pwc-archive/papers-with-abstracts huggingface dataset:
Hyperinflation in Brazil, Israel, and Nicaragua revisited
Date: 2016-04-12 00:00:00
Tasks: Time Series Analysis

The aim of this work is to address the description of hyperinflation regimes
in economy. The spirals of hyperinflation developed in Brazil, Israel, and
Nicaragua are revisited. This new analysis of data indicates that the episodes
occurred in Brazil and Nicaragua can be understood within the frame of the
model available in the literature, which is based on a nonlinear feedback (NLF)
characterized by an exponent $\beta>0$. In the NLF model the accumulated
consumer price index carries a finite time singularity of the type
$1/(t_c-t)^{(1- \beta)/\beta}$ determining a critical time $t_c$ at which the
economy would crash. It is shown that in the case of Brazil the entire episode
cannot be described with a unique set of parameters because the time series was
strongly affected by a change of policy. This fact gives support to the "so
called" Lucas critique, who stated that model's parameters usually change once
policy changes. On the other hand, such a model is not able to provide any
$t_c$ in the case of the weaker hyperinflation occurred in Israel. It is shown
that in this case the fit of data yields $\beta \to 0$. This limit leads to the
linear feedback formulation which does not predict any $t_c$. An extension for
the NLF model is suggested.
Minimax perfect stopping rules for selling an asset near its ultimate maximum
Date: 2016-07-14 00:00:00

We study the problem of selling an asset near its ultimate maximum in the
minimax setting. The regret-based notion of a perfect stopping time is
introduced. A perfect stopping time is uniquely characterized by its optimality
properties and has the following form: one should sell the asset if its price
deviates from the running maximum by a certain time-dependent quantity. The
related selling rule improves any earlier one and cannot be improved by further
delay. The results, which are applicable to a quite general price model, are
illustrated by several examples.
Pairwise Network Information and Nonlinear Correlations
Date: 2016-09-29 00:00:00

Reconstructing the structural connectivity between interacting units from
observed activity is a challenge across many different disciplines. The
fundamental first step is to establish whether or to what extent the
interactions between the units can be considered pairwise and, thus, can be
modeled as an interaction network with simple links corresponding to pairwise
interactions. In principle this can be determined by comparing the maximum
entropy given the bivariate probability distributions to the true joint
entropy. In many practical cases this is not an option since the bivariate
distributions needed may not be reliably estimated, or the optimization is too
computationally expensive. Here we present an approach that allows one to use
mutual informations as a proxy for the bivariate distributions. This has the
advantage of being less computationally expensive and easier to estimate. We
achieve this by introducing a novel entropy maximization scheme that is based
on conditioning on entropies and mutual informations. This renders our approach
typically superior to other methods based on linear approximations. The
advantages of the proposed method are documented using oscillator networks and
a resting-state human brain network as generic relevant examples.
Essay on the State of Research and Innovation in France and the European Union
Date: 2016-01-14 00:00:00

Innovation in the economy is an important engine of growth and no economy,
whatever its complexity and degree of advancement, whether it is based on
industry, agriculture, high tech or the providing of services, can be truly
healthy without innovating actors within it. The aim of this work, done by an
applied mathematician working in finance, not by an economist or a lawyer,
isn't to provide an exhaustive view of the all the mechanisms in France and in
Europe that aim at fostering innovation in the economy and to offer solutions
for removing all the roadblocks that still hinder innovation; indeed such a
study would go far beyond the scope of this study. What I modestly attempted to
achieve in this study was firstly to draw a panorama of what is working and
what needs to perfected as far as innovation is concerned in France and Europe,
then secondly to offer some solutions and personal thoughts to boost
innovation.
Multistage Portfolio Optimization: A Duality Result in Conic Market Models
Date: 2016-01-22 00:00:00
Tasks: Portfolio Optimization

We prove a general duality result for multi-stage portfolio optimization
problems in markets with proportional transaction costs. The financial market
is described by Kabanov's model of foreign exchange markets over a finite
probability space and finite-horizon discrete time steps. This framework allows
us to compare vector-valued portfolios under a partial ordering, so that our
model does not require liquidation into some numeraire at terminal time.
  We embed the vector-valued portfolio problem into the set-optimization
framework, and generate a problem dual to portfolio optimization. Using recent
results in the development of set optimization, we then show that a strong
duality relationship holds between the problems.
Limits to causal inference with state-space reconstruction for infectious disease
Date: 2016-10-29 00:00:00
Tasks: Causal Inference, Sensitivity, Time Series, Time Series Analysis

Infectious diseases are notorious for their complex dynamics, which make it
difficult to fit models to test hypotheses. Methods based on state-space
reconstruction have been proposed to infer causal interactions in noisy,
nonlinear dynamical systems. These "model-free" methods are collectively known
as convergent cross-mapping (CCM). Although CCM has theoretical support,
natural systems routinely violate its assumptions. To identify the practical
limits of causal inference under CCM, we simulated the dynamics of two pathogen
strains with varying interaction strengths. The original method of CCM is
extremely sensitive to periodic fluctuations, inferring interactions between
independent strains that oscillate with similar frequencies. This sensitivity
vanishes with alternative criteria for inferring causality. However, CCM
remains sensitive to high levels of process noise and changes to the
deterministic attractor. This sensitivity is problematic because it remains
challenging to gauge noise and dynamical changes in natural systems, including
the quality of reconstructed attractors that underlie cross-mapping. We
illustrate these challenges by analyzing time series of reportable childhood
infections in New York City and Chicago during the pre-vaccine era. We comment
on the statistical and conceptual challenges that currently limit the use of
state-space reconstruction in causal inference.
Exponential integrability properties of Euler discretization schemes for the Cox-Ingersoll-Ross process
Date: 2015-12-17 00:00:00

We analyze exponential integrability properties of the Cox-Ingersoll-Ross
(CIR) process and its Euler discretizations with various types of truncation
and reflection at 0. These properties play a key role in establishing the
finiteness of moments and the strong convergence of numerical approximations
for a class of stochastic differential equations arising in finance. We prove
that both implicit and explicit Euler-Maruyama discretizations for the CIR
process preserve the exponential integrability of the exact solution for a wide
range of parameters, and find lower bounds on the explosion time.
101 Formulaic Alphas
Date: 2016-03-18 00:00:00

We present explicit formulas - that are also computer code - for 101
real-life quantitative trading alphas. Their average holding period
approximately ranges 0.6-6.4 days. The average pair-wise correlation of these
alphas is low, 15.9%. The returns are strongly correlated with volatility, but
have no significant dependence on turnover, directly confirming an earlier
result based on a more indirect empirical analysis. We further find empirically
that turnover has poor explanatory power for alpha correlations.
A unified view of LIBOR models
Date: 2016-07-10 00:00:00

We provide a unified framework for modeling LIBOR rates using general
semimartingales as driving processes and generic functional forms to describe
the evolution of the dynamics. We derive sufficient conditions for the model to
be arbitrage-free which are easily verifiable, and for the LIBOR rates to be
true martingales under the respective forward measures. We discuss when the
conditions are also necessary and comment on further desirable properties such
as those leading to analytical tractability and positivity of rates. This
framework allows to consider several popular models in the literature, such as
LIBOR market models driven by Brownian motion or jump processes, the L\'evy
forward price model as well as the affine LIBOR model, under one umbrella.
Moreover, we derive structural results about LIBOR models and show, in
particular, that only models where the forward price is an exponentially affine
function of the driving process preserve their structure under different
forward measures.
Fast, invariant representation for human action in the visual system
Date: 2017-08-15 00:00:00
Tasks: Action Recognition, Form

Humans can effortlessly recognize others' actions in the presence of complex
transformations, such as changes in viewpoint. Several studies have located the
regions in the brain involved in invariant action recognition, however, the
underlying neural computations remain poorly understood. We use
magnetoencephalography (MEG) decoding and a dataset of well-controlled,
naturalistic videos of five actions (run, walk, jump, eat, drink) performed by
different actors at different viewpoints to study the computational steps used
to recognize actions across complex transformations. In particular, we ask when
the brain discounts changes in 3D viewpoint relative to when it initially
discriminates between actions. We measure the latency difference between
invariant and non-invariant action decoding when subjects view full videos as
well as form-depleted and motion-depleted stimuli. Our results show no
difference in decoding latency or temporal profile between invariant and
non-invariant action recognition in full videos. However, when either form or
motion information is removed from the stimulus set, we observe a decrease and
delay in invariant action decoding. Our results suggest that the brain
recognizes actions and builds invariance to complex transformations at the same
time, and that both form and motion information are crucial for fast, invariant
action recognition.
Temporal Taylor's scaling of facial electromyography and electrodermal activity in the course of emotional stimulation
Date: 2016-04-24 00:00:00
Tasks: Time Series, Time Series Analysis

High frequency psychophysiological data create a challenge for quantitative
modeling based on Big Data tools since they reflect the complexity of processes
taking place in human body and its responses to external events. Here we
present studies of fluctuations in facial electromyography (fEMG) and
electrodermal activity (EDA) massive time series and changes of such signals in
the course of emotional stimulation. Zygomaticus major (ZYG, "smiling" muscle)
activity, corrugator supercilii (COR, "frowning"bmuscle) activity, and phasic
skin conductance (PHSC, sweating) levels of 65 participants were recorded
during experiments that involved exposure to emotional stimuli (i.e., IAPS
images, reading and writing messages on an artificial online discussion board).
Temporal Taylor's fluctuations scaling were found when signals for various
participants and during various types of emotional events were compared. Values
of scaling exponents were close to 1, suggesting an external origin of system
dynamics and/or strong interactions between system's basic elements (e.g.,
muscle fibres). Our statistical analysis shows that the scaling exponents
enable identification of high valence and arousal levels in ZYG and COR
signals.
Unified Growth Theory Contradicted by the Economic Growth in Latin America
Date: 2016-03-25 00:00:00

Historical economic growth in Latin America is analysed using the data of
Maddison. Unified Growth Theory is found to be contradicted by these data in
the same way as it is contradicted by the economic growth in Africa, Asia,
former USSR, Western Europe, Eastern Europe and by the world economic growth.
Paradoxically, Unified Growth Theory is repeatedly and consistently
contradicted by the same data, which were used, but never properly analysed,
during the formulation of this theory. Unified Growth Theory does not explain
the mechanism of the economic growth because it explains features contradicted
by data. This theory is based fundamentally on the unfortunate lack of
understanding of the properties of hyperbolic distribution and on the
unscientific analysis of data. There was no transition from stagnation to
growth at the end of the alleged Malthusian regime because the economic growth
was hyperbolic. There was no escape from Malthusian trap because there was no
trap. There was no takeoff. On the contrary, at the time of the alleged takeoff
economic growth started to be diverted to a slower trajectory. Unified Growth
Theory is dissociated from the reality. This theory needs to be revised or
replaced. In its present form, it is a collection of irrelevant stories based
on impressions and on the unscientific use of data.
Systemic Risk Management in Financial Networks with Credit Default Swaps
Date: 2017-10-13 00:00:00
Tasks: Management

We study insolvency cascades in an interbank system when banks are allowed to
insure their loans with credit default swaps (CDS) sold by other banks. We show
that, by properly shifting financial exposures from one institution to another,
a CDS market can be designed to rewire the network of interbank exposures in a
way that makes it more resilient to insolvency cascades. A regulator can use
information about the topology of the interbank network to devise a systemic
insurance surcharge that is added to the CDS spread. CDS contracts are thus
effectively penalized according to how much they contribute to increasing
systemic risk. CDS contracts that decrease systemic risk remain untaxed. We
simulate this regulated CDS market using an agent-based model (CRISIS
macro-financial model) and we demonstrate that it leads to an interbank system
that is more resilient to insolvency cascades.
Measuring the sequence-affinity landscape of antibodies with massively parallel titration curves
Date: 2016-11-15 00:00:00

Despite the central role that antibodies play in the adaptive immune system
and in biotechnology, much remains unknown about the quantitative relationship
between an antibody's amino acid sequence and its antigen binding affinity.
Here we describe a new experimental approach, called Tite-Seq, that is capable
of measuring binding titration curves and corresponding affinities for
thousands of variant antibodies in parallel. The measurement of titration
curves eliminates the confounding effects of antibody expression and stability
that arise in standard deep mutational scanning assays. We demonstrate Tite-Seq
on the CDR1H and CDR3H regions of a well-studied scFv antibody. Our data shed
light on the structural basis for antigen binding affinity and suggests a role
for secondary CDR loops in establishing antibody stability. Tite-Seq fills a
large gap in the ability to measure critical aspects of the adaptive immune
system, and can be readily used for studying sequence-affinity landscapes in
other protein systems.
Mimicking Collective Firing Patterns of Hundreds of Connected Neurons using a Single-Neuron Experiment
Date: 2016-01-10 00:00:00

The experimental study of neural networks requires simultaneous measurements
of a massive number of neurons, while monitoring properties of the
connectivity, synaptic strengths and delays. Current technological barriers
make such a mission unachievable. In addition, as a result of the enormous
number of required measurements, the estimated network parameters would differ
from the original ones. Here we present a versatile experimental technique,
which enables the study of recurrent neural networks activity while being
capable of dictating the network connectivity and synaptic strengths. This
method is based on the observation that the response of neurons depends solely
on their recent stimulations, a short-term memory. It allows a long-term scheme
of stimulation and recording of a single neuron, to mimic simultaneous activity
measurements of neurons in a recurrent network. Utilization of this technique
demonstrates the spontaneous emergence of cooperative synchronous oscillations,
in particular the coexistence of fast Gamma and slow Delta oscillations, and
opens the horizon for the experimental study of other cooperative phenomena
within large-scale neural networks.
Effect of Replication Fork Dynamics on Escherichia coli Cell Size
Authors: Chaitanya A. Athale
Date: 2016-01-10 00:00:00

The variability in cell size of an isogenic population of Escherichia coli has been widely reported in experiment. The probability density function (PDF) of cell lengths has been variously described by exponential and lognormal functions. While temperature, population density and growth rate have all been shown to affect E. coli cell size distributions, and recent models have validated a link between growth rate and cell size through DNA replication, cell size variability is thought to emerge from growth rate variability. A mechanistic link that could distinguish the source of stochasticity, could improve our understanding of cell size regulation. Here, we have developed a population dynamics model of individual cell division based on the BCD, birth, chromosome replication and division model, with DNA replication based on the Cooper and Helmstetter (CH) multi-fork replication. In our model, stochasticity in the model arises solely from the dynamics of DNA replication forks. We model the forks as two-state systems: stalled and recovered. Our model predicts an increase in cell size variability with growth rate, consistent with previous experimental reports. We perturb the model to test the effect of increased replication fork (RF) stalling frequency, or uncoupling RF stalling from the cell-division machinery. Indeed, despite ignoring DNA and protein segregation asymmetry, the model can faithfully reproduce quantitative changes in cell size distributions. In our model, multi-fork replication produces multiplicative 'noise' and provides a mechanism linking growth rate and cell size variability.
Negative interest rates: why and how?
Date: 2016-01-10 00:00:00

The interest rates (or nominal yields) can be negative, this is an
unavoidable fact which has already been visible during the Great Depression
(1929-39). Nowadays we can find negative rates easily by e.g. auditing. Several
theoretical and practical ideas how to model and eventually overcome empirical
negative rates can be suggested, however, they are far beyond a simple
practical realization. In this paper we discuss the dynamical reasons why
negative interest rates can happen in the second order differential dynamics
and how they can influence the variance and expectation of the interest rate
process. Such issues are highly practical, involving e.g. banking sector and
pension securities.
The effects of fibroblasts on wave dynamics in a mathematical model for human ventricular tissue
Date: 2016-01-11 00:00:00

We present systematic numerical studies of electrical-wave propagation in
two-dimensional (2D) and three-dimensional (3D) mathematical models, for human,
ventricular tissue with myocyte cells that are attached (a) regularly and (b)
randomly to distributed fibroblasts. In both these cases we show that there is
a parameter regime in which single rotating spiral- and scroll-wave states (RS)
retain their integrity and do not evolve to a state ST that displays
spatiotemporal chaos and turbulence. However, in another range of parameters,
we observe a transition from ST to RS states in both 2D or 3D domains and for
both cases (a) and (b). Our studies show that the ST-RS transition and rotation
period of a spiral or scroll wave in the RS state depends on (i) the coupling
strength between myocytes and fibroblasts and (ii) the number of fibroblasts
attached to myocytes. We conclude that myocyte-fibroblast coupling strength and
the number of fibroblasts are more important for the ST-RS transition than the
precise way in which fibroblasts are distributed over myocyte tissue.
Decomposition of Time Series Data of Stock Markets and its Implications for Prediction: An Application for the Indian Auto Sector
Date: 2016-01-11 00:00:00
Tasks: Time Series, Time Series Analysis

With the rapid development and evolution of sophisticated algorithms for
statistical analysis of time series data, the research community has started
spending considerable effort in technical analysis of such data. Forecasting is
also an area which has witnessed a paradigm shift in its approach. In this
work, we have used the time series of the index values of the Auto sector in
India during January 2010 to December 2015 for a deeper understanding of the
behavior of its three constituent components, e.g., the Trend, the Seasonal
component, and the Random component. Based on this structural analysis, we have
also designed three approaches for forecasting and also computed their accuracy
in prediction using suitably chosen training and test data sets. The results
clearly demonstrate the accuracy of our decomposition results and efficiency of
our forecasting techniques, even in presence of a dominant Random component in
the time series.
Improved Measures of Integrated Information
Date: 2016-11-29 00:00:00

Although there is growing interest in measuring integrated information in
computational and cognitive systems, current methods for doing so in practice
are computationally unfeasible. Existing and novel integration measures are
investigated and classified by various desirable properties. A simple taxonomy
of Phi-measures is presented where they are each characterized by their choice
of factorization method (5 options), choice of probability distributions to
compare (3x4 options) and choice of measure for comparing probability
distributions (7 options). When requiring the Phi-measures to satisfy a minimum
of attractive properties, these hundreds of options reduce to a mere handful,
some of which turn out to be identical. Useful exact and approximate formulas
are derived that can be applied to real-world data from laboratory experiments
without posing unreasonable computational demands.