1,592 research outputs found
Holomorphic transforms with application to affine processes
In a rather general setting of It\^o-L\'evy processes we study a class of
transforms (Fourier for example) of the state variable of a process which are
holomorphic in some disc around time zero in the complex plane. We show that
such transforms are related to a system of analytic vectors for the generator
of the process, and we state conditions which allow for holomorphic extension
of these transforms into a strip which contains the positive real axis. Based
on these extensions we develop a functional series expansion of these
transforms in terms of the constituents of the generator. As application, we
show that for multidimensional affine It\^o-L\'evy processes with state
dependent jump part the Fourier transform is holomorphic in a time strip under
some stationarity conditions, and give log-affine series representations for
the transform.Comment: 30 page
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Optimal seismic upgrade timing in seaports with increasing throughput demand via real options
A real options (RO) formulation is proposed for decision-making on the timing to upgrade the seismic performance of existing seaports with increasing throughput demand in earthquake prone areas. The pay-off of the seismic upgrade investment option is estimated based on projected net earnings, repair cost, and downtime for a damaging reference seismic event having a pre-specified annual probability of occurrence. These projections inform a discrete-time RO binomial tree, following the American option valuation framework, which propagates the probability of the reference seismic event assuming Poisson temporal distribution of earthquake occurrence. The net present value of the expected annual payoff of the considered investment is used as an index supporting risk-informed decision-making discounted by the weighted average cost of capital (WACC). Numerical examples pertaining to decision makers with different capital cost, namely port authorities and terminal operators, operating in different economic environments typical of developed and developing countries are furnished to illustrate the applicability of the proposed RO formulation. It is found that high WACC and/or low throughput growth bring the optimal seismic upgrade timing forward, while earthquake consequences and upgrade cost have almost no influence on this timing
Derivatives and Credit Contagion in Interconnected Networks
The importance of adequately modeling credit risk has once again been
highlighted in the recent financial crisis. Defaults tend to cluster around
times of economic stress due to poor macro-economic conditions, {\em but also}
by directly triggering each other through contagion. Although credit default
swaps have radically altered the dynamics of contagion for more than a decade,
models quantifying their impact on systemic risk are still missing. Here, we
examine contagion through credit default swaps in a stylized economic network
of corporates and financial institutions. We analyse such a system using a
stochastic setting, which allows us to exploit limit theorems to exactly solve
the contagion dynamics for the entire system. Our analysis shows that, by
creating additional contagion channels, CDS can actually lead to greater
instability of the entire network in times of economic stress. This is
particularly pronounced when CDS are used by banks to expand their loan books
(arguing that CDS would offload the additional risks from their balance
sheets). Thus, even with complete hedging through CDS, a significant loan book
expansion can lead to considerably enhanced probabilities for the occurrence of
very large losses and very high default rates in the system. Our approach adds
a new dimension to research on credit contagion, and could feed into a rational
underpinning of an improved regulatory framework for credit derivatives.Comment: 26 pages, 7 multi-part figure
A nonparametric urn-based approach to interacting failing systems with an application to credit risk modeling
In this paper we propose a new nonparametric approach to interacting failing
systems (FS), that is systems whose probability of failure is not negligible in
a fixed time horizon, a typical example being firms and financial bonds. The
main purpose when studying a FS is to calculate the probability of default and
the distribution of the number of failures that may occur during the
observation period. A model used to study a failing system is defined default
model. In particular, we present a general recursive model constructed by the
means of inter- acting urns. After introducing the theoretical model and its
properties we show a first application to credit risk modeling, showing how to
assess the idiosyncratic probability of default of an obligor and the joint
probability of failure of a set of obligors in a portfolio of risks, that are
divided into reliability classes
Affine term structure models : a time-changed approach with perfect fit to market curves
We address the so-called calibration problem which consists of fitting in a
tractable way a given model to a specified term structure like, e.g., yield or
default probability curves. Time-homogeneous jump-diffusions like Vasicek or
Cox-Ingersoll-Ross (possibly coupled with compounded Poisson jumps, JCIR), are
tractable processes but have limited flexibility; they fail to replicate actual
market curves. The deterministic shift extension of the latter (Hull-White or
JCIR++) is a simple but yet efficient solution that is widely used by both
academics and practitioners. However, the shift approach is often not
appropriate when positivity is required, which is a common constraint when
dealing with credit spreads or default intensities. In this paper, we tackle
this problem by adopting a time change approach. On the top of providing an
elegant solution to the calibration problem under positivity constraint, our
model features additional interesting properties in terms of implied
volatilities. It is compared to the shift extension on various credit risk
applications such as credit default swap, credit default swaption and credit
valuation adjustment under wrong-way risk. The time change approach is able to
generate much larger volatility and covariance effects under the positivity
constraint. Our model offers an appealing alternative to the shift in such
cases.Comment: 44 pages, figures and table
Moody's Correlated Binomial Default Distributions for Inhomogeneous Portfolios
This paper generalizes Moody's correlated binomial default distribution for
homogeneous (exchangeable) credit portfolio, which is introduced by Witt, to
the case of inhomogeneous portfolios. As inhomogeneous portfolios, we consider
two cases. In the first case, we treat a portfolio whose assets have uniform
default correlation and non-uniform default probabilities. We obtain the
default probability distribution and study the effect of the inhomogeneity on
it. The second case corresponds to a portfolio with inhomogeneous default
correlation. Assets are categorized in several different sectors and the
inter-sector and intra-sector correlations are not the same. We construct the
joint default probabilities and obtain the default probability distribution. We
show that as the number of assets in each sector decreases, inter-sector
correlation becomes more important than intra-sector correlation. We study the
maximum values of the inter-sector default correlation. Our generalization
method can be applied to any correlated binomial default distribution model
which has explicit relations to the conditional default probabilities or
conditional default correlations, e.g. Credit Risk, implied default
distributions. We also compare some popular CDO pricing models from the
viewpoint of the range of the implied tranche correlation.Comment: 29 pages, 17 figures and 1 tabl
Systemic Risk and Default Clustering for Large Financial Systems
As it is known in the finance risk and macroeconomics literature,
risk-sharing in large portfolios may increase the probability of creation of
default clusters and of systemic risk. We review recent developments on
mathematical and computational tools for the quantification of such phenomena.
Limiting analysis such as law of large numbers and central limit theorems allow
to approximate the distribution in large systems and study quantities such as
the loss distribution in large portfolios. Large deviations analysis allow us
to study the tail of the loss distribution and to identify pathways to default
clustering. Sensitivity analysis allows to understand the most likely ways in
which different effects, such as contagion and systematic risks, combine to
lead to large default rates. Such results could give useful insights into how
to optimally safeguard against such events.Comment: in Large Deviations and Asymptotic Methods in Finance, (Editors: P.
Friz, J. Gatheral, A. Gulisashvili, A. Jacqier, J. Teichmann) , Springer
Proceedings in Mathematics and Statistics, Vol. 110 2015
CONVERGENCE OF AMERICAN OPTION VALUES FROM DISCRETE- TO CONTINUOUS-TIME FINANCIAL MODELS 1
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75553/1/j.1467-9965.1994.tb00059.x.pd
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