AI 中文总结
该研究用计量经济学与可解释机器学习综合方法，分析2013年6月至2026年6月布伦特原油价格与尼泊尔证券交易所数据，检验两者动态关系，揭示了格兰杰因果关系、波动特性等，XGBoost性能最佳，两种方法互补提供了市场关系见解。
AI 中文摘要
本研究采用一种将传统计量经济学技术与机器学习和可解释人工智能技术相结合的综合方法，来检验全球油价与尼泊尔证券交易所（NEPSE）之间的动态关系。为此，分析了约13年（2013年6月至2026年6月）的国际油价和NEPSE指数的每日数据，使用格兰杰因果关系、EGARCH(1,1)和DCC-GARCH模型来检验不同特性。还使用随机森林、LightGBM和XGBoost等机器学习模型捕捉非线性关系，并借助SHAP值等可解释人工智能技术进一步解释模型结果。计量分析结果显示布伦特原油到NEPSE存在统计上显著的单向格兰杰因果关系且有四天滞后等。机器学习模型中XGBoost性能最佳，可解释性分析表明NEPSE自身动量和短期波动主要影响其行为，与石油相关信息作用较小。研究结果表明计量经济学和可解释机器学习方法能为石油与股票市场关系提供见解，且相互补充。
英文摘要
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.
