Conditional Value-At-Risk Modelling Using Hybrid LASSO-QRNN to Quantify the Market Risk Dependence on Oil and Gas Companies’ Stock in Indonesia

Annisa Syalsabila; Dedy Dwi Prastyo; Muhammad Sjahid Akbar; Santi Puteri Rahayu; Nagarajan Deivanayagampillai

doi:10.1007/978-3-031-80338-3_22

Conditional Value-At-Risk Modelling Using Hybrid LASSO-QRNN to Quantify the Market Risk Dependence on Oil and Gas Companies’ Stock in Indonesia

Annisa Syalsabila
, Dedy Dwi Prastyo^*
, Muhammad Sjahid Akbar
, Santi Puteri Rahayu
, Nagarajan Deivanayagampillai

^*Corresponding author for this work

Department of Statistics
Institut Teknologi Sepuluh Nopember
Rajalakshmi Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Citations (Scopus)

Abstract

The oil and gas sector is pivotal to the global economy, influencing financial investment decisions due to its substantial impact on economic and geopolitical stability. Consequently, analyzing the risk dependence of oil and gas company stocks is crucial for investors and decision-makers to comprehend the interconnected risks within this sector. This study employed a hybrid LASSO-QRNN approach to develop a conditional Value-at-Risk (CoVaR) model, aiming to capture the risk dependence among oil and gas companies listed on the Indonesian stock exchange (IDX). The findings indicate that systemic risk escalated during the COVID-19 pandemic.

Original language	English
Title of host publication	Advances in Manufacturing Processes and Smart Manufacturing Systems - Smart Materials and Manufacturing Systems and Sustainable Management Operations
Editors	Denni Kurniawan, Fethma M. Nor
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	227-240
Number of pages	14
ISBN (Print)	9783031803376
DOIs	https://doi.org/10.1007/978-3-031-80338-3_22
Publication status	Published - 2024
Event	17th Global Congress on Manufacturing and Management, GCMM 2023 - Kuching, Malaysia Duration: 4 Dec 2023 → 7 Dec 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1215 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	17th Global Congress on Manufacturing and Management, GCMM 2023
Country/Territory	Malaysia
City	Kuching
Period	4/12/23 → 7/12/23

Keywords

CoVaR
LASSO
Oil and Gas Companies
QRNN
risk dependence

Access to Document

10.1007/978-3-031-80338-3_22

Cite this

Syalsabila, A., Prastyo, D. D., Akbar, M. S., Rahayu, S. P., & Deivanayagampillai, N. (2024). Conditional Value-At-Risk Modelling Using Hybrid LASSO-QRNN to Quantify the Market Risk Dependence on Oil and Gas Companies’ Stock in Indonesia. In D. Kurniawan, & F. M. Nor (Eds.), Advances in Manufacturing Processes and Smart Manufacturing Systems - Smart Materials and Manufacturing Systems and Sustainable Management Operations (pp. 227-240). (Lecture Notes in Networks and Systems; Vol. 1215 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-80338-3_22

Syalsabila, Annisa ; Prastyo, Dedy Dwi ; Akbar, Muhammad Sjahid et al. / Conditional Value-At-Risk Modelling Using Hybrid LASSO-QRNN to Quantify the Market Risk Dependence on Oil and Gas Companies’ Stock in Indonesia. Advances in Manufacturing Processes and Smart Manufacturing Systems - Smart Materials and Manufacturing Systems and Sustainable Management Operations. editor / Denni Kurniawan ; Fethma M. Nor. Springer Science and Business Media Deutschland GmbH, 2024. pp. 227-240 (Lecture Notes in Networks and Systems).

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abstract = "The oil and gas sector is pivotal to the global economy, influencing financial investment decisions due to its substantial impact on economic and geopolitical stability. Consequently, analyzing the risk dependence of oil and gas company stocks is crucial for investors and decision-makers to comprehend the interconnected risks within this sector. This study employed a hybrid LASSO-QRNN approach to develop a conditional Value-at-Risk (CoVaR) model, aiming to capture the risk dependence among oil and gas companies listed on the Indonesian stock exchange (IDX). The findings indicate that systemic risk escalated during the COVID-19 pandemic.",

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Syalsabila, A, Prastyo, DD , Akbar, MS , Rahayu, SP & Deivanayagampillai, N 2024, Conditional Value-At-Risk Modelling Using Hybrid LASSO-QRNN to Quantify the Market Risk Dependence on Oil and Gas Companies’ Stock in Indonesia. in D Kurniawan & FM Nor (eds), Advances in Manufacturing Processes and Smart Manufacturing Systems - Smart Materials and Manufacturing Systems and Sustainable Management Operations. Lecture Notes in Networks and Systems, vol. 1215 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 227-240, 17th Global Congress on Manufacturing and Management, GCMM 2023, Kuching, Malaysia, 4/12/23. https://doi.org/10.1007/978-3-031-80338-3_22

Conditional Value-At-Risk Modelling Using Hybrid LASSO-QRNN to Quantify the Market Risk Dependence on Oil and Gas Companies’ Stock in Indonesia. / Syalsabila, Annisa; Prastyo, Dedy Dwi ; Akbar, Muhammad Sjahid et al.
Advances in Manufacturing Processes and Smart Manufacturing Systems - Smart Materials and Manufacturing Systems and Sustainable Management Operations. ed. / Denni Kurniawan; Fethma M. Nor. Springer Science and Business Media Deutschland GmbH, 2024. p. 227-240 (Lecture Notes in Networks and Systems; Vol. 1215 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Syalsabila, Annisa

AU - Prastyo, Dedy Dwi

AU - Akbar, Muhammad Sjahid

AU - Rahayu, Santi Puteri

AU - Deivanayagampillai, Nagarajan

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024

Y1 - 2024

N2 - The oil and gas sector is pivotal to the global economy, influencing financial investment decisions due to its substantial impact on economic and geopolitical stability. Consequently, analyzing the risk dependence of oil and gas company stocks is crucial for investors and decision-makers to comprehend the interconnected risks within this sector. This study employed a hybrid LASSO-QRNN approach to develop a conditional Value-at-Risk (CoVaR) model, aiming to capture the risk dependence among oil and gas companies listed on the Indonesian stock exchange (IDX). The findings indicate that systemic risk escalated during the COVID-19 pandemic.

AB - The oil and gas sector is pivotal to the global economy, influencing financial investment decisions due to its substantial impact on economic and geopolitical stability. Consequently, analyzing the risk dependence of oil and gas company stocks is crucial for investors and decision-makers to comprehend the interconnected risks within this sector. This study employed a hybrid LASSO-QRNN approach to develop a conditional Value-at-Risk (CoVaR) model, aiming to capture the risk dependence among oil and gas companies listed on the Indonesian stock exchange (IDX). The findings indicate that systemic risk escalated during the COVID-19 pandemic.

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Syalsabila A, Prastyo DD , Akbar MS , Rahayu SP, Deivanayagampillai N. Conditional Value-At-Risk Modelling Using Hybrid LASSO-QRNN to Quantify the Market Risk Dependence on Oil and Gas Companies’ Stock in Indonesia. In Kurniawan D, Nor FM, editors, Advances in Manufacturing Processes and Smart Manufacturing Systems - Smart Materials and Manufacturing Systems and Sustainable Management Operations. Springer Science and Business Media Deutschland GmbH. 2024. p. 227-240. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-80338-3_22

Conditional Value-At-Risk Modelling Using Hybrid LASSO-QRNN to Quantify the Market Risk Dependence on Oil and Gas Companies’ Stock in Indonesia

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Keywords

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