论文标题
NOWCASIC网络
Nowcasting Networks
论文作者
论文摘要
我们设计了一种基于神经网络的压缩/完成方法，用于财务现象。后者的意思是在曲线或表面的财务时间序列序列序列（至少在理论上，也适用于更高的维度）。特别是，我们引入了一种原始架构，适合处理可变网格节点定义的数据（到目前为止，财务现象应用程序中最常见的情况，因此不适用PCA或经典自动编码器方法）。关于真实数据集的三个案例研究可以说明这一点。首先，我们介绍了回购曲线数据的方法（随着日历时间的流逝，到期时间的成熟时间）。其次，我们表明我们的方法在股权导数表面数据集上优于基本插值基准（再次具有移动的到期时间）。我们还获得了令人满意的表面检测和表面完成的令人满意的性能。第三，我们在持续到期/男高音网格节点时重新定义的在货币交换表面上对PCA进行基准测试。然后，我们的方法表现为（即使显然不是比）PCA的性能，但是，该方法不适用于在移动的时间到达的网格上定义的原始原始数据）。
We devise a neural network based compression/completion methodology for financial nowcasting. The latter is meant in a broad sense encompassing completion of gridded values, interpolation, or outlier detection, in the context of financial time series of curves or surfaces (also applicable in higher dimensions, at least in theory). In particular, we introduce an original architecture amenable to the treatment of data defined at variable grid nodes (by far the most common situation in financial nowcasting applications, so that PCA or classical autoencoder methods are not applicable). This is illustrated by three case studies on real data sets. First, we introduce our approach on repo curves data (with moving time-to-maturity as calendar time passes). Second, we show that our approach outperforms elementary interpolation benchmarks on an equity derivative surfaces data set (with moving time-to-maturity again). We also obtain a satisfying performance for outlier detection and surface completion. Third, we benchmark our approach against PCA on at-the-money swaption surfaces redefined at constant expiry/tenor grid nodes. Our approach is then shown to perform as well as (even if not obviously better than) the PCA which, however, is not be applicable to the native, raw data defined on a moving time-to-expiry grid).
