论文标题
阈值不对称条件自回归范围（TACARR）模型
Threshold Asymmetric Conditional Autoregressive Range (TACARR) Model
论文作者
论文摘要
本文介绍了阈值不对称的条件自回归范围（TACARR）公式，以建模金融资产的每日价格范围。假定在每个时间点切换两个政权之间，将有条件的预期范围的过程标记为向上市场和下降市场状态。误差过程的干扰项也可以根据策略在两个分布之间进行切换。假定由时间序列的过去值驱动的一个自调节阈值组件决定了当前的市场制度。提出的模型能够捕获金融市场波动率的不对称和异性行为等方面。提出的模型是试图解决在现有价格范围模型中发现的几种潜在缺陷，例如条件自回归范围（CARR），不对称CARR（ACARR），反馈ACARR（FACARR）和阈值自动回应范围（TARR）型号。使用最大似然（ML）方法估算模型的参数。一项模拟研究表明，ML方法在估计塔卡尔模型参数方面表现良好。使用IBM索引数据研究了塔卡尔模型的经验性能，结果表明，该模型是样本中预测和样本外挥发性预测的良好替代方法。 关键词：波动率建模，不对称波动率，CARR模型，制度切换。
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.
