34 research outputs found
Systemic risk in a mean-field model of interbank lending with self-exciting shocks
In this paper we consider a mean-field model of interacting diffusions for
the monetary reserves in which the reserves are subjected to a self- and
cross-exciting shock. This is motivated by the financial acceleration and fire
sales observed in the market. We derive a mean-field limit using a weak
convergence analysis and find an explicit measure-valued process associated
with a large interbanking system. We define systemic risk indicators and
derive, using the limiting process, several law of large numbers results and
verify these numerically. We conclude that self-exciting shocks increase the
systemic risk in the network and their presence in interbank networks should
not be ignored
Systemic risk in a mean-field model of interbank lending with self-exciting shocks
In this paper we consider a mean-field model of interacting diffusions for
the monetary reserves in which the reserves are subjected to a self- and
cross-exciting shock. This is motivated by the financial acceleration and fire
sales observed in the market. We derive a mean-field limit using a weak
convergence analysis and find an explicit measure-valued process associated
with a large interbanking system. We define systemic risk indicators and
derive, using the limiting process, several law of large numbers results and
verify these numerically. We conclude that self-exciting shocks increase the
systemic risk in the network and their presence in interbank networks should
not be ignored
On original and latent space connectivity in deep neural networks
We study whether inputs from the same class can be connected by a continuous
path, in original or latent representation space, such that all points on the
path are mapped by the neural network model to the same class. Understanding
how the neural network views its own input space and how the latent spaces are
structured has value for explainability and robustness. We show that paths,
linear or nonlinear, connecting same-class inputs exist in all cases studied
A neural network-based framework for financial model calibration
A data-driven approach called CaNN (Calibration Neural Network) is proposed
to calibrate financial asset price models using an Artificial Neural Network
(ANN). Determining optimal values of the model parameters is formulated as
training hidden neurons within a machine learning framework, based on available
financial option prices. The framework consists of two parts: a forward pass in
which we train the weights of the ANN off-line, valuing options under many
different asset model parameter settings; and a backward pass, in which we
evaluate the trained ANN-solver on-line, aiming to find the weights of the
neurons in the input layer. The rapid on-line learning of implied volatility by
ANNs, in combination with the use of an adapted parallel global optimization
method, tackles the computation bottleneck and provides a fast and reliable
technique for calibrating model parameters while avoiding, as much as possible,
getting stuck in local minima. Numerical experiments confirm that this
machine-learning framework can be employed to calibrate parameters of
high-dimensional stochastic volatility models efficiently and accurately.Comment: 34 pages, 9 figures, 11 table