Original research
Stability and convergence of Galerkin schemes for parabolic equations with application to Kolmogorov pricing equations in time-inhomogeneous Lévy models
In this paper the authors derive stability and convergence of fully discrete approximation schemes of solutions to linear parabolic evolution equations governed by time-dependent coercive operators.
Robust product Markovian quantization
In this paper the authors formulate the one-dimensional RMQ and d-dimensional PMQ algorithms as standard vector quantization problems by deriving the density, distribution and lower partial expectation functions of the random variables to be quantized at…
Directional predictability between returns and trading volume in the futures markets of energy: insights into traders’ behavior
This papers aims to test for directional predictability between returns and volume (and vice versa) in the energy futures markets, employing a cross-quantilogram approach that enables the assessment of the temporal association between two stationary time…
Severe but plausible – or not?
In this paper, the authors apply a measure of statistical unusualness, called the Mahalanobis distance, to assess the plausibility of the scenarios used in the Federal Reserve's stress tests.
Automatic differentiation for diffusion operator integral variance reduction
This paper demonstrates applications of automatic differentiation with nested dual numbers in the diffusion operator integral variance-reduction framework originally proposed by Heath and Platen.
Preventing the unpleasant: fraudulent financial statement detection using financial ratios
In this paper, the authors investigate financial fraud in companies listed on the Athens Stock Exchange during the period 2008–18 and propose a model to detect fraudulent financial statements.
Revisiting the linkage between internal audit function characteristics and internal control quality
This paper revisits the linkage between internal audit function characteristics and internal control quality and proposes a random polynomial model for assessing ICQ.
Evaluation of backtesting on risk models based on data envelopment analysis
In this study, different value-at-risk models, which are used to measure market risk, are analyzed under different estimation approaches and backtested with an alternative strategy.
Modeling multivariate operational losses via copula-based distributions with g-and-h marginals
In this paper, the authors propose a family of copula-based multivariate distributions with g-and-h marginals.
Performance attribution for multifactorial equity portfolios
This paper revisits the cross-sectional approach to the performance analysis of multifactor investment strategies.
A practitioner’s view of the long-term and recent performance of multifactor investment strategies
In this paper the author studies the performance of factor investment strategies from a practitioner’s point of view.
The loss optimization of loan recovery decision times using forecast cashflows
In this paper, a theoretical method is empirically illustrated in finding the best time to forsake a loan such that the overall credit loss is minimized.
On comprehensive balance sheet stress testing and net interest income risk attribution
In this paper the authors propose a framework for granular-level stressed net interest income calculation and profit-and-loss risk attribution.
Estimating future value-at-risk from value samples, and applications to future initial margin
This paper discusses several methods to estimate fVaR or margin requirements and their expected time evolution, from simple options to more complex interest swaps.
Regularization effect on model calibration
This paper compares two methods to calibrate two popular models that are widely used for stochastic volatility modeling (ie, the SABR and Heston models) with the time series of options written on the Nasdaq 100 index to examine the regularization effect…
How to build a risk factor model for non-life insurance risk
In this paper the authors present a dependence model for non-life insurance risk based on risk factors, analogous to those generally used for life insurance or asset risk.
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.
Bank-sourced transition matrixes: are banks’ internal credit risk estimates Markovian?
This study explores banks’ internal credit risk estimates and the associated banksourced transition matrixes.
A cost–benefit analysis of anti-procyclicality: analyzing approaches to procyclicality reduction in central counterparty initial margin models
In this paper, the authors suggest how margin setters and policy makers might measure procyclicality and target particular levels of it by recalibrating parameters in a margin model to reduce its procyclicality or by applying an anti-procyclicality tool.
What drives Bitcoin fees? Using SegWit to assess Bitcoin’s long-run sustainability
In this paper the authors use block-level data from the Bitcoin blockchain to estimate the impact of congestion and the US dollar price on fee rates.
Are there multiple independent risk anomalies in the cross section of stock returns?
Using multivariate portfolio sorts, firm-level cross-sectional regressions and spanning tests, this paper shows that, in the cross section of stock returns, most commonly used risk measures in academia and in practice are separate return predictors with…
Ruin problems in a discrete risk model in a Markovian environment
This paper finds that the derivations in a previous paper by Yang et al (2019) are erroneous, and analyzes the risk model model correctly using the matrix analytic method.
Covid-19 and the credit cycle: 2020 revisited and 2021 outlook
This study continues the author’s examination and forecasts as to the impact of Covid-19 on the US credit cycle after one and a half years since the pandemic first began.
Customer churn prediction for commercial banks using customer-value-weighted machine learning models
In this paper the authors propose a framework to address the issue of customer churn prediction, and they quantify customer values with the use of an improved customer value model.