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…
Creditworthiness of individual entities may offer an insight into systemic risk of financial markets
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.
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.
Irrational behaviours that creep into product structuring can be controlled mathematically
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.
This paper provides a review of graphical modeling and describes potential applications in econometrics and finance.
Mixing, not scaling, best approach for using external losses
This paper uses a maximum likelihood estimation to assess the projected average default rates of debt portfolios.
StatWeather impresses energy traders with long-range forecasts
New stress-testing method offers a break from decades-old traditio
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.
NYU quants use Bayesian techniques to sequence trades, considering trading costs and multiple assets
Kolm and Ritter present a multiperiod, multi-asset selection model with transacion costs, kept computationally tractrable
Regulators argue a backstop is needed to avoid too-low modelled numbers
Assessing exposures and vulnerabilities gives sophisticated risk view
Paper focuses on dealing with sparse data
Construction of large portfolios consistent with investors' views and stress test scenarios is a challenging task, considering the volume of information to be processed. Attilio Meucci, David Ardia and Marcello Colasante introduce a technique that…
Stress testing with fully flexible causal inputs