Cryptocurrencies like Bitcoin, Ethereum, and other digital assets have gained significant popularity in recent years. These digital currencies are known for their high volatility, meaning their prices can change frequently and substantially within short periods. Various factors can contribute to this volatility, such as market speculation, regulatory changes, technological developments, and global economic events. To manage and predict this volatility, different statistical and econometric models have been developed. One such model is the SETAR (Self-Exciting Threshold AutoRegressive) model, which is particularly useful for analyzing time series with nonlinear dynamics and regime-dependent behavior, such as cryptocurrency prices. The goal of this task is to apply SETAR models and evaluate their effectiveness in analyzing highly volatile cryptocurrency prices.

Cryptocurrencies like Bitcoin, Ethereum, and other digital assets have gained significant popularity in recent years. These digital currencies are known for their high volatility, meaning their prices can change frequently and substantially within short periods. Various factors can contribute to this volatility, such as market speculation, regulatory changes, technological developments, and global economic events. To manage and predict this volatility, different statistical and econometric models have been developed. One such model is the SETAR (Self-Exciting Threshold AutoRegressive) model, which is particularly useful for analyzing time series with nonlinear dynamics and regime-dependent behavior, such as cryptocurrency prices. The goal of this task is to apply SETAR models and evaluate their effectiveness in analyzing highly volatile cryptocurrency prices.