Estimating value-at-risk on time series data with possibly heteroscedastic dynamics is a highly challenging task. Typically, we face a small data problem in combination with a high degree of non-linearity, causing difficulties for both classical and machine-learning estimation algorithms. In this paper, we propose a novel value-at-risk estimator using a long short-term memory (LSTM) neural network and compare its performance to benchmark GARCH estimators. Our results indicate that even for a relatively short time series, the LSTM could be used to refine or monitor risk estimation processes and correctly identify the underlying risk dynamics in a non-parametric fashion. We evaluate the estimator on both simulated and market data with a focus on heteroscedasticity, finding that LSTM exhibits a similar performance to GARCH estimators on simulated data, whereas on real market data it is more sensitive towards increasing or decreasing volatility and outperforms all existing estimators of value-at-risk in terms of exception rate and mean quantile score.
翻译:在时间序列数据中,可能具有混凝土动态性能的估算值风险时间序列数据是一项极具挑战性的任务。通常,我们面临一个小型的数据问题,同时存在高度非线性,给古典和机器学习估算算法造成困难。在本文中,我们提议使用长期短期内存(LSTM)神经网络,将其性能与基准的GARCH测算器进行比较,我们的结果显示,即使时间序列相对较短,LSTM也可以用来改进或监测风险估算过程,并正确地以非参数性的方式查明潜在的风险动态。我们评估模拟和市场数据的估测器,重点是测器性能,发现LSTM在模拟数据中与GACCH估测器显示类似性能,而在实际市场数据中,它更敏感地注意增加或减少波动性,并超越所有现有的定值风险的异常率和平均定量分数。