An extended CAViaR model for early-warning of exceedances of the air pollution standards. The case of PM10 in the city of Madrid

Atmospheric Pollution Research

article

Lidia Sanchis-Marco¹  José-María Montero²  Gema Fernández-Avilés² 

[1] Department Economic Analysis and Finance, University of Castilla-La Mancha;Department of Statistics, University of Castilla-La Mancha

关键词: Air pollution control;    PM10;    Meteorological conditions;    Value-at-Risk;    CAViaR;    Non-linear quantile regression;   

DOI  :  10.1016/j.apr.2022.101355

学科分类：农业科学（综合）

来源: Dokuz Eylul Universitesi * Department of Environmental Engineering

【 摘 要 】

The last decade has witnessed significant advances in air pollution control . However, the European Union standard for PM10 is still exceeded in large cities. Most of the literature on anticipating PM10 exceedances of the standard is based on PM10 forecasting. However, it fails when it comes to forecasting extreme values. This is the reason why we focus on a Value-at-Risk-based approach imported from finance: The Conditional Autoregressive Value-at-Risk (CAViaR) modelling, which we extend with a meteorological indicator. CAViaR has a core advantage with respect to traditional VaR-based methods used in finance: it is a direct distribution-free approach, naturally linked to the concept of VaR, thus overcoming the limitations of the typical indirect approaches used in financial literature. This is the first article using a CAViaR-based strategy in air pollution control. This methodology has been applied in the city of Madrid to forecast the risk of short-term PM10 exceedances of the standard. Backtesting reveals that the extended CAViaR outperforms the standard CAViaR model and other typical specifications in the financial literature on the topic, including the EWMA model proposed by RiskMetrics, the Gaussian GARCH(1,1) specification, and a hybrid strategy combining GARCH-type forecasting with the block-maxima approach of the Extreme Value Theory. Specifically, the extended CAViaR exhibits the highest p-values in the back-tests used. Our proposal provides citizens and authorities with a set of PM10 VaR-values, together with their respective probabilities which is key information to decide whether or not to take any remedial measure and, if so, what measures to take.

【 授权许可】

CC BY