Gauging volatility through alternative data
Konstantinos Vafeidis, Associate (London)
![Post feature](/rails/active_storage/representations/proxy/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaHBBdTVLIiwiZXhwIjpudWxsLCJwdXIiOiJibG9iX2lkIn19--578fa221badee9af90a9c6ba7871c50948c498b7/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaDdCem9MWm05eWJXRjBTU0lJY0c1bkJqb0dSVlE2QzNKbGMybDZaVWtpRFRFeU9EQjRPRFV6QmpzR1ZBPT0iLCJleHAiOm51bGwsInB1ciI6InZhcmlhdGlvbiJ9fQ==--4a0ea5eeb6f0e6d47bff3b653fd69e214040023f/Intel.png)
The turbulence that 2022 cast upon global markets re-ignited volatility trading strategies. While many considered deploying alternative data to support their use cases, a best practice guide was difficult to find. Touching upon academic findings and industry standards, this report generates ideas and suggests alternative data solutions for distinct volatility trading use cases.