Bayesian modelling
The realized local volatility surface
The authors put forward a Bayesian nonparametric estimation method which reconstructs a counterfactual generalized Wiener measure from historical price data.
Interpolating commodity futures prices with Kriging
A futures price’s term structure is built to account for trends and seasonality effects
Quants see promise in Bayesian machine learning
Risk USA: probability theory may hold key to creating ‘self-aware’ AI
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.
Decomposing supply shocks in the US electricity industry: evidence from a time-varying Bayesian panel vector autoregression model
This paper investigates spillovers between electricity supply shocks and US growth, using monthly data from forty-eight US states from January 2001 to September 2016, and employs a novel strategy for electricity supply shocks based on a time-varying…
Quantifying systemic risk using Bayesian networks
Creditworthiness of individual entities may offer an insight into systemic risk of financial markets
Estimating marginal effects of key factors that influence wholesale electricity demand and price distributions in Texas via quantile variable selection methods
Using a large data set from the Electric Reliability Council of Texas, this study uses quantile regressions and attendant variable selection methods to choose the most important factors that influence demand and price distributions; subsequently, the…
Static and dynamic risk capital allocations with the Euler rule
This paper studies the volatility of the Euler rule for capital allocation in static and dynamic empirical applications with a simulated history.
A note on the standard measurement approach versus the loss distribution approach–advanced measurement approach: the dawning of a new regulation
This paper presents a nonexhaustive review of the literature on operational risk quantification under a combination of the loss distribution approach model – the most commonly used of the AMA models – and extreme value theory.
Bayesian analysis in an aggregate loss model: validation of the structure functions
This paper considers the empirical evaluation of a collective risk model with the geometric as the primary distribution and the exponential as the secondary distribution.
Why risk aversion should be built into product structuring
Irrational behaviours that creep into product structuring can be controlled mathematically
Elasticity theory of structuring
Andrei Soklakov presents a product design theory that incorporates Bayesian information processing and risk aversion
A correlated structural credit risk model with random coefficients and its Bayesian estimation using stock and credit market information
Using historical equity and credit market data, this paper illustrates the validation of a structural correlated default model applied to Black–Cox setups.
The econometrics of Bayesian graphical models: a review with financial application
This paper provides a review of graphical modeling and describes potential applications in econometrics and finance.
Comparing alternative mixing models for external operational risk data
Mixing, not scaling, best approach for using external losses
Bayesian synthesis of portfolio credit risk with missing ratings
This paper uses a maximum likelihood estimation to assess the projected average default rates of debt portfolios.
Data provider of the year: StatWeather
StatWeather impresses energy traders with long-range forecasts
Cutting Edge introduction: Creative stress testing
New stress-testing method offers a break from decades-old traditio
Bayesian operational risk models
This paper proposes a methodology to frame risk self-assessment data into suitable prior distributions that can produce posterior distributions from which accurate operational risk measures.
Cutting edge introduction: Hidden models for hidden costs
NYU quants use Bayesian techniques to sequence trades, considering trading costs and multiple assets