PMWeather|$55.6k Vol|
time19 hrs 41 mins

Highest temperature in Lucknow on March 19? - AI Odds Analysis

All Outcomes
Market Price
AI Fair Value
Value Edge
34°C or below
YesNo
35°C
YesNo
36°C
YesNo
37°C
YesNo
38°C
YesNo
43°C
YesNo
41°C
YesNo
42°C
YesNo
39°C
YesNo
40°C
YesNo
44°C or higher
YesNo
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AI Insights:

03.17 19:24 Updated
Fair Value Reasoning:
Based on market data from March 17 and updated meteorological models, the temperature rebound trend is confirmed. The collapse of '34°C or below' from a high of 83c to under 40c validates the assessment that the 'western disturbance' was a temporary cooling event. Wunderground and major models now converge on a high of approx. 35°C (95°F) for the 19th, with some models touching 36°C. The market price for 35°C (32c) is still undervalued relative to its probability as the peak of the distribution, while '34°C or below' remains overvalued due to inertia.

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Exotics
While weather forecasting is a conventional topic, predicting the specific high temperature for a non-global hub like Lucknow, India, on a specific date is relatively niche and trivial. For most non-local traders, this requires specific research into regional climate data, making it distinct from mainstream prediction events.
Movers
March 16-17, 2026: '34°C or below' plummeted from 65c to 37.5c, while '35°C' climbed from 23c to 36c. Reason: As the 19th approaches, meteorological data confirmed that the current cool spell is ending rapidly, with temperatures set to rebound to the 35-36°C range as originally modeled. March 15, 2026: '34°C or below' spiked from 50c to 83.5c. Reason: Local rain and dust storms triggered panic selling of higher temperature options, as the market overreacted to the short-term cooling event.
Divergence
Although prices have begun to correct, the market still assigns a ~40% probability to '34°C or below', whereas mainstream weather forecasts (Google/Wunderground) have much higher confidence in 35°C or above. The market's lag reflects an 'anchoring effect' among traders regarding the recent cold snap, creating a divergence from model data.

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