Your English writing platform
Discover LudwigSuggestions(1)
Exact(4)
In general, the observation of a random time, in particular a default time, is modelled by the progressive enlargement of filtration, as proposed by Elliott et al. (2000) and Bielecki and Rutkowski (2002).
From the practical point of view, when a random event (which can be more general than a default time) arrives, there will often be some extra accompanying information revealed at the random time.
Modelling the flow of market information concerning a default time is crucial and in this paper we consider a process, β=(β t, t≥0), whose natural filtration (mathbb {F}^{beta }) describes the flow of information available for market agents about the time at which the default occurs.
Each transition has a default time length you can alter in this way.
Similar(56)
To compute the activity weights a (p)(k), first, a default time-constant Δ act-def is applied to the activity weights.
This leads to a yearly default time rate for which the actual power supplied does not meet the announcement within a given tolerance.
A predictable default time is typical of structural credit risk models, while totally inaccessible default times are one of the most important features of reduced-form credit risk models.
This leads to an annual default time rate (DTR) for which the actual power supplied to the grid does not match the day-ahead power bid within a given tolerance.
As a concrete case, we consider a hybrid default model similar as in Campi et al. (2009) where the filtration (mathbb {F}) is generated by a Brownian motion and a Poisson process, the default time is the minimum of two random times: the first hitting time of a Brownian diffusion and the first jump time of the Poisson process.
Non-trivial and sufficient conditions for making the default time a predictable stopping time will be considered in another paper, (Bedini and Hinz 2017).
The idea of modelling the information about the default time with a Brownian bridge defined on a stochastic interval was introduced in the thesis (Bedini 2012).
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com