Tissaoui Kais, Boubaker Sahbi, Hkiri Besma, Azibi Nadia
Management Information Systems Department, Applied College, University of Ha'il, P.O. Box 2440, Hail City, Saudi Arabia.
Department of Computer and Network Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah, 21959, Saudi Arabia.
Sci Rep. 2024 Sep 30;14(1):22711. doi: 10.1038/s41598-024-74456-8.
This paper studies the forecasting power of uncertainty emanating from the commodities market, energy market, economic policy, and geopolitical threats to the CBOE Volatility Index (VIX). In this study, the relationship between the various uncertainty metrics throughout the period 2012-2022, using a multi-model transfer function technique optimized by particle swarm optimization (PSO) is estimated. Furthermore, we utilize PSO for parameter optimization within the multi-model framework, improving model performance and convergence speed. According to empirical findings, the CBOE Volatility Index reacts nonlinearly to the uncertainty indices. Specifically, the conclusions of the performance metrics show that the OVX index (MAPE: 4.1559%; RMSE: 1.0476% and W: 96.74%) outperforms the geopolitical risk index, the Bloomberg energy index, and the economic policy uncertainty index in predicting the volatility of the US equities market. Although individual models have generated respectful performance, results from the aggregate simulation show that when all predictors are combined, they simultaneously provide better performance indicators (MAVE: 2.7511 %; RMSE: 0.7361%; R2: 98.93%) than when they are estimated separately. In addition, results provide evidence that, when considering non-linear patterns in the data, the multi-model transfer function technique calibrated using PSO demonstrates its outperformance over autoregressive baseline models, traditional econometric models, and deep learning techniques. The effectiveness and accuracy of the multi-model transfer function method tuned by PSO as a forecasting tool are confirmed by the convergence analysis of the cost function. Our methodology innovates by employing a multi-model transfer function technique, which captures the complex and nonlinear relationships between uncertainty indicators and the VIX more comprehensively than traditional single-model approaches. These results are important for traders in terms of hedging as well as portfolio diversification by investing in defensive equities and for policymakers in terms of reliability and preciseness of volatility forecasts.
本文研究了商品市场、能源市场、经济政策以及地缘政治威胁所产生的不确定性对芝加哥期权交易所波动率指数（VIX）的预测能力。在本研究中，使用粒子群优化（PSO）优化的多模型传递函数技术，估计了2012年至2022年期间各种不确定性指标之间的关系。此外，我们在多模型框架内利用PSO进行参数优化，提高了模型性能和收敛速度。根据实证结果，芝加哥期权交易所波动率指数对不确定性指数有非线性反应。具体而言，性能指标的结论表明，OVX指数（平均绝对百分比误差：4.1559%；均方根误差：1.0476%；W统计量：96.74%）在预测美国股票市场波动率方面优于地缘政治风险指数、彭博能源指数和经济政策不确定性指数。尽管单个模型都取得了不错的表现，但综合模拟结果表明，当所有预测变量结合在一起时，它们同时提供的性能指标（平均绝对误差：2.7511%；均方根误差：0.7361%；R²：98.93%）比单独估计时更好。此外，结果表明，在考虑数据中的非线性模式时，使用PSO校准的多模型传递函数技术比自回归基线模型、传统计量经济学模型和深度学习技术表现更优。通过成本函数的收敛分析，证实了PSO调整的多模型传递函数方法作为预测工具的有效性和准确性。我们的方法通过采用多模型传递函数技术进行创新，该技术比传统的单模型方法更全面地捕捉了不确定性指标与VIX之间复杂的非线性关系。这些结果对于交易者进行套期保值以及通过投资防御性股票进行投资组合多元化具有重要意义，对于政策制定者而言，在波动率预测的可靠性和精确性方面也具有重要意义。
