论文标题
通过顺序学习的金融资产尾部动力学的简约分位数回归
Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning
论文作者
论文摘要
我们提出了一个简约的分位回归框架，以了解金融资产回报的动态尾巴行为。我们的模型可以很好地捕捉到时变特征和财务时间序列的不对称重尾属性。它结合了流行的顺序神经网络模型，即LSTM的优点，以及我们构建的新型参数分位数函数，以表示资产回报的条件分布。我们的模型还可以单独捕获更高矩的串行依赖，而不仅仅是波动。在各种资产类别中，样本外预测有条件的分位数或模型的VAR优于Garch家族。此外，与参数概率密度函数方法相比，所提出的方法不会遭受分位数交叉问题的困扰。
We propose a parsimonious quantile regression framework to learn the dynamic tail behaviors of financial asset returns. Our model captures well both the time-varying characteristic and the asymmetrical heavy-tail property of financial time series. It combines the merits of a popular sequential neural network model, i.e., LSTM, with a novel parametric quantile function that we construct to represent the conditional distribution of asset returns. Our model also captures individually the serial dependences of higher moments, rather than just the volatility. Across a wide range of asset classes, the out-of-sample forecasts of conditional quantiles or VaR of our model outperform the GARCH family. Further, the proposed approach does not suffer from the issue of quantile crossing, nor does it expose to the ill-posedness comparing to the parametric probability density function approach.
