Title: Stochastic modelling of ambient air quality and pricing of air pollution derivatives
Authors: Anders Sleire - University of Bergen (Norway) [presenting]
Abstract: Poor air quality in densely populated urban areas is a significant health concern, affecting millions of people globally. In recent years, there has also been increased focus on the impact of air pollution on business and industry. Episodes with extreme pollution levels occur in large metropolitan areas, changing the day-to-day activities of the population. Businesses may suffer financial losses through altered consumer behaviour, or directly from government imposed restrictions aiming to reduce emissions. We introduce air pollution derivatives as instruments for hedging against financial pollution risk. Building upon weather derivatives theory, we design contracts whose payoff depend on publicly available air quality data. The degree of pollution is typically assessed by measuring concentration of key pollutants, such as particulate matter, ground-level ozone, carbon monoxide, sulfur dioxide, lead, and nitrogen dioxide. Results can be communicated to the public on a standardized scale, such as the widely-adopted Air Quality Index (AQI). We develop stochastic models able to capture the seasonality, time-varying volatility, and jumps present in reported particulate matter AQI for a group of major Asian cities. The models are used to price options with AQI-based indexes as settlement references. Some practical use cases are also presented and discussed.