pwc-archive/papers-with-abstracts huggingface dataset:
Hedging with physical or cash settlement under transient multiplicative price impact
Authors: Dirk Becherer, Todor Bilarev
Date: 2018-07-16 00:00:00

We solve the superhedging problem for European options in an illiquid extension of the Black-Scholes model, in which transactions have transient price impact and the costs and the strategies for hedging are affected by physical or cash settlement requirements at maturity. Our analysis is based on a convenient choice of reduced effective coordinates of magnitudes at liquidation for geometric dynamic programming. The price impact is transient over time and multiplicative, ensuring non-negativity of underlying asset prices while maintaining an arbitrage-free model. The basic (log-)linear example is a Black-Scholes model with relative price impact being proportional to the volume of shares traded, where the transience for impact on log-prices is being modelled like in Obizhaeva-Wang \cite{ObizhaevaWang13} for nominal prices. More generally, we allow for non-linear price impact and resilience functions. The viscosity solutions describing the minimal superhedging price are governed by the transient character of the price impact and by the physical or cash settlement specifications. Pricing equations under illiquidity extend no-arbitrage pricing a la Black-Scholes for complete markets in a non-paradoxical way (cf.\ {\c{C}}etin, Soner and Touzi \cite{CetinSonerTouzi10}) even without additional frictions, and can recover it in base cases.
Poster Abstract: Hierarchical Subchannel Allocation for Mode-3 Vehicle-to-Vehicle Sidelink Communications
Date: 2018-07-12 00:00:00
Tasks: Graph Matching

In V2V Mode-3, eNodeBs assign subchannels to vehicles in order for them to
periodically broadcast CAM messages \cite{b2}. A crucial aspect is to ensure
that vehicles in the same cluster will broadcast in orthogonal time
subchannels\footnote{A subchannel is a time-frequency resource chunk capable of
sufficiently conveying a CAM message.} to avoid conflicts. In general,
resource/subchannel allocation problems can be represented as weighted
bipartite graphs. However, in this scenario there is an additional time
orthogonality constraint which cannot be straightforwardly handled by
conventional graph matching methods \cite{b3}. Thus, in our approach the
mentioned constraint has been taken into account. We also perform the
allocation task in a sequential manner based on the constrainedness of each
cluster. To illustrate the gist of the problem, in Fig. 1 we show two partially
overlapping clusters where a conflict between vehicles $V_8$ and $V_{10}$ is
generated as the allotted subchannels are in the same subframe.
Teaching Telecommunication Standards - Bridging the Gap Between Theory and Practice
Date: 2018-07-12 00:00:00

Telecommunication standards have become a reliable mechanism to strengthen
collaboration between industry and research institutions to accelerate the
evolution of communications systems. Standards are needed to enable cooperation
while promoting competition. Within the framework of a standard, the companies
involved in the standardization process contribute and agree on appropriate
technical specifications to ensure diversity, compatibility and facilitate
worldwide commercial deployment and evolution. Those parts of the system that
can create competitive advantages are intentionally left open in the
specifications. Such specifications are extensive, complex and minimalistic.
This makes the telecommunication standards education a difficult endeavor, but
it is much demanded by industry and governments to spur economic growth. This
paper describes a methodology for teaching wireless communications standards.
We define our methodology around six learning stages that assimilate the
standardization process and identify key learning objectives for each. Enabled
by software-defined radio technology we describe a practical learning
environment that facilitates developing many of the needed technical and soft
skills without the inherent difficulty and cost associated with radio frequency
components and regulation. Using only open-source software and commercial
off-the-shelf computers, this environment is portable and can easily be
recreated at other educational institutions and adapted to their educational
needs and constraints. We discuss our and our students' experiences when
employing the proposed methodology to 4th generation (4G) long-term-evolution
(LTE) standard education at Barcelona Tech.
Faster than Nyquist Transmission by Non-Orthogonal Time Division Multiplexing of Nyquist Sinc Sequences
Date: 2018-07-12 00:00:00

One possibility to break down the capacity limit in optical transmission
systems are higher spectral efficiencies, enabled by Faster-than-Nyquist
signaling. Here we present the utilization of non-orthogonal time division
multiplexing of sinc pulse sequences for this purpose. The mathematical
expression, with a representation of the Nyquist sinc sequence by a cosine
Fourier series and simulation results show that non-orthogonal time-division
multiplexing increases the transmittable symbol rate by up to 25%.
Wireless Energy Transmission Channel Modeling in Resonant Beam Charging for IoT Devices
Date: 2018-07-12 00:00:00

Power supply for Internet of Things (IoT) devices is one of the bottlenecks
in IoT development. To provide perpetual power supply to IoT devices, resonant
beam charging (RBC) is a promising safe, long-range and high-power wireless
power transfer solution. How long distance can RBC reach and how much power can
RBC transfer? In this paper, we analyze the RBC's consistent and steady
operational conditions, which determine the maximum power transmission
distance. Moreover, we study the power transmission efficiency within the
operational distance, which determines the deliverable power through the RBC
energy transmission channel. Based on this energy transmission channel
modeling, we numerically evaluate its impacts on the RBC system performance in
terms of the transmission distance, the transmission efficiency, and the output
electrical power. The analysis leads to the guidelines for the RBC system
design and implementation, which can deliver multi-Watt power over multi-meter
distance wirelessly for IoT devices.
Analysis and Modeling of Behavioral Changes in a News Service
Authors: Sonoda Atom, Toriumi Fujio, Nakajima Hiroto, Gouji Miyabi
Date: 2018-07-16 00:00:00
Tasks: Clustering, Collaborative Filtering, Diversity, Recommendation Systems

Information is transmitted through websites, and immediate reactions to
various kinds of information are required. Hence, efforts by users to select
information themselves have increased, which is fueling further improvements in
recommendation services that can reduce such burdens. On the other hand, filter
bubbles that only provide biased information to users are generated due to
redundant recommendations. In this research, we analyzed behavioral changes
prior to recommendation by clustering, and we found that user attributes and
cluster contents are different among users with different behavioral changes.
The proportion of users under forty and women was relatively large in the
diversity-increasing group. We also proposed an article selection model to
clarify the influence of recommendation systems on behavioral changes. We
compared our proposed model with the target data, verified it, and evaluated
the effect of recommendation systems on user behavior. Our simulation results
showed that diversity usually decreases, but collaborative filtering can
suppress the diversity decrease more effectively than non-recommendations. We
also found that the category that users are interested in the most is easily
strengthened and is one factor that leads to less diversity, and a
recommendation method that can suppress the strengthening of the category that
users are interested in the most will be effective for developing a
recommendation system that can suppress diversity decreasing.
Entropy and Energy Detection-based Spectrum Sensing over F Composite Fading Channels
Date: 2018-07-16 00:00:00
Tasks: Diversity

In this paper, we investigate the performance of energy detection-based
spectrum sensing over F composite fading channels. To this end, an analytical
expression for the average detection probability is firstly derived. This
expression is then extended to account for collaborative spectrum sensing,
square-law selection diversity reception and noise power uncertainty. The
corresponding receiver operating characteristics (ROC) are analyzed for
different conditions of the average signal-to-noise ratio (SNR), noise power
uncertainty, time-bandwidth product, multipath fading, shadowing, number of
diversity branches and number of collaborating users. It is shown that the
energy detection performance is sensitive to the severity of the multipath
fading and amount of shadowing, whereby even small variations in either of
these physical phenomena can significantly impact the detection probability. As
a figure of merit to evaluate the detection performance, the area under the ROC
curve (AUC) is derived and evaluated for different multipath fading and
shadowing conditions. Closed-form expressions for the Shannon entropy and cross
entropy are also formulated and assessed for different average SNR, multipath
fading and shadowing conditions. Then the relationship between the Shannon
entropy and ROC/AUC is examined where it is found that the average number of
bits required for encoding a signal becomes small (i.e., low Shannon entropy)
when the detection probability is high or when the AUC is large. The difference
between composite and traditional small-scale fading is emphasized by comparing
the cross entropy for Rayleigh and Nakagami-m fading. A validation of the
analytical results is provided through a careful comparison with the results of
some simulations.
Quickest Detection of Dynamic Events in Networks
Date: 2018-07-16 00:00:00

The problem of quickest detection of dynamic events in networks is studied.
At some unknown time, an event occurs, and a number of nodes in the network are
affected by the event, in that they undergo a change in the statistics of their
observations. It is assumed that the event is dynamic, in that it can propagate
along the edges in the network, and affect more and more nodes with time. The
event propagation dynamics is assumed to be unknown. The goal is to design a
sequential algorithm that can detect a "significant" event, i.e., when the
event has affected no fewer than $\eta$ nodes, as quickly as possible, while
controlling the false alarm rate. Fully connected networks are studied first,
and the results are then extended to arbitrarily connected networks. The
designed algorithms are shown to be adaptive to the unknown propagation
dynamics, and their first-order asymptotic optimality is demonstrated as the
false alarm rate goes to zero. The algorithms can be implemented with linear
computational complexity in the network size at each time step, which is
critical for online implementation. Numerical simulations are provided to
validate the theoretical results.
Leakage Mitigation and Internal Delay Compensation in FMCW Radar for Small Drone Detection
Date: 2018-07-17 00:00:00

One of the notorious problems of frequency modulated continuous-wave (FMCW)
radar is leakage between the transmitter and the receiver. The phase noise of
the leakage is expressed as a skirt around the leakage signal on power
spectrum. It causes the deterioration of the dynamic range, especially, in the
near-distance region. Therefore, although FMCW radar has an advantage over
pulse radar in terms of near-distance target detection due to its way of
operation, the advantage of FMCW radar can be lost because of the leakage.
Another problem of FMCW radar is internal delay in the radar system. It leads
to the decrease of the maximum detectable range. In this paper, a novel
down-conversion technique which resolves these problems is proposed. Detailed
theory and procedures to implement the proposed technique are explained. Then,
performances of it are verified with the experiment results. The proposed
technique can be implemented through frequency planning and digital signal
processing without additional parts. The results show that the proposed
technique lowers the noise floor about 7.0 dB in the near-distance region and
2.1 dB even in the far-distance region. Also, the results demonstrate the
proposed technique recover the reduced maximum detectable range by compensating
the internal delay.
Limit Theorems for Factor Models
Date: 2020-09-23 00:00:00
Tasks: Time Series, Time Series Analysis, valid

The paper establishes the central limit theorems and proposes how to perform
valid inference in factor models. We consider a setting where many
counties/regions/assets are observed for many time periods, and when estimation
of a global parameter includes aggregation of a cross-section of heterogeneous
micro-parameters estimated separately for each entity. The central limit
theorem applies for quantities involving both cross-sectional and time series
aggregation, as well as for quadratic forms in time-aggregated errors. The
paper studies the conditions when one can consistently estimate the asymptotic
variance, and proposes a bootstrap scheme for cases when one cannot. A small
simulation study illustrates performance of the asymptotic and bootstrap
procedures. The results are useful for making inferences in two-step estimation
procedures related to factor models, as well as in other related contexts. Our
treatment avoids structural modeling of cross-sectional dependence but imposes
time-series independence.
User Fairness in Recommender Systems
Authors: Leonhardt Jurek, Anand Avishek, Khosla Megha
Date: 2018-07-17 00:00:00
Tasks: Diversity, Fairness, Recommendation Systems

Recent works in recommendation systems have focused on diversity in
recommendations as an important aspect of recommendation quality. In this work
we argue that the post-processing algorithms aimed at only improving diversity
among recommendations lead to discrimination among the users. We introduce the
notion of user fairness which has been overlooked in literature so far and
propose measures to quantify it. Our experiments on two diversification
algorithms show that an increase in aggregate diversity results in increased
disparity among the users.
To Post or Not to Post: Using Online Trends to Predict Popularity of Offline Content
Authors: Abbar Sofiane, Castillo Carlos, Sanfilippo Antonio
Date: 2018-07-17 00:00:00
Tasks: Articles, Time Series, Time Series Analysis, Time Series Forecasting

Predicting the popularity of online content has attracted much attention in
the past few years. In news rooms, for instance, journalists and editors are
keen to know, as soon as possible, the articles that will bring the most
traffic into their website. The relevant literature includes a number of
approaches and algorithms to perform this forecasting. Most of the proposed
methods require monitoring the popularity of content during some time after it
is posted, before making any longer-term prediction. In this paper, we propose
a new approach for predicting the popularity of news articles before they go
online. Our approach complements existing content-based methods, and is based
on a number of observations regarding article similarity and topicality. First,
the popularity of a new article is correlated with the popularity of similar
articles of recent publication. Second, the popularity of the new article is
related to the recent historical popularity of its main topic. Based on these
observations, we use time series forecasting to predict the number of visits an
article will receive. Our experiments, conducted on a real data collection of
articles in an international news website, demonstrate the effectiveness and
efficiency of the proposed method.
Dynamic reshaping of functional brain networks during visual object recognition
Date: 2018-07-31 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Object Recognition

Emerging evidence shows that the modular organization of the human brain
allows for better and efficient cognitive performance. Many of these cognitive
functions are very fast and occur in subsecond time scale such as the visual
object recognition. Here, we investigate brain network modularity while
controlling stimuli meaningfulness and measuring participant reaction time. We
particularly raised two questions: i) does the dynamic brain network modularity
change during the recognition of meaningful and meaningless visual images? And
ii) is there a correlation between network modularity and the reaction time of
participants? To tackle these issues, we collected dense electroencephalography
(EEG, 256 channels) data from 20 healthy human subjects performing a cognitive
task consisting of naming meaningful (tools, animals) and meaningless
(scrambled) images. Functional brain networks in both categories were estimated
at subsecond time scale using the EEG source connectivity method. By using
multislice modularity algorithms, we tracked the reconfiguration of functional
networks during the recognition of both meaningful and meaningless images.
Results showed a difference in the module characteristics of both conditions in
term of integration (interactions between modules) and occurrence (probability
on average of any two brain regions to fall in the same module during the
task). Integration and occurrence were greater for meaningless than for
meaningful images. Our findings revealed also that the occurrence within the
right frontal regions and the left occipito-temporal can help to predict the
ability of the brain to rapidly recognize and name visual stimuli. We speculate
that these observations are applicable not only to other fast cognitive
functions but also to detect fast disconnections that can occur in some brain
disorders.
Log-optimal portfolio without NFLVR: existence, complete characterization, and duality
Date: 2018-07-14 00:00:00

This paper addresses the log-optimal portfolio for a general semimartingale
model. The most advanced literature on the topic elaborates existence and
characterization of this portfolio under no-free-lunch-with-vanishing-risk
assumption (NFLVR). There are many financial models violating NFLVR, while
admitting the log-optimal portfolio on the one hand. On the other hand, for
financial markets under progressively enlargement of filtration, NFLVR remains
completely an open issue, and hence the literature can be applied to these
models. Herein, we provide a complete characterization of log-optimal portfolio
and its associated optimal deflator, necessary and sufficient conditions for
their existence, and we elaborate their duality as well without NFLVR.
Centralized Scheduling Strategies for Cooperative HARQ Retransmissions in Multi-Source Multi-Relay Wireless Networks
Date: 2018-07-17 00:00:00
Tasks: Fairness, Scheduling, valid

In this paper, we investigate centralized scheduling strategies for
cooperative incremental redundancy retransmissions in the slow-fading
half-duplex multiple access multiple relay channel. Time Division Multiple
Access is assumed for the sources and the relays. Sources transmit successively
in time slots for the first phase. The second phase consists of a limited
number of time slots for retransmissions. In each time slot, the destination
schedules a node (being a relay or a source) to retransmit, conditional on the
knowledge of the correctly decoded source sets of each node (which is itself
for a source). A scheduled relay retransmission uses Joint Network and Channel
Coding on its correctly decoded source messages (cooperative retransmission).
Several node selection strategies are proposed based on the maximization of the
long-term aggregate throughput under a given constraint of fairness.
Monte-Carlo simulations show that these strategies outperform the state of the
art one based on the minimization of the probability of the common outage event
after each time-slot. Moreover, the long-term aggregate throughput reached with
these strategies is close to the upper-bound, calculated by the exhaustive
search approach. The same conclusion remains valid for both symmetric and
asymmetric source rate scenarios.
A Generalized Forced Oscillation Method for tuning Proportional Resonant Controllers
Date: 2018-07-17 00:00:00
Tasks: Philosophy

This paper proposes a tuning methodology for proportional resonant (PR)
controllers by using the design philosophy of the Ziegler-Nichols forced
oscillation method. Unlike such related methods that are usual for PID design,
and those that have been recently proposed for PR controllers, the method in
this paper is not restricted to plants whose Nyquist diagram crosses the
negative real axis. It involves a feedback experiment with a relay of
adjustable phase, which allows the identification of the most appropriate point
of the frequency response for each class of plants. The validation of the
proposed method, which includes the identification experiment and specific
tuning formulas that provide appropriate stability margins for each class of
plants, is performed by means of numerical examples in a large variety of
plants, showing its wide applicability.
Analysis of Advisor Portfolio using Multivariate Time Series and Cosine Similarity
Date: 2018-07-13 00:00:00
Tasks: Time Series, Time Series Analysis

In mutual fund, an investment adviser gives advice to clients about investing
in securities such as stocks, bonds, mutual funds, or exchange traded funds.
Some investment advisers manage portfolios of securities. In this paper, we
analyze advisor portfolio for each advisor so as to recognize the pattern in
each adviser's portfolio. Such analysis helps the sales people to sell the fund
company products to the suitable advisors desirable to the nature of the
product they want to sell. This is done by analyzing the kind of products
advisors have been interested in which will help to boost the sales of the
products as sales people will be reaching the appropriate advisors.
Deep Learning-Based BSDE Solver for Libor Market Model with Application to Bermudan Swaption Pricing and Hedging
Date: 2018-09-22 00:00:00

The Libor market model is a mainstay term structure model of interest rates
for derivatives pricing, especially for Bermudan swaptions, and other exotic
Libor callable derivatives. For numerical implementation the pricing of
derivatives with Libor market models is mainly carried out with Monte Carlo
simulation. The PDE grid approach is not particularly feasible due to Curse of
Dimensionality. The standard Monte Carlo method for American/Bermudan swaption
pricing more or less uses regression to estimate expected value as a linear
combination of basis functions (Longstaff and Schwartz). However, Monte Carlo
method only provides the lower bound for American option price. Another
complexity is the computation of the sensitivities of the option, the so-called
Greeks, which are fundamental for a trader's hedging activity. Recently, an
alternative numerical method based on deep learning and backward stochastic
differential equations appeared in quite a few researches. For European style
options the feedforward deep neural networks (DNN) show not only feasibility
but also efficiency to obtain both prices and numerical Greeks. In this paper,
a new backward DNN solver is proposed for Bermudan swaptions. Our approach is
representing financial pricing problems in the form of high dimensional
stochastic optimal control problems, FBSDEs, or equivalent PDEs. We demonstrate
that using backward DNN the high-dimension Bermudan swaption pricing and
hedging can be solved effectively and efficiently. A comparison between Monte
Carlo simulation and the new method for pricing vanilla interest rate options
manifests the superior performance of the new method. We then use this method
to calculate prices and Greeks of Bermudan swaptions as a prelude for other
Libor callable derivatives.