  In this paper, we present an artificial neural network framework for
portfolio compression of a large portfolio of European options with varying
maturities (target portfolio) by a significantly smaller portfolio of European
options with shorter or same maturity (compressed portfolio), which also
represents a self-replicating static hedge portfolio of the target portfolio.
For the proposed machine learning architecture, which is consummately
interpretable by choice of design, we also define the algorithm to learn model
parameters by providing a parameter initialisation technique and leveraging the
optimisation methodology proposed in Lokeshwar and Jain (2024), which was
initially introduced to price Bermudan options. We demonstrate the convergence
of errors and the iterative evolution of neural network parameters over the
course of optimization process, using selected target portfolio samples for
illustration. We demonstrate through numerical examples that the Exposure
distributions and Exposure profiles (Expected Exposure and Potential Future
Exposure) of the target portfolio and compressed portfolio align closely across
future risk horizons under risk-neutral and real-world scenarios. Additionally,
we benchmark the target portfolio's Financial Greeks (Delta, Gamma, and Vega)
against the compressed portfolio at future time horizons across different
market scenarios generated by Monte-Carlo simulations. Finally, we compare the
regulatory capital requirement under the standardised approach for counterparty
credit risk of the target portfolio against the compressed portfolio and
highlight that the capital requirement for the compact portfolio substantially
reduces.
