Modeling the Volatility of the S&P BVL Mining Index Using Support Vector Machines and a Linear GARCH Model
DOI:
https://doi.org/10.36881/ri.v8i1.882Keywords:
Volatility, Forecasting, GARCHAbstract
This work addresses the critical challenge of predicting volatility in the financial market, specifically focused on the S&P BVL Mining Index of the Peruvian mining sector. The difficulty lies in the complex and dynamic nature of volatility, which presents significant challenges for investors and risk managers in making informed and strategic decisions. The study proposes to evaluate the effectiveness of a hybrid model combining Support Vector Regression with Generalized Autoregressive Conditional Heteroskedasticity (SVR-GARCH) that incorporates a linear kernel, against conventional GARCH approaches. Methodologically, the study employs a quantitative design, gathering and processing daily historical data through the Yahoo! Finance API using Python programming tools, covering a period from January 31, 2014, to February 12, 2024. The Augmented Dickey-Fuller (ADF) unit root test is implemented to determine the stationarity of the time series. The findings indicate that the proposed SVR-GARCH-Linear model not only provides more accurate predictions compared to standard models but also proves to be robust against market fluctuations and the specific sensitivities of the mining index. We conclude that the hybrid approach represents a significant improvement in prediction and risk management tools, with practical applications that could extend to other indices and financial markets, demonstrating the need to incorporate machine learning techniques in contemporary financial modeling.
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