pwc-archive/papers-with-abstracts huggingface dataset:
Reflected Backward SDE approach to the price-hedge of defaultable claims with contingent switching CSA
Date: 2015-02-26 00:00:00

In this work we study the price-hedge issue for general defaultable contracts
characterized by the presence of a contingent CSA of switching type. This is a
contingent risk mitigation mechanism that allow the counterparties of a
defaultable contract to switch from zero to full/perfect collateralization and
switch back whenever until maturity T paying some instantaneous switching costs
, taking in account in the picture CVA, collateralization and the funding
problem. We have been lead to the study of this theoretical pricing/hedging
problem, by the economic significance of this type of mechanism which allows a
greater flexibility in managing all the defaultable contract risks with respect
to the "standard" non contingent mitigation mechanisms (as full or partial
collateralization). In particular, our approach through hedging strategy
decomposition of the claim (proposition 2.2.5) and its price-hedge
representation through system of nonlinear reflected BSDE (theorem 3.2.4) are
the main contribution of the work.
Fragmentation trees reloaded
Date: 2015-01-28 00:00:00
Tasks: Combinatorial Optimization

Metabolites, small molecules that are involved in cellular reactions, provide
a direct functional signature of cellular state. Untargeted metabolomics
experiments usually relies on tandem mass spectrometry to identify the
thousands of compounds in a biological sample. Today, the vast majority of
metabolites remain unknown. Fragmentation trees have become a powerful tool for
the interpretation of tandem mass spectrometry data of small molecules. These
trees are found by combinatorial optimization, and aim at explaining the
experimental data via fragmentation cascades. To obtain biochemically
meaningful results requires an elaborate optimization function. We present a
new scoring for computing fragmentation trees, transforming the combinatorial
optimization into a maximum a posteriori estimator. We demonstrate the
superiority of the new scoring for two tasks: Both for the de novo
identification of molecular formulas of unknown compounds, and for searching a
database for structurally similar compounds, our methods performs significantly
better than the previous scoring, as well as other methods for this task. Our
method can expedite the workflow for untargeted metabolomics, allowing
researchers to investigate unknowns using automated computational methods.
Protein residue networks from a local search perspective
Date: 2015-11-30 00:00:00
Tasks: Diversity, Protein Folding

We examined protein residue networks (PRNs) from a local search perspective
to understand why PRNs are highly clustered when having short paths is
important for protein functionality. We found that by adopting a local search
perspective, this conflict between form and function is resolved as increased
clustering actually helps to reduce path length in PRNs. Further, the paths
found via our EDS local search algorithm are more congruent with the
characteristics of intra-protein communication. EDS identifies a subset of PRN
edges called short-cuts that are distinct, have high usage, impacts EDS path
length, diversity and stretch, and are dominated by short-range contacts. The
short-cuts form a network (SCN) that increases in size and transitivity as a
protein folds. The structure of a SCN supports its function and formation, and
the function of a SCN influences its formation. Several significant differences
in terms of SCN structure, function and formation is found between successful
and unsuccessful MD trajectories. By connecting the static and the dynamic
aspects of PRNs, the protein folding process becomes a problem of graph
formation with the purpose of forming suitable pathways within proteins.
Purchasing Term Life Insurance to Reach a Bequest Goal while Consuming
Date: 2016-02-26 00:00:00

We determine the optimal strategies for purchasing term life insurance and
for investing in a risky financial market in order to maximize the probability
of reaching a bequest goal while consuming from an investment account. We
extend Bayraktar and Young (2015) by allowing the individual to purchase term
life insurance to reach her bequest goal. The premium rate for life insurance,
$h$, serves as a parameter to connect two seemingly unrelated problems. As the
premium rate approaches $0$, covering the bequest goal becomes costless, so the
individual simply wants to avoid ruin that might result from her consumption.
Thus, as $h$ approaches $0$, the problem in this paper becomes equivalent to
minimizing the probability of lifetime ruin, which is solved in Young (2004).
On the other hand, as the premium rate becomes arbitrarily large, the
individual will not buy life insurance to reach her bequest goal. Thus, as $h$
approaches infinity, the problem in this paper becomes equivalent to maximizing
the probability of reaching the bequest goal when life insurance is not
available in the market, which is solved in Bayraktar and Young (2015).
A framework for analyzing ecological trait-based models in multi-dimensional niche spaces
Date: 2015-02-27 00:00:00

We develop an theoretical approach for predicting biodiversity in
multi-dimensional niche spaces, arising due to ecological drivers such as
competitive exclusion. The novelty of our approach relies on the fact that
ecological niches are described by sequences of strings, which allows us to
describe multiple traits. We define the mathematical framework for analyzing
pattern forming instabilities in these models, showing surprisingly that the
analytic linear theory predicts the asymptotically long time population
distributions of niches in the model. We propose a test for identifying
ecological drivers in biodiversity distributions, based on representing
ecosystem data by means of a certain transform introduced in the theory.
Detecting the Escherichia coli metabolic backbone
Date: 2016-02-21 00:00:00

The heterogeneity of reaction fluxes present in a metabolic network within a
single flux state can be exploited to construct the so-called backbone as a
reduced version of metabolism. The backbone maintains all significant fluxes
producing or consuming metabolites while displaying a substantially decreased
number of interconnections and, hence, it becomes a useful tool to extract
primary metabolic routes. Here, we disclose the metabolic backbone of
Escherichia coli using the computationally predicted fluxes which maximize the
growth rate in glucose minimal medium, and we compare it with the backbone of
Mycoplasma pneumoniae, a much simpler organism. We find that the central core
in both backbones is mainly composed of reactions in ancient pathways, still
playing at present a key role in energy metabolism. In E. coli, the analysis of
the backbone reveals that the synthesis of nucleotides and the metabolism of
lipids form smaller cores which rely critically on energy metabolism; but not
conversely. At the same time, an analysis of the dependence of this backbone on
media composition leads to the identification of pathways sensitive to
environmental changes. The metabolic backbone of an organism is thus useful to
trace simultaneously both its evolution and adaptation fingerprints.
Coupling news sentiment with web browsing data improves prediction of intra-day price dynamics
Date: 2015-12-15 00:00:00
Tasks: Sentiment Analysis

The new digital revolution of big data is deeply changing our capability of
understanding society and forecasting the outcome of many social and economic
systems. Unfortunately, information can be very heterogeneous in the
importance, relevance, and surprise it conveys, affecting severely the
predictive power of semantic and statistical methods. Here we show that the
aggregation of web users' behavior can be elicited to overcome this problem in
a hard to predict complex system, namely the financial market. Specifically,
our in-sample analysis shows that the combined use of sentiment analysis of
news and browsing activity of users of Yahoo! Finance greatly helps forecasting
intra-day and daily price changes of a set of 100 highly capitalized US stocks
traded in the period 2012-2013. Sentiment analysis or browsing activity when
taken alone have very small or no predictive power. Conversely, when
considering a "news signal" where in a given time interval we compute the
average sentiment of the clicked news, weighted by the number of clicks, we
show that for nearly 50% of the companies such signal Granger-causes hourly
price returns. Our result indicates a "wisdom-of-the-crowd" effect that allows
to exploit users' activity to identify and weigh properly the relevant and
surprising news, enhancing considerably the forecasting power of the news
sentiment.
Variance reduced multilevel path simulation: going beyond the complexity $\varepsilon^{-2}$
Date: 2017-03-11 00:00:00

In this paper a novel modification of the multilevel Monte Carlo approach,
allowing for further significant complexity reduction, is proposed. The idea of
the modification is to use the method of control variates to reduce variance at
level zero. We show that, under a proper choice of control variates, one can
reduce the complexity order of the modified MLMC algorithm down to
$\varepsilon^{-2+\delta}$ for any $\delta\in [0,1)$ with $\varepsilon$ being
the precision to be achieved. These theoretical results are illustrated by
several numerical examples.
Extending the multi-level method for the simulation of stochastic biological systems
Date: 2016-05-19 00:00:00

The multi-level method for discrete state systems, first introduced by
Anderson and Higham [Multiscale Model. Simul. 10:146--179, 2012], is a highly
efficient simulation technique that can be used to elucidate statistical
characteristics of biochemical reaction networks. A single point estimator is
produced in a cost-effective manner by combining a number of estimators of
differing accuracy in a telescoping sum, and, as such, the method has the
potential to revolutionise the field of stochastic simulation. The first term
in the sum is calculated using an approximate simulation algorithm, and can be
calculated quickly but is of significant bias. Subsequent terms successively
correct this bias by combining estimators from approximate stochastic
simulations algorithms of increasing accuracy, until a desired level of
accuracy is reached.
  In this paper we present several refinements of the multi-level method which
render it easier to understand and implement, and also more efficient. Given
the substantial and complex nature of the multi-level method, the first part of
this work (Sections 2 - 5) is written as a tutorial, with the aim of providing
a practical guide to its use. The second part (Sections 6 - 8) takes on a form
akin to a research article, thereby providing the means for a deft
implementation of the technique, and concludes with a discussion of a number of
open problems.
On the complexity of computing MP distance between binary phylogenetic trees
Date: 2015-01-18 00:00:00

Within the field of phylogenetics there is great interest in distance
measures to quantify the dissimilarity of two trees. Recently, a new distance
measure has been proposed: the Maximum Parsimony (MP) distance. This is based
on the difference of the parsimony scores of a single character on both trees
under consideration, and the goal is to find the character which maximizes this
difference. Here we show that computation of MP distance on two \emph{binary}
phylogenetic trees is NP-hard. This is a highly nontrivial extension of an
earlier NP-hardness proof for two multifurcating phylogenetic trees, and it is
particularly relevant given the prominence of binary trees in the phylogenetics
literature. As a corollary to the main hardness result we show that computation
of MP distance is also hard on binary trees if the number of states available
is bounded. In fact, via a different reduction we show that it is hard even if
only two states are available. Finally, as a first response to this hardness we
give a simple Integer Linear Program (ILP) formulation which is capable of
computing the MP distance exactly for small trees (and for larger trees when
only a small number of character states are available) and which is used to
computationally verify several auxiliary results required by the hardness
proofs.
A Million Metaorder Analysis of Market Impact on the Bitcoin
Date: 2015-09-21 00:00:00

We present a thorough empirical analysis of market impact on the Bitcoin/USD
exchange market using a complete dataset that allows us to reconstruct more
than one million metaorders. We empirically confirm the "square-root law'' for
market impact, which holds on four decades in spite of the quasi-absence of
statistical arbitrage and market marking strategies. We show that the
square-root impact holds during the whole trajectory of a metaorder and not
only for the final execution price. We also attempt to decompose the order flow
into an "informed'' and "uninformed'' component, the latter leading to an
almost complete long-term decay of impact. This study sheds light on the
hypotheses and predictions of several market impact models recently proposed in
the literature and promotes heterogeneous agent models as promising candidates
to explain price impact on the Bitcoin market -- and, we believe, on other
markets as well.
Conditional Analysis and a Principal-Agent problem
Date: 2016-06-14 00:00:00
Tasks: Portfolio Optimization, Translation

We analyze conditional optimization problems arising in discrete time
Principal-Agent problems of delegated portfolio optimization with linear
contracts. Applying tools from Conditional Analysis we show that some results
known in the literature for very specific instances of the problem carry over
to translation invariant and time-consistent utility functions in very general
probabilistic settings. However, we find that optimal contracts must in general
make use of derivatives for compensation.
Bootstrap testing for cross-correlation under low firing activity
Date: 2015-04-16 00:00:00

A new cross-correlation synchrony index for neural activity is proposed. The
index is based on the integration of the kernel estimation of the
cross-correlation function. It is used to test for the dynamic synchronization
levels of spontaneous neural activity under two induced brain states:
sleep-like and awake-like. Two bootstrap resampling plans are proposed to
approximate the distribution of the test statistics. The results of the first
bootstrap method indicate that it is useful to discern significant differences
in the synchronization dynamics of brain states characterized by a neural
activity with low firing rate. The second bootstrap method is useful to unveil
subtle differences in the synchronization levels of the awake-like state,
depending on the activation pathway.
Comprehensive Time-Series Regression Models Using GRETL -- U.S. GDP and Government Consumption Expenditures & Gross Investment from 1980 to 2013
Date: 2019-08-17 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Regression

Using Gretl, I apply ARMA, Vector ARMA, VAR, state-space model with a Kalman
filter, transfer-function and intervention models, unit root tests,
cointegration test, volatility models (ARCH, GARCH, ARCH-M, GARCH-M,
Taylor-Schwert GARCH, GJR, TARCH, NARCH, APARCH, EGARCH) to analyze quarterly
time series of GDP and Government Consumption Expenditures & Gross Investment
(GCEGI) from 1980 to 2013. The article is organized as: (I) Definition; (II)
Regression Models; (III) Discussion. Additionally, I discovered a unique
interaction between GDP and GCEGI in both the short-run and the long-run and
provided policy makers with some suggestions. For example in the short run, GDP
responded positively and very significantly (0.00248) to GCEGI, while GCEGI
reacted positively but not too significantly (0.08051) to GDP. In the long run,
current GDP responded negatively and permanently (0.09229) to a shock in past
GCEGI, while current GCEGI reacted negatively yet temporarily (0.29821) to a
shock in past GDP. Therefore, policy makers should not adjust current GCEGI
based merely on the condition of current and past GDP. Although increasing
GCEGI does help GDP in the short-term, significantly abrupt increase in GCEGI
might not be good to the long-term health of GDP. Instead, a balanced,
sustainable, and economically viable solution is recommended, so that the
short-term benefits to the current economy from increasing GCEGI often largely
secured by the long-term loan outweigh or at least equal to the negative effect
to the future economy from the long-term debt incurred by the loan. Finally, I
found that non-normally distributed volatility models generally perform better
than normally distributed ones. More specifically, TARCH-GED performs the best
in the group of non-normally distributed, while GARCH-M does the best in the
group of normally distributed.
Teaching Computational Neuroscience
Date: 2015-03-16 00:00:00

The problems and beauty of teaching computational neuroscience are discussed
by reviewing three new textbooks.
Accurate chromosome segregation by probabilistic self-organization
Date: 2015-06-23 00:00:00

Background: For faithful chromosome segregation during cell division, correct
attachments must be established between sister chromosomes and microtubules
from opposite spindle poles through kinetochores (chromosome bi-orientation).
Incorrect attachments of kinetochore microtubules (kMTs) lead to chromosome
mis-segregation and aneuploidy, which is often associated with developmental
abnormalities such as Down syndrome and diseases including cancer. The
interaction between kinetochores and microtubules is highly dynamic with
frequent attachments and detachments. However, it remains unclear how
chromosome bi-orientation is achieved with such accuracy in such a dynamic
process.
  Results: To gain new insight into this essential process, we have developed a
simple mathematical model of kinetochore-microtubule interactions during cell
division in general, i.e. both mitosis and meiosis. Firstly, the model reveals
that the balance between attachment and detachment probabilities of kMTs is
crucial for correct chromosome bi-orientation. With the right balance,
incorrect attachments are resolved spontaneously into correct bi-oriented
conformations while an imbalance leads to persistent errors. In addition, the
model explains why errors are more commonly found in the first meiotic division
(meiosis I) than in mitosis and how a faulty conformation can evade the spindle
assembly checkpoint, which may lead to a chromosome loss.
  Conclusions: The proposed model, despite its simplicity, helps us understand
one of the primary causes of chromosomal instability - aberrant
kinetochore-microtubule interactions. The model reveals that chromosome
bi-orientation is a probabilistic self-organization, rather than a
sophisticated process of error detection and correction.
Transcriptional leakage versus noise: A simple mechanism of conversion between binary and graded response in autoregulated genes
Date: 2015-01-16 00:00:00

We study the response of an autoregulated gene to a range of concentrations
of signal molecules. We show that transcriptional leakage and noise due to
translational bursting have the opposite effects. In a positively autoregulated
gene, increasing the noise converts the response from graded to binary, while
increasing the leakage converts the response from binary to graded. Our
findings support the hypothesis that, being a common phenomenon, leaky
expression may be a relatively easy way for evolutionary tuning of the type of
gene response without changing the type of regulation from positive to
negative.
Fast and accurate approximate inference of transcript expression from RNA-seq data
Date: 2015-06-30 00:00:00
Tasks: Bayesian Inference

Motivation: Assigning RNA-seq reads to their transcript of origin is a
fundamental task in transcript expression estimation. Where ambiguities in
assignments exist due to transcripts sharing sequence, e.g. alternative
isoforms or alleles, the problem can be solved through probabilistic inference.
Bayesian methods have been shown to provide accurate transcript abundance
estimates compared to competing methods. However, exact Bayesian inference is
intractable and approximate methods such as Markov chain Monte Carlo (MCMC) and
Variational Bayes (VB) are typically used. While providing a high degree of
accuracy and modelling flexibility, standard implementations can be
prohibitively slow for large datasets and complex transcriptome annotations.
  Results: We propose a novel approximate inference scheme based on VB and
apply it to an existing model of transcript expression inference from RNA-seq
data. Recent advances in VB algorithmics are used to improve the convergence of
the algorithm beyond the standard Variational Bayes Expectation Maximisation
(VBEM) algorithm. We apply our algorithm to simulated and biological datasets,
demonstrating a significant increase in speed with only very small loss in
accuracy of expression level estimation. We carry out a comparative study
against seven popular alternative methods and demonstrate that our new
algorithm provides excellent accuracy and inter-replicate consistency while
remaining competitive in computation time.
  Availability: The methods were implemented in R and C++, and are available as
part of the BitSeq project at \url{https://github.com/BitSeq}. The method is
also available through the BitSeq Bioconductor package. The source code to
reproduce all simulation results can be accessed via
\url{https://github.com/BitSeq/BitSeqVB_benchmarking}.
Local inhibitory plasticity tunes global brain dynamics and allows the emergence of functional brain networks
Date: 2015-01-20 00:00:00
Tasks: Functional Connectivity

Rich, spontaneous brain activity has been observed across a range of
different temporal and spatial scales. These dynamics are thought to be
important t for efficient neural functioning. Experimental evidence suggests
that these neural dynamics are maintained across a variety of different
cognitive states, in response to alterations of the environment and to changes
in brain configuration (e.g., across individuals, development and in many
neurological disorders). This suggests that the brain has evolved mechanisms to
stabilize dynamics and maintain them across a range of situations. Here, we
employ a local homeostatic inhibitory plasticity mechanism, balancing
inhibitory and excitatory activity in a model of macroscopic brain activity
based on white-matter structural connectivity. We demonstrate that the addition
of homeostatic plasticity regulates network activity and allows for the
emergence of rich, spontaneous dynamics across a range of brain configurations.
Furthermore, the presence of homeostatic plasticity maximises the overlap
between empirical and simulated patterns of functional connectivity. Therefore,
this work presents a simple, local, biologically plausible inhibitory mechanism
that allows stable dynamics to emerge in the brain and which facilitates the
formation of functional connectivity networks.