2012
DOI: 10.1063/1.4759592
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Non-arbitrage in financial markets: A Bayesian approach for verification

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Cited by 4 publications
(4 citation statements)
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References 28 publications
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“…• Testing covariance structures in multivariate normal models, treating in a unified way several alternative hypotheses (often treated as special cases in the literature): [123,232]; • Testing unit root and cointegration hypotheses in time series, using plain and simple forms of prior information like flat or Jeffreys priors (no need for artificial priors): [42,51,52,234]; • Solving Bayesian classification problems and testing nested and non-nested or separate hypotheses: [8,9,[124][125][126]160]; • Analyzing systems' reliability from failure datasets: [104,143,177] • Testing dependence structures using statistical copulas: [83]; • Testing (non)-informative sampling conditions in statistical surveys: [199]; • Model selection for generalized Poisson distributions: [97,205]; • Model selection for generalized jump diffusion and Brownian motions, extremal distributions, and persistent memory processes: [6,18,122,171,172]; • Testing independence structures in contingency tables and multinomial models: [7,19,148,158]; • Software certification according to compliance conditions: [153]; • Testing market equilibrium conditions for fundamental and financial derivative asset prices: [39]; • Testing hypotheses in empirical economic studies: [41]; • Event identification in acoustic signal processing: [98][99][100]; • Testing Hardy-Weinberg equilibrium in genetics: [29,106,…”
Section: Applicationsmentioning
confidence: 99%
“…• Testing covariance structures in multivariate normal models, treating in a unified way several alternative hypotheses (often treated as special cases in the literature): [123,232]; • Testing unit root and cointegration hypotheses in time series, using plain and simple forms of prior information like flat or Jeffreys priors (no need for artificial priors): [42,51,52,234]; • Solving Bayesian classification problems and testing nested and non-nested or separate hypotheses: [8,9,[124][125][126]160]; • Analyzing systems' reliability from failure datasets: [104,143,177] • Testing dependence structures using statistical copulas: [83]; • Testing (non)-informative sampling conditions in statistical surveys: [199]; • Model selection for generalized Poisson distributions: [97,205]; • Model selection for generalized jump diffusion and Brownian motions, extremal distributions, and persistent memory processes: [6,18,122,171,172]; • Testing independence structures in contingency tables and multinomial models: [7,19,148,158]; • Software certification according to compliance conditions: [153]; • Testing market equilibrium conditions for fundamental and financial derivative asset prices: [39]; • Testing hypotheses in empirical economic studies: [41]; • Event identification in acoustic signal processing: [98][99][100]; • Testing Hardy-Weinberg equilibrium in genetics: [29,106,…”
Section: Applicationsmentioning
confidence: 99%
“…• Reference [22] applies the FBST to detect equilibrium conditions, or the lack thereof, in market prices of economic commodities or financial derivative contracts.…”
Section: Statistical Modelingmentioning
confidence: 99%
“…472 More specifically, this proposal advances the idea of "decompos[ing] [CCP] functions into discrete elements and tailor the operational and organizational framework to the particular characteristics of each." 473 All of these proposals are important and need to be pursued, but none of the proposals directly address our current dilemma that the implementation of a central clearing mandate after the Great Recession has created a dichotomy between the goal of risk mitigation and the creation of an entirely new level of systemic risk. The risk profile of CCPs has changed by creating institutions that may be too-big-to-bail; therefore, other reforms are needed.…”
Section: ]mentioning
confidence: 99%