## Impact of Household Energy Efficiency on Crude Oil Price Volatility: An Asymmetric Quantile-on-Quantile Regression Analysis### IntroductionThe interplay between energy consumption patterns and global oil market dynamics has gained significant attention amidst concerns over energy security and environmental sustainability. Crude oil prices are known for their volatility, often fluctuating due to various global events and economic factors. Extensive studies have examined the determinants of crude oil price volatility [1, 2]. However, the relationship between household energy efficiency and crude oil price volatility remains an emerging area in international oil market dynamics.This study aims to investigate the impact of household energy efficiency in the United States (referred to as "United States" hereafter) on crude oil price volatility. By analyzing historical and forecast data, this research provides valuable insights for developing energy policies that promote price stability in the oil market and reduce the nation's vulnerability to external shocks.### Literature Review and Hypothesis DevelopmentPrevious studies [2, 7, 8, 9] have explored the factors influencing crude oil price volatility, encompassing supply and demand shocks, commodity attributes, macroeconomics, geopolitics, alternative energy, and energy efficiency. However, the link between crude oil volatility and household energy use has received limited attention in the literature.Building on previous research, this study formulates the following hypotheses to guide the investigation:1. **H1(a):** Improvement in household energy efficiency is associated with a decline in crude oil price volatility.2. **H1(b):** The effect of household energy efficiency is asymmetric across the conditional quantiles of crude oil price volatility and household energy efficiency.3. **H1(c):** Improvement in energy-related CO2 emissions (a proxy for environmental impact) is associated with a decline in crude oil price volatility.4. **H1(d):** The effect of energy-related CO2 emissions is asymmetric across the conditional quantiles of crude oil price volatility and energy-related CO2 emissions.5. **H1(e):** Increase in retail electricity prices (a proxy for substitute energy prices) is associated with a decline in crude oil price volatility.6. **H1(f):** The effect of retail electricity prices is asymmetric across the conditional quantiles of crude oil price volatility and retail electricity prices.### Nonparametric Multivariate-QQR ModelThe empirical analysis employs the Multivariate-Quantile-on-Quantile Regression (MQQR) model [4], which extends the standard QQR model to the multivariate case. This model captures the asymmetric effects of multiple explanatory variables on the dependent variable, mitigating omitted variable bias and providing a more comprehensive analysis.The MQQR model specifies the relationship between crude oil price volatility (LNCOPV) and household energy efficiency (LNHEE), energy-related CO2 emissions (LNCO2), and retail electricity prices (LNREP) as:```LNCOPV = α0 + α1 * LNHEE + α2 * LNCO2 + α3 * LNREP + ε```where ε represents the error term and α0, α1, α2, and α3 are quantile-dependent coefficients. By estimating these coefficients across different quantiles, the study uncovers the varying effects of the explanatory variables on LNCOPV at different levels of volatility.### Data Description and Empirical ResultsThe study utilizes quarterly data covering the period 1970Q1 - 2040Q4, with the latter part based on forecast projections. The variables are expressed in their natural logarithmic forms. Unit root and non-linearity tests confirm the stationarity and asymmetry in the data, supporting the use of quantile-based analysis.The Multivariate-QQR results reveal that LNHEE has a negative and asymmetric effect on LNCOPV. Specifically, the effect is stronger in quantiles before the median quantiles of LNCOPV and decreases with increasing quantiles. This implies that improving household energy efficiency dampens crude oil price volatility, particularly in periods of moderate to low volatility.In contrast, LNCO2 and LNREP positively affect LNCOPV. The effect of LNCO2 is more pronounced in the upper and lower tails of the distribution, suggesting that energy-related CO2 emissions exacerbate oil price volatility, particularly during extreme events. Similarly, LNREP positively influences LNCOPV, with the effect increasing in higher quantiles, indicating that substitute energy prices contribute to oil price volatility during periods of high demand.### Robustness ChecksSensitivity analysis using only historical data confirms the reliability of the forecast data and the robustness of the findings. The KRLS machine learning approach further supports the results, showing that LNHEE dampens LNCOPV, while LNCO2 and LNREP intensify LNCOPV. Marginal effect plots confirm the asymmetric nature of the relationships.### Policy ImplicationsThe findings have significant policy implications for achieving price stability in the oil market and promoting sustainable energy practices.1. **Improve Household Energy Efficiency:** Enhancing household energy efficiency reduces the demand for crude oil, contributing to lower oil price volatility. Investments in energy-efficient appliances and home insulation, energy-saving practices, and awareness campaigns should be prioritized.2. **Promote Environmental Sustainability:** Reducing energy-related CO2 emissions through investments in renewable energy sources and energy efficiency measures mitigates the upward pressure on crude oil prices.3. **Diversify Energy Portfolio:** Diversifying the energy mix by reducing reliance on fossil fuels and promoting alternative energy sources, such as solar, wind, and hydroelectricity, helps stabilize the demand and prices in the oil market.### ConclusionThis study unveils the asymmetric relationship between household energy efficiency, energy-related CO2 emissions, retail electricity prices, and crude oil price volatility in the United States. The findings highlight the importance of investing in household energy efficiency, promoting environmental sustainability, and diversifying the energy portfolio to dampen oil price volatility. By implementing these measures, policymakers can contribute to a more stable energy market and enhance energy security in the 21st century.### References[1] Kang, B., Nikitopoulos, C. S., & Prokopczuk, M. (2020). Economic Determinants of Oil Futures Volatility: A Term Structure Perspective. Energy Economics, 88, 104743.[2] Zhao, J. (2022). Exploring the Influence of the Main Factors on the Crude Oil Price Volatility: An Analysis Based on GARCH-MIDAS Model with Lasso Approach. Resources Policy, 79, 103031.[3] Zhang, Y., Hyder, M., Baloch, Z. A., Qian, C., & Saydaliev, H. B. (2022). Nexus between Oil Price Volatility and Inflation: Mediating Nexus from Exchange Rate.