pwc-archive/papers-with-abstracts huggingface dataset:
A Simple Data-Adaptive Probabilistic Variant Calling Model
Date: 2015-01-13 00:00:00
Tasks: model, Specificity

Background: Several sources of noise obfuscate the identification of single
nucleotide variation (SNV) in next generation sequencing data. For instance,
errors may be introduced during library construction and sequencing steps. In
addition, the reference genome and the algorithms used for the alignment of the
reads are further critical factors determining the efficacy of variant calling
methods. It is crucial to account for these factors in individual sequencing
experiments.
  Results: We introduce a simple data-adaptive model for variant calling. This
model automatically adjusts to specific factors such as alignment errors. To
achieve this, several characteristics are sampled from sites with low mismatch
rates, and these are used to estimate empirical log-likelihoods. These
likelihoods are then combined to a score that typically gives rise to a mixture
distribution. From these we determine a decision threshold to separate
potentially variant sites from the noisy background.
  Conclusions: In simulations we show that our simple proposed model is
competitive with frequently used much more complex SNV calling algorithms in
terms of sensitivity and specificity. It performs specifically well in cases
with low allele frequencies. The application to next-generation sequencing data
reveals stark differences of the score distributions indicating a strong
influence of data specific sources of noise. The proposed model is specifically
designed to adjust to these differences.
Valuation of Barrier Options using Sequential Monte Carlo
Date: 2015-07-24 00:00:00

Sequential Monte Carlo (SMC) methods have successfully been used in many
applications in engineering, statistics and physics. However, these are seldom
used in financial option pricing literature and practice. This paper presents
SMC method for pricing barrier options with continuous and discrete monitoring
of the barrier condition. Under the SMC method, simulated asset values rejected
due to barrier condition are re-sampled from asset samples that do not breach
the barrier condition improving the efficiency of the option price estimator;
while under the standard Monte Carlo many simulated asset paths can be rejected
by the barrier condition making it harder to estimate option price accurately.
We compare SMC with the standard Monte Carlo method and demonstrate that the
extra effort to implement SMC when compared with the standard Monte Carlo is
very little while improvement in price estimate can be significant. Both
methods result in unbiased estimators for the price converging to the true
value as $1/\sqrt{M}$, where $M$ is the number of simulations (asset paths).
However, the variance of SMC estimator is smaller and does not grow with the
number of time steps when compared to the standard Monte Carlo. In this paper
we demonstrate that SMC can successfully be used for pricing barrier options.
SMC can also be used for pricing other exotic options and also for cases with
many underlying assets and additional stochastic factors such as stochastic
volatility; we provide general formulas and references.
On the stationarity of Dynamic Conditional Correlation models
Date: 2016-03-29 00:00:00

We provide conditions for the existence and the unicity of strictly
stationary solutions of the usual Dynamic Conditional Correlation GARCH models
(DCC-GARCH). The proof is based on Tweedie's (1988) criteria, after having
rewritten DCC-GARCH models as nonlinear Markov chains. Moreover, we study the
existence of their finite moments.
Differential stoichiometry among core ribosomal proteins
Date: 2015-04-15 00:00:00

Understanding the regulation and structure of ribosomes is essential to
understanding protein synthesis and its deregulation in disease. While
ribosomes are believed to have a fixed stoichiometry among their core ribosomal
proteins (RPs), some experiments suggest a more variable composition. Testing
such variability requires direct and precise quantification of RPs. We used
mass-spectrometry to directly quantify RPs across monosomes and polysomes of
mouse embryonic stem cells (ESC) and budding yeast. Our data show that the
stoichiometry among core RPs in wild-type yeast cells and ESC depends both on
the growth conditions and on the number of ribosomes bound per mRNA.
Furthermore, we find that the fitness of cells with a deleted RP-gene is
inversely proportional to the enrichment of the corresponding RP in polysomes.
Together, our findings support the existence of ribosomes with distinct protein
composition and physiological function.
Estimation of the Global Minimum Variance Portfolio in High Dimensions
Date: 2015-11-13 00:00:00
Tasks: valid, Vocal Bursts Intensity Prediction

We estimate the global minimum variance (GMV) portfolio in the
high-dimensional case using results from random matrix theory. This approach
leads to a shrinkage-type estimator which is distribution-free and it is
optimal in the sense of minimizing the out-of-sample variance. Its asymptotic
properties are investigated assuming that the number of assets $p$ depends on
the sample size $n$ such that $\frac{p}{n}\rightarrow c\in (0,+\infty)$ as $n$
tends to infinity. The results are obtained under weak assumptions imposed on
the distribution of the asset returns, namely it is only required the fourth
moments existence. Furthermore, we make no assumption on the upper bound of the
spectrum of the covariance matrix. As a result, the theoretical findings are
also valid if the dependencies between the asset returns are described by a
factor model which appears to be very popular in financial literature nowadays.
This is also well-documented in a numerical study where the small- and
large-sample behavior of the derived estimator are compared with existing
estimators of the GMV portfolio. The resulting estimator shows significant
improvements and it turns out to be robust to the deviations from normality.
Relation between Financial Market Structure and the Real Economy: Comparison between Clustering Methods
Date: 2015-01-21 00:00:00
Tasks: Clustering, Portfolio Optimization, Relation

We quantify the amount of information filtered by different hierarchical
clustering methods on correlations between stock returns comparing it with the
underlying industrial activity structure. Specifically, we apply, for the first
time to financial data, a novel hierarchical clustering approach, the Directed
Bubble Hierarchical Tree and we compare it with other methods including the
Linkage and k-medoids. In particular, by taking the industrial sector
classification of stocks as a benchmark partition, we evaluate how the
different methods retrieve this classification. The results show that the
Directed Bubble Hierarchical Tree can outperform other methods, being able to
retrieve more information with fewer clusters. Moreover, we show that the
economic information is hidden at different levels of the hierarchical
structures depending on the clustering method. The dynamical analysis on a
rolling window also reveals that the different methods show different degrees
of sensitivity to events affecting financial markets, like crises. These
results can be of interest for all the applications of clustering methods to
portfolio optimization and risk hedging.
Notes on Alpha Stream Optimization
Date: 2015-03-19 00:00:00

In these notes we discuss investment allocation to multiple alpha streams
traded on the same execution platform, including when trades are crossed
internally resulting in turnover reduction. We discuss approaches to alpha
weight optimization where one maximizes P&L subject to bounds on volatility (or
Sharpe ratio). The presence of negative alpha weights, which are allowed when
alpha streams are traded on the same execution platform, complicates the
optimization problem. By using factor model approach to alpha covariance
matrix, the original optimization problem can be viewed as a 1-dimensional root
searching problem plus an optimization problem that requires a finite number of
iterations. We discuss this approach without costs and with linear costs, and
also with nonlinear costs in a certain approximation, which makes the
allocation problem tractable without forgoing nonlinear portfolio capacity
bound effects.
Phase transition on the convergence rate of parameter estimation under an Ornstein-Uhlenbeck diffusion on a tree
Date: 2016-05-25 00:00:00
Tasks: parameter estimation

Diffusion processes on trees are commonly used in evolutionary biology to
model the joint distribution of continuous traits, such as body mass, across
species. Estimating the parameters of such processes from tip values presents
challenges because of the intrinsic correlation between the observations
produced by the shared evolutionary history, thus violating the standard
independence assumption of large-sample theory. For instance Ho and An\'e
\cite{HoAne13} recently proved that the mean (also known in this context as
selection optimum) of an Ornstein-Uhlenbeck process on a tree cannot be
estimated consistently from an increasing number of tip observations if the
tree height is bounded. Here, using a fruitful connection to the so-called
reconstruction problem in probability theory, we study the convergence rate of
parameter estimation in the unbounded height case. For the mean of the process,
we provide a necessary and sufficient condition for the consistency of the
maximum likelihood estimator (MLE) and establish a phase transition on its
convergence rate in terms of the growth of the tree. In particular we show that
a loss of $\sqrt{n}$-consistency (i.e., the variance of the MLE becomes
$\Omega(n^{-1})$, where $n$ is the number of tips) occurs when the tree growth
is larger than a threshold related to the phase transition of the
reconstruction problem. For the covariance parameters, we give a novel,
efficient estimation method which achieves $\sqrt{n}$-consistency under natural
assumptions on the tree.
On the statistical machinery of alien species
Date: 2016-07-22 00:00:00

Many species of plants are found in regions to which they are alien and their
global distribution has been found to exhibit several remarkable
patterns,characterised by exponential functions of the kind that could arise
through versions of MacArthur's broken stick. We show here that these various
patterns are all quantitatively reproduced by a simple algorithm, in terms of a
single parameter- a single stick to be broken. This algorithm admits a
biological interpretation in terms of niche structures fluctuating with time
and productivity; with sites and species highly idiosyncratic. Technically,
this is an application of statistical mechanics to ecology quite different from
the familiar application to species abundance distributions.
Protein synthesis driven by dynamical stochastic transcription
Date: 2015-07-23 00:00:00
Tasks: Translation

In this manuscript we propose a mathematical framework to couple
transcription and translation in which mRNA production is described by a set of
master equations while the dynamics of protein density is governed by a random
differential equation. The coupling between the two processes is given by a
stochastic perturbation whose statistics satisfies the master equations. In
this approach, from the knowledge of the analytical time dependent distribution
of mRNA number, we are able to calculate the dynamics of the probability
density of the protein population.
Assessing phenotypic correlation through the multivariate phylogenetic latent liability model
Date: 2015-09-16 00:00:00

Understanding which phenotypic traits are consistently correlated throughout
evolution is a highly pertinent problem in modern evolutionary biology. Here,
we propose a multivariate phylogenetic latent liability model for assessing the
correlation between multiple types of data, while simultaneously controlling
for their unknown shared evolutionary history informed through molecular
sequences. The latent formulation enables us to consider in a single model
combinations of continuous traits, discrete binary traits and discrete traits
with multiple ordered and unordered states. Previous approaches have
entertained a single data type generally along a fixed history, precluding
estimation of correlation between traits and ignoring uncertainty in the
history. We implement our model in a Bayesian phylogenetic framework, and
discuss inference techniques for hypothesis testing. Finally, we showcase the
method through applications to columbine flower morphology, antibiotic
resistance in Salmonella and epitope evolution in influenza.
The limits of statistical significance of Hawkes processes fitted to financial data
Date: 2015-03-22 00:00:00

Many fits of Hawkes processes to financial data look rather good but most of
them are not statistically significant. This raises the question of what part
of market dynamics this model is able to account for exactly. We document the
accuracy of such processes as one varies the time interval of calibration and
compare the performance of various types of kernels made up of sums of
exponentials. Because of their around-the-clock opening times, FX markets are
ideally suited to our aim as they allow us to avoid the complications of the
long daily overnight closures of equity markets. One can achieve statistical
significance according to three simultaneous tests provided that one uses
kernels with two exponentials for fitting an hour at a time, and two or three
exponentials for full days, while longer periods could not be fitted within
statistical satisfaction because of the non-stationarity of the endogenous
process. Fitted timescales are relatively short and endogeneity factor is high
but sub-critical at about 0.8.
Differentiating the L\'evy walk from a composite correlated random walk
Date: 2015-04-27 00:00:00

1. Understanding how to find targets with very limited information is a topic
of interest in many disciplines. In ecology, such research has often focused on
the development of two movement models: i) the L\'evy walk and; ii) the
composite correlated random walk and its associated area-restricted search
behaviour. Although the processes underlying these models differ, they can
produce similar movement patterns. Due to this similarity and because of their
disparate formulation, current methods cannot reliably differentiate between
these two models.
  2. Here, we present a method that differentiates between the two models. It
consists of likelihood functions, including one for a hidden Markov model, and
associated statistical measures that assess the relative support for and
absolute fit of each model.
  3. Using a simulation study, we show that our method can differentiate
between the two search models over a range of parameter values. Using the
movement data of two polar bears (\textit{Ursus maritimus}), we show that the
method can be applied to complex, real-world movement paths.
  4. By providing the means to differentiate between the two most prominent
search models in the literature, and a framework that could be extended to
include other models, we facilitate further research into the strategies
animals use to find resources.
Counterintuitive properties of the fixation time in network-structured populations
Date: 2015-04-22 00:00:00
Tasks: valid

Evolutionary dynamics on graphs can lead to many interesting and
counterintuitive findings. We study the Moran process, a discrete time
birth-death process, that describes the invasion of a mutant type into a
population of wild-type individuals. Remarkably, the fixation probability of a
single mutant is the same on all regular networks. But non-regular networks can
increase or decrease the fixation probability. While the time until fixation
formally depends on the same transition probabilities as the fixation
probabilities, there is no obvious relation between them. For example, an
amplifier of selection, which increases the fixation probability and thus
decreases the number of mutations needed until one of them is successful, can
at the same time slow down the process of fixation. Based on small networks, we
show analytically that (i) the time to fixation can decrease when links are
removed from the network and (ii) the node providing the best starting
conditions in terms of the shortest fixation time depends on the fitness of the
mutant. Our results are obtained analytically on small networks, but numerical
simulations show that they are qualitatively valid even in much larger
populations.
A Set of Discrete Formulae for the Performance of a Tsetse Population During Aerial Spraying
Date: 2015-10-28 00:00:00

A set of discrete formulae that calculates the hypothetical impact of aerial
spraying on a tsetse population is derived and the work is thought to be novel.
Both the original population and the subsequent generations which survive the
aerial spraying, may ultimately be thought of as deriving from two, distinct
sources. These origins are, however, neither distinct, nor relevant by the
third generation. It is for this reason that the female population is
considered to be composed of the following four categories for the purposes of
derivation: Original flies which existed as such at the commencement of
spraying; original pupae which existed as such at the commencement of spraying;
the immediate descendants of both the aforementioned categories, during
spraying; third and higher generation descendants. In theory, the latter
category is a recurrence relation. In practice, the third generation's pupal
stage has hardly come into existence, even by the end of a completed operation.
Implicit in the formulae is the assumption of one, temperature-dependent
mortality rate for the entire pupal stage, a second for the period between
eclosion and ovulation and yet a third for the entire, adult life-span. Gravid
female resistance to the insecticide is assumed to be inconsequential. A
further assumption of the formulae is that at least one male is always
available (degree of sterility variable).
Nonlinear network dynamics under perturbations of the underlying graph
Date: 2015-01-15 00:00:00

Many natural systems are organized as networks, in which the nodes (be they
cells, individuals or populations) interact in a time-dependent fashion. The
dynamic behavior of these networks depends on how these nodes are connected,
which can be understood in terms of an adjacency matrix, and connection
strengths. The object of our study is to relate connectivity to temporal
behavior in networks of coupled nonlinear oscillators. We investigate the
relationship between classes of system architectures and classes of their
possible dynamics, when the nodes are coupled according to a connectivity
scheme that obeys certain constrains, but also incorporates random aspects.
  We illustrate how the phase space dynamics and bifurcations of the system
change when perturbing the underlying adjacency graph. We differentiate between
the effects on dynamics of the following operations that directly modulate
network connectivity: (1) increasing/decreasing edge weights, (2)
increasing/decreasing edge density, (3) altering edge configuration by adding,
deleting or moving edges.
  We discuss the significance of our results in the context of real life
networks. Some interpretations lead us to draw conclusions that may apply to
brain networks, synaptic restructuring and neural dynamics.
Name Disambiguation from link data in a collaboration graph using temporal and topological features
Authors: Zhang Baichuan, Saha Tanay Kumar, Hasan Mohammad Al
Date: 2016-02-18 00:00:00
Tasks: Entity Disambiguation, Retrieval

In a social community, multiple persons may share the same name, phone number
or some other identifying attributes. This, along with other phenomena, such as
name abbreviation, name misspelling, and human error leads to erroneous
aggregation of records of multiple persons under a single reference. Such
mistakes affect the performance of document retrieval, web search, database
integration, and more importantly, improper attribution of credit (or blame).
The task of entity disambiguation partitions the records belonging to multiple
persons with the objective that each decomposed partition is composed of
records of a unique person. Existing solutions to this task use either
biographical attributes, or auxiliary features that are collected from external
sources, such as Wikipedia. However, for many scenarios, such auxiliary
features are not available, or they are costly to obtain. Besides, the attempt
of collecting biographical or external data sustains the risk of privacy
violation. In this work, we propose a method for solving entity disambiguation
task from link information obtained from a collaboration network. Our method is
non-intrusive of privacy as it uses only the time-stamped graph topology of an
anonymized network. Experimental results on two real-life academic
collaboration networks show that the proposed method has satisfactory
performance.
Moral Hazard in Dynamic Risk Management
Date: 2015-03-16 00:00:00
Tasks: Management

We consider a contracting problem in which a principal hires an agent to
manage a risky project. When the agent chooses volatility components of the
output process and the principal observes the output continuously, the
principal can compute the quadratic variation of the output, but not the
individual components. This leads to moral hazard with respect to the risk
choices of the agent. We identify a family of admissible contracts for which
the optimal agent's action is explicitly characterized, and, using the recent
theory of singular changes of measures for It\^o processes, we study how
restrictive this family is. In particular, in the special case of the standard
Homlstr\"om-Milgrom model with fixed volatility, the family includes all
possible contracts. We solve the principal-agent problem in the case of CARA
preferences, and show that the optimal contract is linear in these factors: the
contractible sources of risk, including the output, the quadratic variation of
the output and the cross-variations between the output and the contractible
risk sources. Thus, like sample Sharpe ratios used in practice, path-dependent
contracts naturally arise when there is moral hazard with respect to risk
management. In a numerical example, we show that the loss of efficiency can be
significant if the principal does not use the quadratic variation component of
the optimal contract.
Collective Dynamics of Dividing Chemotactic Cells
Date: 2015-01-16 00:00:00

The large scale behaviour of a population of cells that grow and interact
through the concentration field of the chemicals they secrete is studied using
dynamical renormalization group methods. The combination of the effective
long-range chemotactic interaction and lack of number conservation leads to a
rich variety of phase behaviour in the system, which includes a sharp
transition from a phase that has moderate (or controlled) growth and regulated
chemical interactions to a phase with strong (or uncontrolled) growth and no
chemical interactions. The transition point has nontrivial critical exponents.
Our results might help shed light on the interplay between chemical signalling
and growth in tissues and colonies, and in particular on the challenging
problem of cancer metastasis.
Credit Bubbles in Arbitrage Markets: The Geometric Arbitrage Approach to Credit Risk
Authors: Simone Farinelli, Hideyuki Takada
Date: 2014-06-26 00:00:00

We apply Geometric Arbitrage Theory to obtain results in mathematical finance for credit markets, which do not need stochastic differential geometry in their formulation. We obtain closed form equations involving default intensities and loss given defaults characterizing the no-free-lunch-with-vanishing-risk condition for corporate bonds, as well as the generic dynamics for credit market allowing for arbitrage possibilities. Moreover, arbitrage credit bubbles for both base credit assets and credit derivatives are explicitly computed for the market dynamics minimizing the arbitrage.