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All Journal Emerging Science Journal
Guayjarernpanishk, Pannarat
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Environmental Science

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Effective Forecasting of Insurer Capital Requirements: ARMA-GARCH, ARMA-GARCH-EVT, and DCC-GARCH Approaches Chaiyawat, Thitivadee; Guayjarernpanishk, Pannarat
Emerging Science Journal Vol 8, No 6 (2024): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-06-03

Abstract

This research paper presents a comprehensive analysis of three prominent volatility and dependence models for financial time series: ARMA-GARCH, GARCH-EVT, and DCC-GARCH. These models are employed to assess and forecast capital requirements for life and non-life insurer investments. This study evaluates the models' performance in forecasting Value-at-Risk, using daily data on key Thai financial indicators (representing permissible insurer investment assets) from March 2009 to March 2024. Specifically, 1-day and 10-day VaR forecasts are generated using the ARMA-GARCH and DCC-GARCH models, while the ARMA-GARCH-EVT model is employed for 1-day VaR forecasting. Our findings indicate that the ARMA-GARCH model effectively captures time-varying volatility, while the GARCH-EVT approach enhances tail risk estimation, particularly relevant for stress testing. Additionally, the DCC-GARCH model allows for the examination of dynamic conditional correlations between assets, providing insights into portfolio diversification benefits. Rigorous backtesting procedures, employing Kupiec and Christoffersen tests with a rolling window of 1,000 out-of-sample observations, confirm that the majority of models accurately forecast VaR at their respective horizons, with only a very small subset of 10-day VaR models exhibiting limitations. These results highlight that ARMA-GARCH, ARMA-GARCH-EVT, and DCC-GARCH models offer insurers robust tools for estimating minimum capital requirements, forecasting investment risk, and guiding strategic asset allocation decisions. This research underscores the effectiveness of these models for practical application in the insurance industry while also emphasizing the importance of continued model validation, particularly for extended forecasting horizons. Doi: 10.28991/ESJ-2024-08-06-03 Full Text: PDF
Air Pollution Forecasting in a Regional Context for Sustainable Management Guayjarernpanishk, Pannarat; Chutiman, Nipaporn; Piwpuan, Narumol; Kong-ied, Butsakorn; Chiangpradit, Monchaya
Emerging Science Journal Vol 8, No 5 (2024): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-05-024

Abstract

The aim of this research was to develop and apply a statistical model that can be used to forecast long-term daily maximum particulate matter with a diameter of less than 2.5 microns (PM2.5) concentrations. In order to predict the daily maximum PM2.5 concentrations in the northeastern region of Thailand, the extreme value theory was analyzed, and an appropriate distribution model was identified by employing the Generalized Pareto distribution (GPD). The data of daily maximum PM2.5 concentrations during the years 2021–2023 obtained from six stations was used. These stations are located in Khon Kaen, Loei, Nakhon Ratchasima, Nong Khai, Nakhon Phanom, and Ubon Ratchathani provinces. The results of this study reveal that the GPD is appropriate based on the results of Kolmogorov-Smirnov Statistics Test. Estimating the return levels during the following return periods: 2 years, 5 years, 10 years, 25 years, 50 years, and 100 years showed that the area in the upper northeastern region, particularly Loei and Nakhon Phanom, has daily maximum PM2.5 concentrations above 500 micrograms per cubic meter. These results can also be used as information to support decision-making when conducting response planning in high-risk areas, which can be helpful for efficient resource planning and prevention actions. Doi: 10.28991/ESJ-2024-08-05-024 Full Text: PDF
Extreme Value Model to Forecast PM2.5 Concentration Through a Non-Stationary Process Guayjarernpanishk, Pannarat; Remsungnen, Tawun; Chutiman, Nipaporn; Chiangpradit, Monchaya; Kong-ied, Butsakorn
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-025

Abstract

The objectives of this research were to develop a model to forecast and estimate the return levels for daily maximum PM2.5 concentrations in Thailand, applying Extreme Value Theory (EVT) with the Generalized Extreme Value (GEV) distribution under eight models for stationary and non-stationary process. This research utilized reanalysis data from the NASA EARTHDATA satellite, represented as grid points with a spatial resolution of 50 × 62.5 km, enabling the analysis of daily maximum PM2.5 concentrations across 176 grid points from January 1, 2009 to October 31, 2024. The analysis revealed that Model 2 (μ(t)=β0+β1t where σ and ξ are constants) is the most suitable model for five grid points, namely Sa Kaeo Province, Uthai Thani Province, Nakhon Ratchasima Province, Bueng Kan Province and Mae Hong Son Province, whereas Model 1 (μ, σ and ξ are constants) is suitable for the remaining 171 grid points. Estimating the return levels for return periods of 5, 10, 25, and 50 years showed that Northern Thailand had the most extreme daily PM2.5 concentrations, for all return periods especially Mae Hong Son Province. The results of this analysis can serve as valuable information to support decision-making for response planning in high-risk areas, aiding in efficient resource allocation and preventive measures.
Co-Authors Chaiyawat, Thitivadee Chiangpradit, Monchaya Chutiman, Nipaporn Kong-ied, Butsakorn Piwpuan, Narumol Remsungnen, Tawun