We propose a novel class of Bounded GARCH (B-GARCH) processes designed to characterise the conditional heteroskedasticity of continuous time series constrained within a compact interval. While standard GARCH specifications are tailored for R-valued data, many empirical series, such as realised correlation, unemployment rates, expenditure shares, and relative humidity, are bounded. Fitting a traditional GARCH model to such data creates a severe support mismatch, frequently generating predictions and density forecasts that breach the natural boundaries of the data (boundary violation). Furthermore, conventional approaches utilising non-linear link transformations onto the real line might induce interpretability issues and estimation biases. To address these limitations, our proposed B-GARCH framework ensures a proper predictive distribution and an equation for the dynamic conditional variance with a parameter constraint that strictly preserves the bounded support of the entire process almost surely. We establish the fundamental probabilistic properties of the process, proving strict stationarity and ergodicity, and address parameter estimation via conditional maximum likelihood. The asymptotic consistency and normality of the estimators are rigorously derived, and their finite-sample performance is validated through Monte Carlo simulations. An empirical application to the monthly realised correlation between the Vanguard Real Estate ETF (VNQ) and the iShares 20+ Year Treasury Bond ETF (TLT) demonstrates that B-GARCH models effectively capture volatility clustering while completely eliminating boundary violation issue that plagues traditional GARCH models.
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