Technical paper/Gaussian model
Semi-analytic conditional expectations
A data-driven approach to computing expectations for the pricing and hedging of exotics
Optimal turnover, liquidity and autocorrelation
A novel optimal execution approach via continuous-time stochastic processes is introduced
Independent component analysis is proposed as an alternative to principal component analysis
Dynamically controlled kernel estimation
An accurate data-driven and model-agnostic method to compute conditional expectations is presented
Probabilistic machine learning for local volatility
In this paper, the authors propose to approach the calibration problem of local volatility with Bayesian statistics to infer a conditional distribution over functions given observed data.
Risk measures: a generalization from the univariate to the matrix-variate
This paper develops a method for estimating value-at-risk and conditional value-at-risk when the underlying risk factors follow a beta distribution in a univariate and a matrix-variate setting.
Machine learning hedge strategy with deep Gaussian process regression
An optimal hedging strategy for options in discrete time using a reinforcement learning technique
Gaussian process regression for derivative portfolio modeling and application to credit valuation adjustment computations
The authors present a multi-Gaussian process regression approach, which is well suited for the over-the-counter derivative portfolio valuation involved in credit valuation adjustment (CVA) computation.
Variance optimal hedging with application to electricity markets
In this paper, the author uses the mean–variance hedging criterion to value contracts in incomplete markets.
Model risk tiering: an exploration of industry practices and principles
This paper seeks to shed light on one critical area of such frameworks: model risk tiering, or the rating of risk inherent in the use of individual models, which can benefit a firm’s resource allocation and overall risk management capabilities.
Equity market impact modeling: an empirical analysis for the Chinese market
This paper discusses and derives the extremum of the expectation of permanent impact and realized impact by constructing several special trading trajectories in the Chinese market.
Multifactor granularity adjustments for market and counterparty risks
In this paper, the authors propose several flexible families of models to manage the market and/or the counterparty risk of portfolios of financial assets.
Pricing multivariate barrier reverse convertibles with factor-based subordinators
In this paper, the authors study factor-based subordinated Lévy processes in their variance gamma (VG) and normal inverse Gaussian (NIG) specifications, and focus on their ability to price multivariate exotic derivatives.
Model risk in the Fundamental Review of the Trading Book: the case of the Default Risk Charge
This paper assesses the model risk associated with the copula choice for the calculation of the Default Risk Charge (DRC) measure.
Primary-firm-driven portfolio loss
This paper describes a simple model that can be used for risk management.
Thomas Roos derives model-independent bounds for amortising and accreting Bermudan swaptions
Smile with the Gaussian term structure model
This paper presents a natural extension of the LGM that keeps the affine structure and generates an implied volatility smile.
NetMES: a network based marginal expected shortfall measure
This paper aims to build novel measures of systemic risk that take the multivariate nature of the problem into account by means of network models.
Time series models for credit default swap premiums
This paper analyzes the theoretical properties and statistical behavior of credit default swap (CDS) premiums over time.
Cutting edge intro: Righting wrong-way risk
Models that describe wrong-way risk should move away from simplistic copula models, critics say.
Risk evaluation of mortgage-loan portfolios in a low interest rate environment
Volume 16, Issue 5 (2014)
Cutting edge intro: CDOs and the risk of risk aversion
New analysis shows CDOs can withstand high levels of correlation – what they can’t cope with, though, is a sudden change in risk appetite