A new implementation of Network GARCH Model
网络 GARCH 模型的一种新实现AI 解读 ↓
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网络 GARCH 模型的一种新实现
- 研究问题
- 大量金融资产同时呈现波动聚集和跨资产溢出时，如何以较简约的网络模型刻画自身及多阶邻居对动态波动的影响？
- 方法与思路
- 作者把 GARCH 动态嵌入广义网络自回归框架，构建 GNGARCH，加入邻居持续性、动态协方差与高阶作用，并以损失最小化估计。
- 材料陈述的贡献
- 摘要称论文给出模型推导、向量化和稳态条件，并扩展阈值系数刻画杠杆效应；模拟及七十五只美国股票应用展示波动估计与预测。
- 阅读关注点
- 应核对虚拟网络如何构造、条件协方差的有效性及稳态限制，以及平方收益代理和 MSE、QLIKE 损失对估计与预测比较的影响。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未给出稳态条件细节、估计量性质或预测比较数值，不能确认普遍预测优势。
原文摘要
Volatility clustering and spillovers are key features of real-world financial time series when there are a lot of cross-sectional financial assets. While network analysis helps connect stocks that are 'similar' or 'correlated', which is effective to link volatility spillovers between stocks, contemporary multivariate ARCH-GARCH formulations struggle to represent structured network dependence and remain parsimonious. We introduce the Generalised Network GARCH (GNGARCH) model as a network volatility model that embeds the GARCH dynamics within the Generalised Network Autoregressive (GNAR) framework, to capture the dynamic volatility of financial asset return by both the asset itself and its 'neighbouring' assets from the constructed virtual network. The proposed volatility model GNGARCH also addresses the limitations for current studies of network GARCH by adapting neighbouring volatility persistence, dynamic conditional covariance updates, and allowing higher-order neighbouring effects rather than only immediate neighbours. This paper provides the model derivation, vectorisation and conversion, stationarity conditions, and also an extension by incorporating threshold coefficients to capture leverage effects. We show that the GNGARCH is a valid volatility model satisfying the stylised facts of financial return series through simulation. Parameter estimation is then performed by using squared returns as variance proxy and minimising a loss function that is either mean squared error (MSE) or quasi-likelihood (QLIKE). We apply our model on 75 of the most active US stocks under a virtual network, and highlight the model's ability in volatility estimation and forecast.