Nowcasting Networks

TL;DR

This paper introduces a neural network architecture tailored for financial nowcasting tasks, capable of handling data on variable grids, outperforming simple benchmarks and matching PCA in certain scenarios, with applications to curves and surfaces.

Contribution

The paper presents a novel neural network architecture designed specifically for variable grid data in financial nowcasting, addressing limitations of PCA and classical autoencoders.

Findings

Outperforms elementary interpolation benchmarks on equity derivative surfaces.

Effective for outlier detection and surface completion.

Performs comparably to PCA on swaption surfaces, even on raw data.

Abstract

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).