Modeling the Dynamic Relationship Between Brent Crude Oil Prices and the Nepal Stock Exchange: An Integrated Econometric and Explainable Machine Learning Approach
布伦特原油与尼泊尔股市的动态关系：计量与可解释机器学习AI 解读 ↓
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布伦特原油与尼泊尔股市的动态关系：计量与可解释机器学习
- 研究问题
- 布伦特原油价格与尼泊尔股市之间是否存在预测关系、波动不对称和时变相关性，机器学习能否补充对非线性关系的描述？
- 方法与思路
- 作者使用约十三年的日度数据，结合 Granger 检验、EGARCH 和 DCC-GARCH，并比较树模型及 SHAP、部分依赖和个体条件期望解释。
- 材料陈述的贡献
- 摘要称，原油对股市存在四日滞后的单向 Granger 关系，两市场波动持续且相关性较弱、随时间变化；机器学习中自身动量更重要。
- 阅读关注点
- 应核对价格与收益变量的处理、滞后选择和模型诊断；机器学习部分应检查时间顺序划分、信息泄漏、预测目标及样本外比较标准。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；Granger 关系不等同于结构因果效应，摘要未说明预测目标及划分，无法核验性能优势。
原文摘要
This study examines the dynamic relationship between the global oil prices and Nepal Stock Exchange (NEPSE) using an integrated approach which combines traditional econometric techniques with machine learning and explainable AI techniques. For this, Daily data of International Oil prices and NEPSE index is analyzed from approximately thirteen years (June 2013 to June 2026) using Granger causality, EGARCH(1,1), and DCC-GARCH models to examine different properties like predictive relationships, asymmetric volatility behaviour, and time-varying correlations. To further supplement the econometric analysis, Machine Learning Models like Random Forest, LightGBM, and XGBoost algorithms were used to capture nonlinear relationships, along with explainable artificial intelligence techniques like SHAP values, Partial Dependence Plots, and Individual Conditional Expectation plots to further interpret the results of the model. The results from the econometric analysis showed a statistically significant unidirectional Granger causality from Brent crude oil to NEPSE with a four-day lag, high volatility persistence in both markets, and weak yet highly time-varying conditional correlations. Among the machine learning models, XGBoost achieves the best performance, and explainability analysis reveals that NEPSE own momentum and short-term volatility mainly influence its own behaviour and oil-related information serves as a minor, method-dependent contributor. The findings demonstrate that econometric and explainable machine learning approaches provide insights into the oil and equity market relationship in a way that each approach complements the result of one another.