OneGARCH

OneGARCH is a variant of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, designed to capture the volatility clustering phenomenon in financial time series data. Unlike traditional GARCH models, which assume that the conditional variance of the time series is a function of past variances and squared residuals, OneGARCH introduces a single parameter that controls the persistence of volatility. This parameter, often denoted as omega, determines the weight given to past variances in the calculation of the current conditional variance.

The primary advantage of OneGARCH is its simplicity and parsimony. By reducing the number of parameters, OneGARCH can provide a more straightforward interpretation of the data's volatility dynamics. This model is particularly useful in situations where the data is limited or where the estimation of multiple parameters is computationally intensive.

OneGARCH has been applied in various financial contexts, including stock market analysis, foreign exchange rate modeling, and risk management. Its ability to capture the essential features of volatility clustering with fewer parameters makes it a valuable tool for practitioners and researchers alike. However, like all statistical models, OneGARCH has its limitations. It may not capture the full complexity of volatility dynamics in some financial time series, and its performance can be sensitive to the choice of the omega parameter.

In summary, OneGARCH is a simplified yet effective variant of the GARCH model, offering a balance between model complexity and interpretability. Its applications in financial analysis highlight its potential as a practical tool for understanding and managing financial volatility.