pwc-archive/papers-with-abstracts huggingface dataset:
Pricing American Call Options by the Black-Scholes Equation with a Nonlinear Volatility Function
Date: 2018-06-13 00:00:00

In this paper we investigate a nonlinear generalization of the Black-Scholes
equation for pricing American style call options in which the volatility term
may depend on the underlying asset price and the Gamma of the option. We
propose a numerical method for pricing American style call options by means of
transformation of the free boundary problem for a nonlinear Black-Scholes
equation into the so-called Gamma variational inequality with the new variable
depending on the Gamma of the option. We apply a modified projective successive
over relaxation method in order to construct an effective numerical scheme for
discretization of the Gamma variational inequality. Finally, we present several
computational examples for the nonlinear Black-Scholes equation for pricing
American style call option under presence of variable transaction costs.
An Investigation into Laboucheres Betting System to Improve Odds of Favorable Outcomes to Generate a Positive Externality Empirically
Date: 2017-06-29 00:00:00

The Labouchere gambling system is hypothesized to increase the probability of
winning a predetermined arbitrary profit in a gambling system such as a coin
flip or a roulette game in which both payouts and odds are 1:1. However, use of
the system increases the downside monetary risk in the event of a streak of
multiple losses. To begin, a player creates an arbitrary series of consecutive
integers with a sum equal to the desired profit from multiple rounds of
betting. Using the system, a player will either win an amount equal to the sum
of the elements of the initial series or lose all of their available capital.
This sequence was simulated multiple times to determine the statistical
characteristics of both the return and of the loss in an average round of
betting. By running the simulations of millions of rounds of Labouchere, it was
possible to discern the probable outcomes of running the system using the
Labouchere gambling sequence and plotting the results on a graph to map the
average return on the initial capital investment. The Labouchere system is very
psychologically appealing to players because when applied over time it provides
very consistent linear returns. However, there is eventually a critical moment
at which the available capital for betting is exceeded and a player loses all
of their available capital. It was found that as the number of bets increased,
the outcome of applying the sequence approached zero.
Option Pricing under Fast-varying and Rough Stochastic Volatility
Date: 2018-04-16 00:00:00
Tasks: Time Series, Time Series Analysis, valid

Recent empirical studies suggest that the volatilities associated with
financial time series exhibit short-range correlations. This entails that the
volatility process is very rough and its autocorrelation exhibits sharp decay
at the origin. Another classic stylistic feature often assumed for the
volatility is that it is mean reverting. In this paper it is shown that the
price impact of a rapidly mean reverting rough volatility model coincides with
that associated with fast mean reverting Markov stochastic volatility models.
This reconciles the empirical observation of rough volatility paths with the
good fit of the implied volatility surface to models of fast mean reverting
Markov volatilities. Moreover, the result conforms with recent numerical
results regarding rough stochastic volatility models. It extends the scope of
models for which the asymptotic results of fast mean reverting Markov
volatilities are valid. The paper concludes with a general discussion of
fractional volatility asymptotics and their interrelation. The regimes
discussed there include fast and slow volatility factors with strong or small
volatility fluctuations and with the limits not commuting in general. The
notion of a characteristic term structure exponent is introduced, this exponent
governs the implied volatility term structure in the various asymptotic
regimes.
The Strong Cell-based Hydrogen Peroxide Generation Triggered by Cold Atmospheric Plasma
Date: 2017-05-28 00:00:00

Hydrogen peroxide (H2O2) is an important signaling molecule in cancer cells.
However, the significant secretion of H2O2 by cancer cells have been rarely
observed. Cold atmospheric plasma (CAP) is a near room temperature ionized gas
composed of neutral particles, charged particles, reactive species, and
electrons. Here, we first demonstrated that breast cancer cells and pancreatic
adenocarcinoma cells generated micromolar level H2O2 during just 1 min of
direct CAP treatment on these cells. The cell-based H2O2 generation is affected
by the medium volume, the cell confluence, as well as the discharge voltage.
The application of cold atmospheric plasma (CAP) in the cancer treatment has
been intensively investigated over the past decade. Several cellular responses
to the CAP treatment have been observed including the consumption of the
CAP-originated reactive species, the rise of intracellular reactive oxygen
species, the damage on DNA and mitochondria, as well as the activation of
apoptotic events. This is a new previously unknown cellular response to CAP,
which provides a new prospective to understand the interaction between CAP and
cells.
The Role of Money in the Business Cycle
Date: 2020-04-26 00:00:00

The aim of this paper is to reemphasize the money theory of exchange which is
centered on the function of exchange medium of money, and make a contribution
towards linearization of the quantity equation of exchange. A dynamical
quantity equation is presented and an important balanced path of economic
evolution is derived. To understand the business cycle we propose a hypothesis
of natural cycle and driving cycle concerning the evolution of the balanced
path and plentiful conclusions can be made.
Predicting therapeutic and aggravating drugs for hepatocellular carcinoma based on tissue-specific pathways
Date: 2017-06-20 00:00:00

Motivation: Hepatocellular carcinoma (HCC) is a significant health problem
worldwide and annual number of cases are nearly more than 700,000.
However,there are few safe and effective thera-peutic options for HCC
patients.Here, we propose a new approach for predicting therapeutic and
aggravating drugs for HCCbased ontissue-specificpathways, which considersnot
onlyliver tis-sueand functional informationof pathways, but also the changes of
single gene in pathways. Results: Firstly, we map genes related to HCC to the
liver-specific protein interaction network and get anextended tissue-specific
gene set of HCC. Then, based on the extended gene set, 12 en-riched
KEGGfunctional pathways are extracted.Using Kolmogorov-Smirnov statistic, we
calculate the therapeutic scores of drugs based on the 12 tissue-specific
pathways. Finally, after filtering by Comparative Toxicogenomics Database (CTD)
benchmark,we get 3 therapeutic drugs and 3 ag-gravating drugs for HCC.
Furthermore, we validate the6potentially related drugs of HCC by
analyz-ingtheiroverlaps with drug indications reported in PubMed literatures,
and also making CMapprofile similarity analysis and KEGG enrichment analysis
based on their targets. All analysis results sug-gest thatour approach is
effective and accurate for discovering novel therapeutic options for HCC and it
can be easily extended to other diseases.More importantly, our method can
clearly distin-guish therapeutic and aggravating drugsforHCC, which also can be
used to indicateunmarked drug-disease associations in CTD as positive or
negative.
Buy-and-Hold Property for Fully Incomplete Markets when Super-replicating Markovian Claims
Date: 2018-10-15 00:00:00

We show that when the price process $S$ represents a fully incomplete market,
the optimal super-replication of any Markovian claim $g(S_T)$ with $g(\cdot)$
being nonnegative and lower semicontinuous is of buy-and-hold type. Since both
(unbounded) stochastic volatility models and rough volatility models are
examples of fully incomplete markets, one can interpret the buy-and-hold
property when super-replicating Markovian claims as a natural phenomenon in
incomplete markets.
You are in a drawdown. When should you start worrying?
Date: 2017-07-21 00:00:00

Trading strategies that were profitable in the past often degrade with time.
Since unlucky streaks can also hit "healthy" strategies, how can one detect
that something truly worrying is happening? It is intuitive that a drawdown
that lasts too long or one that is too deep should lead to a downward revision
of the assumed Sharpe ratio of the strategy. In this note, we give a
quantitative answer to this question based on the exact probability
distributions for the length and depth of the last drawdown for upward drifting
Brownian motions. We also point out that both managers and investors tend to
underestimate the length and depth of drawdowns consistent with the Sharpe
ratio of the underlying strategy.
The quest for identifiability in human functional connectomes
Date: 2018-04-12 00:00:00
Tasks: Functional Connectivity

The evaluation of the individual 'fingerprint' of a human functional
connectome (FC) is becoming a promising avenue for neuroscientific research,
due to its enormous potential inherent to drawing single subject inferences
from functional connectivity profiles. Here we show that the individual
fingerprint of a human functional connectome can be maximized from a
reconstruction procedure based on group-wise decomposition in a finite number
of brain connectivity modes. We use data from the Human Connectome Project to
demonstrate that the optimal reconstruction of the individual FCs through
connectivity eigenmodes maximizes subject identifiability across resting-state
and all seven tasks evaluated. The identifiability of the optimally
reconstructed individual connectivity profiles increases both at the global and
edgewise level, also when the reconstruction is imposed on additional
functional data of the subjects. Furthermore, reconstructed FC data provide
more robust associations with task-behavioral measurements. Finally, we extend
this approach to also map the most task-sensitive functional connections.
Results show that is possible to maximize individual fingerprinting in the
functional connectivity domain regardless of the task, a crucial next step in
the area of brain connectivity towards individualized connectomics.
Analysis of Footnote Chasing and Citation Searching in an Academic Search Engine
Authors: Kacem Ameni, Mayr Philipp
Date: 2017-09-23 00:00:00
Tasks: Information Retrieval, Retrieval

In interactive information retrieval, researchers consider the user behavior
towards systems and search tasks in order to adapt search results by analyzing
their past interactions. In this paper, we analyze the user behavior towards
Marcia Bates' search stratagems such as 'footnote chasing' and 'citation
search' in an academic search engine. We performed a preliminary analysis of
their frequency and stage of use in the social sciences search engine sowiport.
In addition, we explored the impact of these stratagems on the whole search
process performance. We can conclude that the appearance of these two search
features in real retrieval sessions lead to an improvement of the precision in
terms of positive interactions with 16% when using footnote chasing and 17% for
the citation search stratagem.
Locating large flexible ligands on proteins
Date: 2017-07-11 00:00:00

Many biologically important ligands of proteins are large, flexible, and
often charged molecules that bind to extended regions on the protein surface.
It is infeasible or expensive to locate such ligands on proteins with standard
methods such as docking or molecular dynamics (MD) simulation. The alternative
approach proposed here is the scanning of a spatial and angular grid around the
protein with smaller fragments of the large ligand. Energy values for complete
grids can be computed efficiently with a well-known Fast Fourier Transform
accelerated algorithm and a physically meaningful interaction model. We show
that the approach can readily incorporate flexibility of protein and ligand.
The energy grids (EGs) resulting from the ligand fragment scans can be
transformed into probability distributions, and then directly compared to
probability distributions estimated from MD simulations and experimental
structural data. We test the approach on a diverse set of complexes between
proteins and large, flexible ligands, including a complex of Sonic Hedgehog
protein and heparin, three heparin sulfate substrates or non-substrates of an
epimerase, a multi-branched supramolecular ligand that stabilizes a
protein-peptide complex, and a flexible zwitterionic ligand that binds to a
surface basin of a Kringle domain. In all cases the EG approach gives results
that are in good agreement with experimental data or MD simulations.
Exploring the underlying mechanisms of Xenopus laevis embryonic cell cycle
Date: 2017-06-30 00:00:00
Tasks: Drug Discovery, Nutrition

Cell cycle is an indispensable process in the proliferation and development.
Despite significant efforts, global quantification and physical understanding
are still challenging. In this study, we explored the mechanisms of Xenopus
laevis embryonic cell cycle by quantifying the underlying landscape and flux.
We uncovered the irregular Mexican hat landscape of the Xenopus laevis
embryonic cell cycle with several local basins and barriers on the oscillation
path. The local basins characterize the different phases of Xenopus laevis
embryonic cell cycle and the local barriers represent the checkpoints. The
checkpoint mechanism of cell cycle is revealed by the landscape basins and
barriers. While landscape shape determines the stabilities of the states on the
oscillation path, the curl flux force determines the stability of the cell
cycle flow. Replication is fundamental for biology of living. From our
quantitative study here, we see that replication can not proceed without energy
input. In fact, the curl flux originated from energy or nutrition supply
determines the speed of the cell cycle and guarantees the progression. Speed of
cell cycle is a hallmark of cancer. Through landscape and flux analysis, one
can identify the key elements for controlling the speed. This can help to
design effective strategy for drug discovery against cancer.
A second-order orientation-contrast stimulus for population-receptive-field-based retinotopic mapping
Date: 2017-07-10 00:00:00
Tasks: Optical Character Recognition (OCR)

Visual field or retinotopic mapping is one of the most frequently used
paradigms in fMRI. It uses activity evoked by position-varying high luminance
contrast visual patterns presented throughout the visual field for determining
the spatial organization of cortical visual areas. While the advantage of using
high luminance contrast is that it tends to drive a wide range of neural
populations - thus resulting in high signal-to-noise BOLD responses - this may
also be a limitation, especially for approaches that attempt to squeeze more
information out of the BOLD response, such as population receptive field (pRF)
mapping. In that case, more selective stimulation of a subset of neurons -
despite reduced signals - could result in better characterization of pRF
properties. Here, we used a second-order stimulus based on local differences in
orientation texture - to which we refer as orientation contrast - to perform
retinotopic mapping. Participants in our experiment viewed arrays of Gabor
patches composed of a foreground (a bar) and a background. These could only be
distinguished on the basis of a difference in patch orientation. In our
analyses, we compare the pRF properties obtained using this new orientation
contrast-based retinotopy (OCR) to those obtained using classic luminance
contrast-based retinotopy (LCR). Specifically, in higher order cortical visual
areas such as LO, our novel approach resulted in non-trivial reductions in
estimated population receptive field size of around 30%. We discuss how OCR -
by limiting receptive field scatter and reducing BOLD displacement - may result
in more accurate pRF localization as well. We conclude that using our approach,
it is possible to selectively target particular neuronal populations, opening
the way to use pRF modeling to dissect the response properties of more
clearly-defined neuronal populations in different visual areas.
Document Retrieval for Large Scale Content Analysis using Contextualized Dictionaries
Authors: Wiedemann Gregor, Niekler Andreas
Date: 2017-07-11 00:00:00
Tasks: Information Retrieval, Retrieval, Term Extraction, Topic Models

This paper presents a procedure to retrieve subsets of relevant documents
from large text collections for Content Analysis, e.g. in social sciences.
Document retrieval for this purpose needs to take account of the fact that
analysts often cannot describe their research objective with a small set of key
terms, especially when dealing with theoretical or rather abstract research
interests. Instead, it is much easier to define a set of paradigmatic documents
which reflect topics of interest as well as targeted manner of speech. Thus, in
contrast to classic information retrieval tasks we employ manually compiled
collections of reference documents to compose large queries of several hundred
key terms, called dictionaries. We extract dictionaries via Topic Models and
also use co-occurrence data from reference collections. Evaluations show that
the procedure improves retrieval results for this purpose compared to
alternative methods of key term extraction as well as neglecting co-occurrence
data.
Equivalence relations on ecosystems
Date: 2017-07-11 00:00:00

In abstract terms, ecosystem ecology is about determining when two
ecosystems, superficially different, are alike in some deeper way. An external
observer can choose any ecosystem property as being important. In contrast, two
ecosystems are equivalent from the point of view of the organisms they contain
if and only if for each species, the proportional population growth rate does
not differ between the ecosystems. Comparative studies of ecosystems should
therefore focus on patterns in proportional population growth rates, rather
than patterns in other properties such as relative abundances. Popular
activities such as measuring dissimilarity, and representing dissimilarity via
ordination, can then be done from the point of view of the organisms in
ecosystems. Summarizing the state of an ecosystem under this approach remains
challenging. In general, the dynamics on equivalence classes of ecosystems
defined in this way are structurally different from the dynamics of ecosystems
as seen by an external observer. This may limit the extent to which natural
selection can act on ecosystem structure.
Structural changes in barley protein LTP1 isoforms at air-water interfaces
Date: 2017-07-11 00:00:00
Tasks: Specificity

We use a coarse-grained model to study the conformational changes in two
barley proteins, LTP1 and its ligand adduct isoform LTP1b, that result from
their adsorption to the air-water interface. The model introduces the interface
through hydropathy indices. We justify the model by all-atom simulations. The
choice of the proteins is motivated by making attempts to understand formation
and stability of foam in beer. We demonstrate that both proteins flatten out at
the interface and can make a continuous stabilizing and denser film. We show
that the degree of the flattening depends on the protein -- the layers of LTP1b
should be denser than those of LTP1 -- and on the presence of glycation. It
also depends on the number ($\le 4$) of the disulfide bonds in the proteins.
The geometry of the proteins is sensitive to the specificity of the absent
bonds. We provide estimates of the volume of cavities of the proteins when away
from the interface.
Viability and Arbitrage under Knightian Uncertainty
Date: 2020-05-13 00:00:00

We reconsider the microeconomic foundations of financial economics. Motivated
by the importance of Knightian Uncertainty in markets, we present a model that
does not carry any probabilistic structure ex ante, yet is based on a common
order. We derive the fundamental equivalence of economic viability of asset
prices and absence of arbitrage. We also obtain a modified version of the
Fundamental Theorem of Asset Pricing using the notion of sublinear pricing
measures. Different versions of the Efficient Market Hypothesis are related to
the assumptions one is willing to impose on the common order.
Wextractor: Follow-up of the evolution of prices in web pages
Authors: Lloret-Gazo Jorge
Date: 2017-07-11 00:00:00

In the e-commerce world, the follow-up of prices in detail web pages is of
great interest for things like buying a product when it falls below some
threshold. For doing this task, instead of bookmarking the pages and revisiting
them, in this paper we propose a novel web data extraction system, called
Wextractor. It consists of an extraction method and a web app for listing the
retrieved prices. As for the final user, the main feature of Wextractor is
usability because (s)he only has to signal the pages of interest and our system
automatically extracts the price from the page.
The partial damage loss cover ratemaking of the automobile insurance using generalized linear models
Date: 2017-07-12 00:00:00

It is illustrated a methodology to compute the pure premium for the
automobile insurance (claim frequency and severity) using generalized linear
models. It is obtained the pure premium for the partial damage loss cover (PPD)
using a set of automobile insurance policies with an exposition of a year. It
is found that the most influential variables in the claim frequency are the car
production year, the insured's age, and the region's subscription policy and
the most influential variables in the claim severity are the car's value, type
and make and the insured's gender.
Scientific Table Search Using Keyword Queries
Authors: Gao Kyle Yingkai, Callan Jamie
Date: 2017-07-11 00:00:00
Tasks: Table Search

Tables are common and important in scientific documents, yet most text-based
document search systems do not capture structures and semantics specific to
tables. How to bridge different types of mismatch between keywords queries and
scientific tables and what influences ranking quality needs to be carefully
investigated. This paper considers the structure of tables and gives different
emphasis to table components. On the query side, thanks to external knowledge
such as knowledge bases and ontologies, key concepts are extracted and used to
build structured queries, and target quantity types are identified and used to
expand original queries. A probabilistic framework is proposed to incorporate
structural and semantic information from both query and table sides. We also
construct and release TableArXiv, a high quality dataset with 105 queries and
corresponding relevance judgements for scientific table search. Experiments
demonstrate significantly higher accuracy overall and at the top of the
rankings than several baseline methods.
Portfolio Risk Assessment using Copula Models
Date: 2017-07-12 00:00:00
Tasks: Time Series, Time Series Analysis

In the paper, we use and investigate copulas models to represent multivariate
dependence in financial time series. We propose the algorithm of risk measure
computation using copula models. Using the optimal mean-$CVaR$ portfolio we
compute portfolio's Profit and Loss series and corresponded risk measures
curves. Value-at-risk and Conditional-Value-at-risk curves were simulated by
three copula models: full Gaussian, Student's $t$ and regular vine copula.
These risk curves are lower than historical values of the risk measures curve.
All three models have superior prediction ability than a usual empirical
method. Further directions of research are described.