Impact of Oil Price Uncertainty on Selected Sectoral Returns in the Pakistani Stock Market: A Quantile-on-Quantile Approach

Purpose-This study investigates the impact of Oil Price Uncertainty (OPU) on the return of five energy-dependent sectors in the Pakistani market (Automobile and Assemblers, Cement, Oil and Gas, Power and Distribution, and Chemicals). More specifically, we examine how these selected sectors respond under OPU under different market conditions (bearish, normal, and bullish market conditions). Study Design/methodology/approach-The study employs a Quantile-on-Quantile Regression (QQR) approach to assess the non-linear and quantile-dependent association between sectoral returns and OPU. Monthly data is used to perform the examination, ranging from February 2010 to July 2024. Before performing QQR estimation, we check for the applicability of this approach by testing the normality, linearity, and stationarity of the selected series. Findings - Our study confirms a non-linear, sector-dependent, and quantile-dependent relationship between returns and OPU. Where the Power and Distribution and Chemicals sectors’ returns show a consistent negative association with OPU, the results of other sectors display more complexity. The Cement sector displays a positive relationship with OPU in bearish market conditions but a negative association in normal market conditions. Similarly, the Automobile and Assembly Oil and Gas sectors show a negative association with OPU in bearish and normal market conditions, which turns into a positive association in bullish market conditions. Research Practical Implications-The current study has two significant practical implications. First, our findings advocate adopting a sector-specific risk response strategy for investors when considering OPU. Second, our findings recommend government intervention to stabilize the Power and Distribution sector of Pakistan under high OPU, as this sector demonstrates low resilience and adaptability under OPU. Originality/value- Although literature documents the direct impact of OPU on commodities, tokens, and other macroeconomic uncertainties, the transmission of OPU through sectoral linkages remains underexamined, especially for frontier and less efficient markets like Pakistan. Additionally, examining this association from a non-linear perspective is also missing in the literature. Our study addresses these identified gaps.

Authors

Institutions

Publication Details

Journal
Journal of Innovative Research in Management Sciences
Published
2024-09-30
DOI
https://doi.org/10.62270/jirms.v5i3.85
Primary Topic
Market Dynamics and Volatility
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Impact of Oil Price Uncertainty on Selected Sectoral Returns in the Pakistani Stock Market: A Quantile-on-Quantile Approach

Ozair Siddiqui, Naveed Khan, Muhammed Zaly Shah
Journal of Innovative Research in Management Sciences
Market Dynamics and Volatility
article

Impact of Oil Price Uncertainty on Selected Sectoral Returns in the Pakistani Stock Market: A Quantile-on-Quantile Approach

Ozair Siddiqui, Naveed Khan, Muhammed Zaly Shah
article en

Abstract

Purpose-This study investigates the impact of Oil Price Uncertainty (OPU) on the return of five energy-dependent sectors in the Pakistani market (Automobile and Assemblers, Cement, Oil and Gas, Power and Distribution, and Chemicals). More specifically, we examine how these selected sectors respond under OPU under different market conditions (bearish, normal, and bullish market conditions). Study Design/methodology/approach-The study employs a Quantile-on-Quantile Regression (QQR) approach to assess the non-linear and quantile-dependent association between sectoral returns and OPU. Monthly data is used to perform the examination, ranging from February 2010 to July 2024. Before performing QQR estimation, we check for the applicability of this approach by testing the normality, linearity, and stationarity of the selected series. Findings - Our study confirms a non-linear, sector-dependent, and quantile-dependent relationship between returns and OPU. Where the Power and Distribution and Chemicals sectors’ returns show a consistent negative association with OPU, the results of other sectors display more complexity. The Cement sector displays a positive relationship with OPU in bearish market conditions but a negative association in normal market conditions. Similarly, the Automobile and Assembly Oil and Gas sectors show a negative association with OPU in bearish and normal market conditions, which turns into a positive association in bullish market conditions. Research Practical Implications-The current study has two significant practical implications. First, our findings advocate adopting a sector-specific risk response strategy for investors when considering OPU. Second, our findings recommend government intervention to stabilize the Power and Distribution sector of Pakistan under high OPU, as this sector demonstrates low resilience and adaptability under OPU. Originality/value- Although literature documents the direct impact of OPU on commodities, tokens, and other macroeconomic uncertainties, the transmission of OPU through sectoral linkages remains underexamined, especially for frontier and less efficient markets like Pakistan. Additionally, examining this association from a non-linear perspective is also missing in the literature. Our study addresses these identified gaps.

Journal of Innovative Research in Management SciencesVol. 5(3)
International Islamic University, Islamabad (PK), Association of Chartered Certified Accountants (GB)
Openalex Percentile: Top 67%
Market Dynamics and Volatility
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.