  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).
