Anamol Khadka, Milan Arjel, Ayush Lataula, Aayam Dhakal, Prajun Trital, Mingmar Sherpa, Biman Rimal
arXiv 9 Jul 2026 · Econometrics
arXiv:2607.11922 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Lo, A. W., & MacKinlay, A. C (1990) An econometric analysis of nonsynchronous trading | 0.644 | 2 | 2 | 100% |
| 2 | U.S. Energy Information Administration (2026) Crude oil prices: Brent – Europe | 0.511 | 2 | 1 | 100% |
| 3 | Khana, P (2026) Dataset 2 –- NEPSE Index Daily Closing Prices | 0.511 | 2 | 1 | 100% |
| 4 | Ben Salem, L., Zayati, M., Nouira, R., & Rault, C (2024) Volatility spillover between oil prices and main exchange rates: Evidence from a DCC-GARCH-connectedness approach | 0.405 | 1 | 1 | 100% |
| 5 | Breiman, L (2001) Random forests | 0.405 | 1 | 1 | 100% |
| 6 | Campbell, J. Y., & Thompson, S. B (2008) Predicting excess stock returns out of sample: Can anything beat the historical average? | 0.405 | 1 | 1 | 100% |
| 7 | Chen, T., & Guestrin, C (2016) XGBoost: A scalable tree boosting system | 0.405 | 1 | 1 | 100% |
| 8 | Engle, R. F (2002) Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models | 0.405 | 1 | 1 | 100% |
| 9 | Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., &… (2017) LightGBM: A highly efficient gradient boosting decision tree | 0.405 | 1 | 1 | 100% |
| 10 | Khalid, T. A., Alexandri, M. B., Sumadinata, W. S., & Yunus, M (2025) Volatility spillover between stock returns and oil prices in ASEAN: A post-pandemic reassessment using EGARCH | 0.405 | 1 | 1 | 100% |
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