Applications of explainable artificial intelligence in finance—a systematic review of finance, information systems, and computer science literature
Digitalization and technologization affect numerous domains, promising advantages but
also entailing risks. Hence, when decision-makers in highly-regulated domains like Finance …
also entailing risks. Hence, when decision-makers in highly-regulated domains like Finance …
[图书][B] Impact of Artificial Intelligence in business and society: Opportunities and challenges
Belonging to the realm of intelligent technologies, it is increasingly accepted that AI has
evolved from being merely a development standpoint in computer science. Indeed, recent …
evolved from being merely a development standpoint in computer science. Indeed, recent …
Opening the black box–Quantile neural networks for loss given default prediction
R Kellner, M Nagl, D Rösch - Journal of Banking & Finance, 2022 - Elsevier
We extend the linear quantile regression with a neural network structure to enable more
flexibility in every quantile of the bank loan loss given default distribution. This allows us to …
flexibility in every quantile of the bank loan loss given default distribution. This allows us to …
Storm after the Gloomy days: Influences of COVID-19 pandemic on volatility of the energy market.
LT Ha - Resources Policy, 2022 - europepmc.org
Volatility is a common phenomenon in the energy market, but COVID-19 has cast a dark
shadow over this characteristic. In light of this observation, individuals might have an …
shadow over this characteristic. In light of this observation, individuals might have an …
[HTML][HTML] VaR and ES forecasting via recurrent neural network-based stateful models
Due to the widespread and quickly escalating effects of large negative returns, as well as
due to the increase in the importance of regulatory framework for financial institutions, the …
due to the increase in the importance of regulatory framework for financial institutions, the …
Cross-sectional learning of extremal dependence among financial assets
We propose a novel probabilistic model to facilitate the learning of multivariate tail
dependence of multiple financial assets. Our method allows one to construct from known …
dependence of multiple financial assets. Our method allows one to construct from known …
Modeling of Machine Learning-Based Extreme Value Theory in Stock Investment Risk Prediction: A Systematic Literature Review
The stock market is heavily influenced by global sentiment, which is full of uncertainty and is
characterized by extreme values and linear and nonlinear variables. High-frequency data …
characterized by extreme values and linear and nonlinear variables. High-frequency data …
Risk-Neutral Generative Networks
We present a functional generative approach to extract risk-neutral densities from market
prices of options. Specifically, we model the log-returns on the time-to-maturity continuum as …
prices of options. Specifically, we model the log-returns on the time-to-maturity continuum as …
Risk and return prediction for pricing portfolios of non-performing consumer credit
We design a system for risk-analyzing and pricing portfolios of non-performing consumer
credit loans. The rapid development of credit lending business for consumers heightens the …
credit loans. The rapid development of credit lending business for consumers heightens the …
Estimating value at risk: LSTM vs. GARCH
W Ormaniec, M Pitera, S Safarveisi… - arXiv preprint arXiv …, 2022 - arxiv.org
Estimating value-at-risk on time series data with possibly heteroscedastic dynamics is a
highly challenging task. Typically, we face a small data problem in combination with a high …
highly challenging task. Typically, we face a small data problem in combination with a high …