论文标题
贝叶斯长期长期记忆模型，以实现风险和预期短缺的关节预测
A Bayesian Long Short-Term Memory Model for Value at Risk and Expected Shortfall Joint Forecasting
论文作者
论文摘要
价值风险（VAR）和预期的短缺（ES）在金融领域广泛使用，以衡量市场风险并管理极端市场的转变。分位数得分函数与非对称拉普拉斯密度之间的最新连接导致了基于弹性的VAR和ES联合建模的框架。能够捕获这两个数量的基本联合动态，这对财务应用具有很高的兴趣。我们通过开发基于不对称拉普拉斯准类模型的混合模型来解决这个问题，并采用了长期的短期记忆（LSTM）时间序列序列建模技术，从机器学习来有效地捕获VAR和ES的基本动力学。我们将此模型称为LSTM-AL。我们在LSTM-AL模型中采用自适应马尔可夫链蒙特卡洛（MCMC）算法。经验结果表明，所提出的LSTM-AL模型可以在一系列良好的竞争模型上提高VAR和ES预测精度。
Value-at-Risk (VaR) and Expected Shortfall (ES) are widely used in the financial sector to measure the market risk and manage the extreme market movement. The recent link between the quantile score function and the Asymmetric Laplace density has led to a flexible likelihood-based framework for joint modelling of VaR and ES. It is of high interest in financial applications to be able to capture the underlying joint dynamics of these two quantities. We address this problem by developing a hybrid model that is based on the Asymmetric Laplace quasi-likelihood and employs the Long Short-Term Memory (LSTM) time series modelling technique from Machine Learning to capture efficiently the underlying dynamics of VaR and ES. We refer to this model as LSTM-AL. We adopt the adaptive Markov chain Monte Carlo (MCMC) algorithm for Bayesian inference in the LSTM-AL model. Empirical results show that the proposed LSTM-AL model can improve the VaR and ES forecasting accuracy over a range of well-established competing models.
