880 research outputs found
Debt valuation and chapter 22
Numerous studies have examined the effect on credit spreads of renegotiation. These studies have generally focused on the impact on spread levels in general, and not on how renegotiation influences the relative pricing of senior versus junior debt claims. In this paper, we show that the scope for sequential renegotiation may reduce and even eliminate the premium for debt seniority. Our analysis also explains why companies may engage in repeated Chapter 11 bankruptcy filings (a phenomenon commonly referred to as Chapter 22)
Commentary on four papers on credit risk modeling
Bank capital ; Bank loans ; Risk ; Bank management
European pension systems: a simulation analysis
Pension systems in different countries vary widely in such aspects as the dependence of benefits on earlier labour income, the minimum permitted retirement age and limits on labour supply after retirement. This paper uses a simulation model of a rational, utility-maximising household facing the detailed pension provisions of eight European countries to study microeconomic distortions induced by the different rules and regulations. We examine in particular the impact on savings, labour supply, retirement age decisions and welfare.
Compressive Embedding and Visualization using Graphs
Visualizing high-dimensional data has been a focus in data analysis
communities for decades, which has led to the design of many algorithms, some
of which are now considered references (such as t-SNE for example). In our era
of overwhelming data volumes, the scalability of such methods have become more
and more important. In this work, we present a method which allows to apply any
visualization or embedding algorithm on very large datasets by considering only
a fraction of the data as input and then extending the information to all data
points using a graph encoding its global similarity. We show that in most
cases, using only samples is sufficient to diffuse the
information to all data points. In addition, we propose quantitative
methods to measure the quality of embeddings and demonstrate the validity of
our technique on both synthetic and real-world datasets
Tracking Time-Vertex Propagation using Dynamic Graph Wavelets
Graph Signal Processing generalizes classical signal processing to signal or
data indexed by the vertices of a weighted graph. So far, the research efforts
have been focused on static graph signals. However numerous applications
involve graph signals evolving in time, such as spreading or propagation of
waves on a network. The analysis of this type of data requires a new set of
methods that fully takes into account the time and graph dimensions. We propose
a novel class of wavelet frames named Dynamic Graph Wavelets, whose time-vertex
evolution follows a dynamic process. We demonstrate that this set of functions
can be combined with sparsity based approaches such as compressive sensing to
reveal information on the dynamic processes occurring on a graph. Experiments
on real seismological data show the efficiency of the technique, allowing to
estimate the epicenter of earthquake events recorded by a seismic network
Low-Rank Matrices on Graphs: Generalized Recovery & Applications
Many real world datasets subsume a linear or non-linear low-rank structure in
a very low-dimensional space. Unfortunately, one often has very little or no
information about the geometry of the space, resulting in a highly
under-determined recovery problem. Under certain circumstances,
state-of-the-art algorithms provide an exact recovery for linear low-rank
structures but at the expense of highly inscalable algorithms which use nuclear
norm. However, the case of non-linear structures remains unresolved. We revisit
the problem of low-rank recovery from a totally different perspective,
involving graphs which encode pairwise similarity between the data samples and
features. Surprisingly, our analysis confirms that it is possible to recover
many approximate linear and non-linear low-rank structures with recovery
guarantees with a set of highly scalable and efficient algorithms. We call such
data matrices as \textit{Low-Rank matrices on graphs} and show that many real
world datasets satisfy this assumption approximately due to underlying
stationarity. Our detailed theoretical and experimental analysis unveils the
power of the simple, yet very novel recovery framework \textit{Fast Robust PCA
on Graphs
Inpainting of long audio segments with similarity graphs
We present a novel method for the compensation of long duration data loss in
audio signals, in particular music. The concealment of such signal defects is
based on a graph that encodes signal structure in terms of time-persistent
spectral similarity. A suitable candidate segment for the substitution of the
lost content is proposed by an intuitive optimization scheme and smoothly
inserted into the gap, i.e. the lost or distorted signal region. Extensive
listening tests show that the proposed algorithm provides highly promising
results when applied to a variety of real-world music signals
- âŠ