Air pollution option pricing model based on AQI

Xue, Jian; Xu, Yuanyuan; Zhao, Laijun; Wang, Chenchen; Rasool, Zeeshan; Ni, Ming-ming; Wang, Qin; Li, Deqiang

doi:10.1016/j.apr.2018.10.011

Cited by 26 publications

(13 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Haze is mainly defined using the corresponding AQI, which includes pollutants such as SO 2 , NO 2 , PM 2.5 , PM 10 , CO and O 3 , with higher values indicating more polluted air (Dai et al, 2013;Jian et al, 2018). In fact, the haze showed obvious seasonal variation, and the most serious haze weather is in the season of low moisture content (Zhang et al, 2016).…”

Section: Aqi and Pm 25 Concentrationmentioning

confidence: 99%

A method for predicting short‐time changes in fine particulate matter (PM_2.5) mass concentration based on the global navigation satellite system zenith tropospheric delay

Guo

Zhang

Xia

2019

Meteorological Applications

View full text Add to dashboard Cite

In this study, a method of haze prediction has been developed based on zenith tropospheric delay (ZTD). The relationship between ZTD and fine particulate matter (PM2.5, the main component of haze) during hazy periods in Beijing from 2015 to 2018 was analysed. The correlation between ZTD and the PM2.5 series was analysed based on data from three hazy periods with relatively stable weather conditions, no heavy rainfall and relatively continuous data, and the correlation coefficient between ZTD and the PM2.5 series was maintained in the range [0.5207, 0.7883]. The analysis showed a strong correlation between them based on data from three hazy periods with relatively stable weather conditions. To enhance the correlation between ZTD and the PM2.5 sequence, the standard orthogonal wavelet Daubechies (db5) method was used in this research. By removing the influence of the high frequency signal using the method, the ZTD and PM2.5 sequences were reconstructed using the fourth‐layer low frequency coefficients, their correlation coefficient was maintained in the range [0.5540, 0.9067] and the percentage range of the correlation coefficient was increased to [2.46, 16.69]. Finally, the reconstructed PM2.5, ZTD, relative humidity, average wind speed and NO2 series through db5 were used to establish a multiple regression model to predict the change in the PM2.5 mass concentration. The experimental results show that the multivariate regression model after wavelet analysis is superior to the traditional multivariate regression method in predicting the short‐term change of PM2.5 through four statistics values (R2, F, P, S2) of the regression model and that it is effective and feasible for predicting short‐term haze.

show abstract

Section: Aqi and Pm 25 Concentrationmentioning

confidence: 99%

A method for predicting short‐time changes in fine particulate matter (PM_2.5) mass concentration based on the global navigation satellite system zenith tropospheric delay

Guo

Zhang

Xia

2019

Meteorological Applications

View full text Add to dashboard Cite

show abstract

“…Li et al [36] expanded the O-U model by using an AR-GARCH model to optimize the fitting accuracy for the prediction of average daily temperatures in Shanghai, China. However, in subsequent research on using the O-U model to predict air pollutants and air quality, researchers sometimes incorrectly assumed that the randomly perturbed variable followed a normal distribution [37,38]. In addition, these authors did not test for autocorrelation and conditional heteroscedasticity; both problems must be tested for and, if present, must be solved to preserve the accuracy of predictions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, the PM 2.5 concentration cannot fully reflect the severity of air pollution, so its application may be limited. Xue et al [38] designed an air pollution options contract based on a multi-pollutant air quality index (AQI), further expanding the available indexes suitable for use in derivatives. The design of an underlying index based on AQI is more comprehensive than a single-pollutant index, but it may not describe the actual situation sufficiently well, and must therefore be improved.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The construction of the underlying index is an important step in the design of derivatives. Xue et al [38] did not differentiate among the different levels of pollution when they designed their average deviation index (ADI) for air pollution options, resulting in an unrealistic index design. For example, the average deviation index can be low when AQI is high, and this is clearly inconsistent with the actual situation.…”

Section: Index Construction For Aqi Optionsmentioning

confidence: 99%

See 1 more Smart Citation

An Actuarial Pricing Method for Air Quality Index Options

Liu

Zhao

Wang

et al. 2019

IJERPH

Self Cite

View full text Add to dashboard Cite

Poor air quality has a negative impact on social life and economic production activities. Using financial derivatives to hedge risks is one of the important methods. Air quality index (AQI) options are designed to help enterprises cope with the operational risk caused by air pollution. First, the expanded Ornstein–Uhlenbeck model is established using an autoregressive-generalized autoregressive conditional heteroscedasticity (AR-GARCH) method to predict AQI for a city. Next, the average AQI is constructed to be as the underlying index for the AQI options. We then priced AQI options using an actuarial method with an Esscher transform. Meanwhile payoff functions for the options are established to let enterprises hedge against the operational risk caused by air pollution. Finally, we determined the price of AQI options using data from Xi’an, China, and the example of a tourism enterprise as a case study of how AQI options can be applied to hedge against operational risk for enterprises. With AQI options trading, enterprises can hedge against operational risks caused by air pollution. The applicability of AQI options is wide, it can also be applied in other cities or regions.

show abstract

“…Methods of constructing the underlying index about the atmospheric environment are usually constructed based on AQI or the concentrations of specific air pollutants such as fine particulate matter (PM 2.5 and PM 10 ). For example, [14] designed an air pollution options based on air quality index. [15] constructed an average deviation index based on AQI to describe a city's air quality during a given period.…”

Section: Introductionmentioning

confidence: 99%

An Options Pricing Method Based on the Atmospheric Environmental Health Index

Zhao

Wang

Yang

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Background: Environmental finance has gradually become an important tool for solving atmospheric pollution problems. The creation of options based on an atmospheric environmental health index (AEHI) is designed to support a rational pricing of financial products related to the health of the atmospheric environment.Method: First, the improved Ornstein-Uhlenbeck model is established to predict the change in air pollutant concentrations, and then the AEHI is constructed using exposure-response Poisson regression model. Then, an options pricing method for AEHI is proposed based on Esscher transform theory. Finally, the investment strategy for enterprises is formulated to hedge operational risk by using the AEHI option.Results: AEHI options prices were determined for two diseases in each of three age groups using Beijing, China, as a case study, and AEHI option is applied to recompense for human health losses and hedge operating risk for an insurance enterprise.Conclusion: The AEHI options should therefore be used to compensate for the damage to human health caused by air pollution, and to hedge against the operational risks of related industries such as insurance.

show abstract

Air pollution option pricing model based on AQI

Cited by 26 publications

References 10 publications

A method for predicting short‐time changes in fine particulate matter (PM_2.5) mass concentration based on the global navigation satellite system zenith tropospheric delay

A method for predicting short‐time changes in fine particulate matter (PM_2.5) mass concentration based on the global navigation satellite system zenith tropospheric delay

An Actuarial Pricing Method for Air Quality Index Options

An Options Pricing Method Based on the Atmospheric Environmental Health Index

Contact Info

Product

Resources

About

Air pollution option pricing model based on AQI

Cited by 26 publications

References 10 publications

A method for predicting short‐time changes in fine particulate matter (PM2.5) mass concentration based on the global navigation satellite system zenith tropospheric delay

A method for predicting short‐time changes in fine particulate matter (PM2.5) mass concentration based on the global navigation satellite system zenith tropospheric delay

An Actuarial Pricing Method for Air Quality Index Options

An Options Pricing Method Based on the Atmospheric Environmental Health Index

Contact Info

Product

Resources

About

A method for predicting short‐time changes in fine particulate matter (PM_2.5) mass concentration based on the global navigation satellite system zenith tropospheric delay

A method for predicting short‐time changes in fine particulate matter (PM_2.5) mass concentration based on the global navigation satellite system zenith tropospheric delay