Threshold Asymmetric Conditional Autoregressive Range (TACARR) Model
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AI 根据所提供的论文材料归类：摘要明确以金融资产日价格极差建模和预测非对称波动率，直接对应金融波动率方向。
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阈值非对称条件自回归极差模型
- 研究问题
- 如何利用金融资产日价格极差刻画波动率的非对称性与异方差，并允许条件期望极差及扰动分布随市场状态切换？
- 方法与思路
- 作者提出 TACARR，由历史序列驱动的自调整阈值确定上涨或下跌状态，允许状态间扰动分布不同，并采用最大似然估计。
- 材料陈述的贡献
- 摘要称，模拟中最大似然估计表现良好，IBM 数据应用支持其作为波动率样本内预测和样本外预测的替代模型。
- 阅读关注点
- 应核对阈值更新规则、状态定义、扰动分布与稳定性条件，以及 IBM 数据口径、预测目标和与既有极差模型的比较设置。
- 阅读边界
- 所给版本为 arXiv 预印本，同行评审及期刊接收未经核实；摘要未提供预测误差、比较检验或参数条件，不能判断普遍预测优势。
原文摘要
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.