Threshold Asymmetric Conditional Autoregressive Range (TACARR) Model

TL;DR

This paper proposes the TACARR model, which captures asymmetric and regime-switching behavior in financial asset price ranges, improving volatility modeling and forecasting accuracy over existing models.

Contribution

It introduces a novel regime-switching threshold model for price ranges, addressing limitations of previous CARR-based models in capturing asymmetry and heteroscedasticity.

Findings

Model effectively captures asymmetric volatility behavior.

Empirical results show improved in-sample prediction.

Out-of-sample forecasts outperform existing models.

Abstract

This paper introduces a Threshold Asymmetric Conditional Autoregressive Range (TACARR) formulation for modeling the daily price ranges of financial assets. It is assumed that the process generating the conditional expected ranges at each time point switches between two regimes, labeled as upward market and downward market states. The disturbance term of the error process is also allowed to switch between two distributions depending on the regime. It is assumed that a self-adjusting threshold component that is driven by the past values of the time series determines the current market regime. The proposed model is able to capture aspects such as asymmetric and heteroscedastic behavior of volatility in financial markets. The proposed model is an attempt at addressing several potential deficits found in existing price range models such as the Conditional Autoregressive Range (CARR), Asymmetric CARR (ACARR), Feedback ACARR (FACARR) and Threshold Autoregressive Range (TARR) models. Parameters of the model are estimated using the Maximum Likelihood (ML) method. A simulation study shows that the ML method performs well in estimating the TACARR model parameters. The empirical performance of the TACARR model was investigated using IBM index data and results show that the proposed model is a good alternative for in-sample prediction and out-of-sample forecasting of volatility. Key Words: Volatility Modeling, Asymmetric Volatility, CARR Models, Regime Switching.