and direct user surveys. However, these methods have limitations. This research uses a more direct approach by observing how financial markets, specifically options markets, respond to the release of NOAA seasonal climate outlooks. Options markets, with their inherent sensitivity to uncertainty, provide an ideal setting for assessing how traders value and incorporate this information. By analyzing the changes in implied volatility surrounding the release of these forecasts, the study aims to quantify the market's perception of their value and inform resource allocation for improving forecasting accuracy.

Prior research on the economic value of weather and climate forecasts has used various approaches. Some studies have used model-based analyses, focusing on specific decision problems and computing the value of information within a particular model. Others have attempted to attribute all unexplained volatility in financial markets to weather risk, or have directly surveyed assumed users of the forecasts. Existing work has also demonstrated the influence of short-term weather forecasts and multi-year climate model forecasts on financial markets. However, the question of whether traders value the less precise and less immediately relevant seasonal forecasts remained open. This study addresses this gap by focusing specifically on the impact of seasonal climate outlooks, bridging the gap between short-term and long-term forecasting horizons.

The study employs an event study design to analyze the impact of NOAA seasonal outlooks (Winter, El Niño Southern Oscillation (ENSO), and Hurricane) on implied volatility in options markets from 2010-2019. The researchers use data from OptionMetrics and Compustat, including implied volatility calculations, stock prices, and industry classifications. They focus on out-of-the-money call options that are nearest-to-the-money, using the shortest maturity options to maximize the signal-to-noise ratio. The core analysis involves event study regressions of the log-change in implied volatility on a dummy variable indicating the release of a seasonal outlook. Controls are included to account for factors like interest rates, earnings announcements, and time to expiration. The study examines the average effect across all firms, as well as disaggregated effects by industry group and sector. The key variable of interest is the coefficient on the outlook release dummy, which indicates the change in implied volatility upon the outlook’s release. A negative coefficient indicates that traders priced uncertainty about the contents of the outlooks. To calculate the aggregate value of these outlooks, the study calculates the total market capitalization of firms significantly affected by the outlooks and estimates the reduction in risk exposure from a one-standard-deviation shock. Finally, the total option market premium is measured to represent the money spent hedging against the risk of the outlook's contents. The analysis also examines the differences between NOAA and non-NOAA outlooks (Farmers' Almanac, Colorado State University) to evaluate the importance of forecast skill. The effect of improved forecast skill is assessed by comparing the May and June ENSO outlooks which, due to a well-known ‘spring barrier’ effect, show a significant increase in skill.

The results reveal that the release of NOAA's June ENSO and Winter Outlooks significantly reduces implied volatility, indicating that traders incorporate these outlooks into their pricing of uncertainty. The negative effects are not driven by a few outlier firms, but rather are observed broadly across numerous industry groups. At a 10% significance level, the June ENSO and Winter Outlooks affect firms with a combined market capitalization of $19.1 trillion (67% of the sample). The June ENSO Outlook shows a stronger effect than the Winter Outlook. The NOAA Hurricane Outlook, in contrast, shows no significant effect, which might be due to its lower skill relative to the other outlooks. Interestingly, the study reveals that almost all sectors experience a negative change in implied volatility, including those not seemingly directly exposed to seasonal climate effects. This points to potential systemic effects through supply chains and other indirect channels. The analysis of risk reduction shows that the June ENSO Outlook reduces market capitalization exposed to a one standard deviation risk by $180 billion, and the Winter Outlook reduces it by $82 billion. The study also calculates the option market premium annually for the June ENSO outlook at $12 million, signifying that traders are willing to pay this premium to hedge the risk associated with the outlook's content. By comparing the May and June ENSO Outlooks, a 1% increase in forecast skill reduces firms' exposure to a one standard deviation shock by $18 billion, and traders invest an additional $1.8 million annually in hedging this risk. The analysis across years demonstrates a clear trend of increasing market responsiveness to these outlooks since approximately 2010, possibly related to improvements in forecasting accuracy, standardization, and process improvements.

The findings demonstrate that financial markets value skillful seasonal climate forecasts and incorporate this information into their pricing of risk. The significant impact of the ENSO and Winter Outlooks highlights the importance of long-term climate patterns to a wide range of economic sectors, extending beyond those intuitively exposed to weather. The lack of a significant effect from the Hurricane Outlook suggests that improvements in accuracy and specificity are needed for this outlook to be fully valued by the market. The analysis also emphasizes the limitations of adaptation; the fact that traders are still hedging despite the existence of forecasts means firms cannot costlessly adapt to predictable seasonal climate risks. This has significant implications for assessing the potential costs of long-run climate change, suggesting that costless adaptation may not fully mitigate these risks. This study makes a strong case for continued investment in improving seasonal climate forecasts, especially the Hurricane Outlook and potentially others with value in other regions or time horizons.

This study provides compelling evidence that financial markets value skillful seasonal climate forecasts, particularly for ENSO and winter weather. The findings underscore the importance of considering long-term climate patterns in economic analysis and highlight the need for improved forecasting skill, particularly in areas like hurricane prediction. The observed market responsiveness suggests substantial potential benefits from investing in advanced forecasting systems, while the limitations of adaptation caution against overly optimistic assumptions about the future costs of climate change. Future research might consider the value of such forecasts in other economic sectors and regions, as well as the potential for market-based mechanisms to improve forecast accuracy.

The study's value calculations are lower bounds, as they do not capture non-market benefits, exclude firms without liquidly traded options, and ignore losses avoided by adaptation using forecast information. The analysis implicitly assumes that financial markets effectively aggregate all available information; however, some information may be held privately or only imperfectly reflected in market prices. The reliance on US financial markets limits the generalizability of the findings. The study primarily focused on NOAA outlooks; the observed effects might be strengthened by including other forecasts available to market participants.