Technical paper/Data
Assessing the efficiency of pure-play internet banks in South Korea, Japan and China with data envelopment analysis
The authors investigate the efficiency of pure-play internet banks in China, Japan and South Korea, recommending they focus on the management of noninterest expenses and income to ensure stable profts.
Default forecasting based on a novel group feature selection method for imbalanced data
The authors construct a group feature selection method which combines optimal instance selection with weighted comprehensive precision in an effort to improve the performance of prediction models in relation to defaulting firms.
Sculpting implied volatility surfaces of illiquid assets
From the stock cumulative distribution function an arbitrage-free volatility surface is derived
Semi-analytic conditional expectations
A data-driven approach to computing expectations for the pricing and hedging of exotics
A structural credit risk model based on purchase order information
This paper proposes a credit risk model based on purchase order information to address the deficiencies of monitoring methods that use only financial statements.
Dynamically controlled kernel estimation
An accurate data-driven and model-agnostic method to compute conditional expectations is presented
Nonlinear risk decomposition for any type of fund
A risk decomposition by fund manager, factor or instrument is proposed
Covariance estimation for risk-based portfolio optimization: an integrated approach
This paper presents a stochastic optimization framework for integrating time-varying factor covariance models in a risk-based portfolio optimization setting.
Bayesian nonparametric covariance estimation with noisy and nonsynchronous asset prices
This paper introduces a Bayesian nonparametric method to estimate the ex post covariance matrix from high-frequency data.
Modeling realized volatility with implied volatility for the EUR/GBP exchange rate
This paper concerns the application of implied volatility in modeling realized volatility in the daily, weekly and monthly horizon using high-frequency data for the EUR/GBP exchange rate.
Efficient representation of supply and demand curves on day-ahead electricity markets
The authors model the supply and demand curves of electricity day-ahead auctions in a parsimonious way by building an appropriate algorithm to present the information about electricity prices and demand with far fewer parameters than the existing…
Nowcasting networks
The authors devise a neural network-based compression/completion methodology for financial nowcasting.
Research on listed companies’ credit ratings, considering classification performance and interpretability
This study uses the correlation coefficient and F-test to select the initial features of a credit evaluation system, and then a validity index for a second selection to ensure that the feature system has the optimum ability to discriminate in determining…
The impact of energy costs on industrial performance: identifying price and quantity effects in the aluminum industry using a data envelopment analysis approach
The authors build a frontier function model with technical and cost efficiency measures to assess the impact of energy costs on competitiveness in the aluminum industry, a heavy energy consumer, by identifying what may be attributed to price and quantity…
Zooming in on equity factor crowding
A measure for crowding in trades is derived from supply and demand imbalances
Neural networks for option pricing and hedging: a literature review
This paper provides a comprehensive review of the field of neural networks, comparing articles in terms of input features, output variables, benchmark models, performance measures, data partition methods and underlying assets. Related work and…
Quantifying systemic risk using Bayesian networks
Creditworthiness of individual entities may offer an insight into systemic risk of financial markets
The market generator
A generative neural network is proposed to create synthetic datasets that mantain the statistical properties of the original dataset
An advanced hybrid classification technique for credit risk evaluation
In this paper, the authors employ a hybrid approach to design a practical and effective CRE model based on a deep belief network (DBN) and the K-means method.
Risk data validation under BCBS 239
Based on a survey of twenty-nine major financial institutions, this paper aims to advise banks and other financial services firms on what is needed to get ready for and become compliant with BCBS 239, especially in the area of risk data validation.
A general framework for constructing bank risk data sets
This paper proposes a general framework for constructing bank risk data sets, which provides an integrated process from data sources to comprehensive risk data sets.