pwc-archive/papers-with-abstracts huggingface dataset:
From Glosten-Milgrom to the whole limit order book and applications to financial regulation
Authors: Weibing Huang, Sergio Pulido, Mathieu Rosenbaum, Pamela Saliba, Emmanouil Sfendourakis
Date: 2019-02-27 00:00:00
Tasks: Position

We build an agent-based model for the order book with three types of market participants: informed trader, noise trader and competitive market makers. Using a Glosten-Milgrom like approach, we are able to deduce the whole limit order book (bid-ask spread and volume available at each price) from the interactions between the different agents. More precisely, we obtain a link between efficient price dynamic, proportion of trades due to the noise trader, traded volume, bid-ask spread and equilibrium limit order book state. With this model, we provide a relevant tool for regulators and market platforms. We show for example that it allows us to forecast consequences of a tick size change on the microstructure of an asset. It also enables us to value quantitatively the queue position of a limit order in the book.
Three Different Ways Synchronization Can Cause Contagion in Financial Markets
Date: 2019-02-27 00:00:00
Tasks: Decision Making

We introduce tools to capture the dynamics of three different pathways, in
which the synchronization of human decision-making could lead to turbulent
periods and contagion phenomena in financial markets. The first pathway is
caused when stock market indices, seen as a set of coupled integrate-and-fire
oscillators, synchronize in frequency. The integrate-and-fire dynamics happens
due to change blindness, a trait in human decision-making where people have the
tendency to ignore small changes, but take action when a large change happens.
The second pathway happens due to feedback mechanisms between market
performance and the use of certain (decoupled) trading strategies. The third
pathway occurs through the effects of communication and its impact on human
decision-making. A model is introduced in which financial market performance
has an impact on decision-making through communication between people.
Conversely, the sentiment created via communication has an impact on financial
market performance. The methodologies used are: agent based modeling, models of
integrate-and-fire oscillators, and communication models of human
decision-making
Low-Resolution Quantization in Phase Modulated Systems: Optimum Detectors and Error Rate Analysis
Date: 2019-02-28 00:00:00
Tasks: Quantization

This paper considers a wireless communication system with low-resolution
quantizers, in which transmitted signals are corrupted by fading and additive
noise. For such wireless systems, a universal lower bound on the average symbol
error probability (SEP), correct for all M-ary modulation schemes, is obtained
when the number of quantization bits is not enough to resolve M signal points.
In the special case of M-ary phase shift keying (M-PSK), the optimum maximum
likelihood detector for equi-probable signal points is derived. Utilizing the
structure of the derived optimum receiver, a general average SEP expression for
M-PSK modulation with n-bit quantization is obtained when the wireless channel
is subject to fading with a circularly-symmetric distribution. Adopting this
result for Nakagami-m fading channels, easy-to-evaluate expressions for the
average SEP for M-PSK modulation are further derived. It is shown that a
transceiver architecture with n-bit quantization is asymptotically optimum in
terms of communication reliability if n is greater than or equal to log_2(M
+1). That is, the decay exponent for the average SEP is the same and equal to m
with infinite-bit and n-bit quantizers for n is greater than or equal to
log_2(M+1). On the other hand, it is only equal to half and 0 for n = log_2(M)
and n < log_2(M), respectively. An extensive simulation study is performed to
illustrate the derived results and energy efficiency gains obtained by means of
low-resolution quantizers.
Trade-offs between carbon stocks and biodiversity in European temperate forests
Date: 2019-02-28 00:00:00
Tasks: Management

Policies to mitigate climate change and biodiversity loss often assume that
protecting carbon-rich forests provides co-benefits in terms of biodiversity,
due to the spatial congruence of carbon stocks and biodiversity at
biogeographic scales. However, it remains unclear whether this holds at the
scales relevant for management, with particularly large knowledge gaps for
temperate forests and for taxa other than trees. We built a comprehensive
dataset of Central European temperate forest structure and multi-taxonomic
diversity (beetles, birds, bryophytes, fungi, lichens, and plants) across 352
plots. We used Boosted Regression Trees to assess the relationship between
above-ground live carbon stocks and (a) taxon-specific richness, (b) a unified
multidiversity index. We used Threshold Indicator Taxa ANalysis to explore
individual species' responses to changing above-ground carbon stocks and to
detect change-points in species composition along the carbon-stock gradient.
Our results reveal an overall weak and highly variable relationship between
richness and carbon stock at the stand scale, both for individual taxonomic
groups and for multidiversity. Similarly, the proportion of win-win and
trade-off species (i.e. species favored or disadvantaged by increasing carbon
stock, respectively) varied substantially across taxa. Win-win species
gradually replaced trade-off species with increasing carbon, without clear
thresholds along the above-ground carbon gradient, suggesting that
community-level surrogates (e.g. richness) might fail to detect critical
changes in biodiversity. Collectively, our analyses highlight that leveraging
co-benefits between carbon and biodiversity in temperate forest may require
stand-scale management that prioritizes either biodiversity or carbon-in order
to maximize co-benefits at broader scales. Importantly, this contrasts with
tropical forests, where climate [...]
Beltanelliformis brunsae Menner in Keller et al., 1974: an undoubted Ediacaran fossil from Neoproterozoic of Dobrogea (Romania)
Date: 2019-02-28 00:00:00

Ediacaran fossils are now largely known in different parts of the world.
However, some countries are poorly documented on these remains of a still
enigmatic life. Thus, rare fossils from the Neoproterozoic Histria Formation of
central Dobrogea (Romania) have been reported. Two specimens with discoid
imprints are described here in detail and assigned to the typical Ediacaran
species Beltanelliformis brunsae Menner in Keller et al., 1974. This
paleontological development confirms both the large geographical distribution
of this species and the Ediacaran age of the Histria Formation. KEY WORDS Flat
discoid imprints, Ediacaran biota, Precambrian, Dobrogea, Romania.
R{\'E}SUM{\'E}
Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure
Date: 2019-02-28 00:00:00
Tasks: Variable Selection

We develop an LM test for Granger causality in high-dimensional VAR models based on penalized least squares estimations. To obtain a test retaining the appropriate size after the variable selection done by the lasso, we propose a post-double-selection procedure to partial out effects of nuisance variables and establish its uniform asymptotic validity. We conduct an extensive set of Monte-Carlo simulations that show our tests perform well under different data generating processes, even without sparsity. We apply our testing procedure to find networks of volatility spillovers and we find evidence that causal relationships become clearer in high-dimensional compared to standard low-dimensional VARs.
External Constraint Handling for Solving Optimal Control Problems with Simultaneous Approaches and Interior Point Methods
Date: 2019-06-12 00:00:00

Inactive constraints do not contribute to the solution of an optimal control
problem, but increase the problem size and burden the numerical computations.
We present a novel strategy for handling inactive constraints efficiently by
systematically removing the inactive and redundant constraints. The method is
designed to be used together with simultaneous approaches under a mesh
refinement framework, with mild assumptions that the original problem has
feasible solutions, and the initial solve of the problem is successful. The
method is tailored for interior point-based solvers, which are known to be very
sensitive to the choice of initial points in terms of feasibility. In the
example problem shown, the proposed scheme achieves more than a 40% reduction
in computation time.
Decawave UWB clock drift correction and powerself-calibration
Date: 2019-02-28 00:00:00
Tasks: Position

The position accuracy based on Decawave Ultra-Wideband (UWB) is
affectedmainly by three factors: hardware delays, clock drift, and signal
power. Thisarticle discusses the last two factors. The general approach to
clock drift correc-tion uses the phase-locked loop (PLL) integrator, which we
show is subject tosignal power variations, and therefore, is less suitable for
clock drift correction.The general approach to the estimation of signal power
correction curves re-quires additional measurement equipment. This article
presents a new methodfor obtaining the curve without additional hardware and
clock drift correctionwithout the PLL integrator. Both correction methods were
fused together toimprove two-way ranging (TWR).
Real-Time detection, classification and DOA estimation of Unmanned Aerial Vehicle
Date: 2019-02-27 00:00:00
Tasks: Quantization

The present work deals with a new passive system for real-time detection,
classification and direction of arrival estimator of Unmanned Aerial Vehicles
(UAVs). The proposed system composed of a very low cost hardware components,
comprises two different arrays of three or six-microphones, non-linear
amplification and filtering of the analog acoustic signal, avoiding also the
saturation effect in case where the UAV is located nearby to the microphones.
Advance array processing methods are used to detect and locate the wide-band
sources in the near and far-field including array calibration and energy based
beamforming techniques. Moreover, oversampling techniques are adopted to
increase the acquired signals accuracy and to also decrease the quantization
noise. The classifier is based on the nearest neighbor rule of a normalized
Power Spectral Density, the acoustic signature of the UAV spectrum in short
periods of time. The low-cost, low-power and high efficiency embedded processor
STM32F405RG is used for system implementation. Preliminary experimental results
have shown the effectiveness of the proposed approach.
A numerical scheme for the quantile hedging problem
Date: 2019-02-28 00:00:00

We consider the numerical approximation of the quantile hedging price in a
non-linear market. In a Markovian framework, we propose a numerical method
based on a Piecewise Constant Policy Timestepping (PCPT) scheme coupled with a
monotone finite difference approximation. We prove the convergence of our
algorithm combining BSDE arguments with the Barles & Jakobsen and Barles &
Souganidis approaches for non-linear equations. In a numerical section, we
illustrate the efficiency of our scheme by considering a financial example in a
market with imperfections.
Improving LoRa Signal Coverage in Urban and Sub-Urban Environments with UAVs
Date: 2019-02-20 00:00:00

LoRa technology enables long-range communication with low-power consumption
for the Internet-of-Things (IoT) devices in the urban and suburban environment.
However, due to terrestrial structures in urban and suburban environments, the
link distance of LoRa transmissions can be reduced. In this paper, we report
signal strength measurements for the in-building and inter-building LoRa links
and provide insights on factors that affect signal quality such as the
spreading factor and antenna orientation. Subsequently, we also provide
measurement results in urban and suburban environments when the LoRa
transmitter is deployed at different heights using an unmanned aerial vehicle
(UAV). Our findings show that the UAV deployment height is critical for
improving coverage in the suburban environment and antenna orientation affects
the communication range.
L\'evy like patterns in the small-scale movements of marsupials in an unfamiliar and risky environment
Date: 2019-02-28 00:00:00

We investigate the movement patterns of three different Neotropical
marsupials in an unfamiliar and risky environment. Animals are released in a
matrix from which they try to reach a patch of forest. Their movements,
performed on a small spacial scale, are best approximated by L\'evy flights.
Patterns of oriented and non-oriented individuals - with forest patches within
or beyond their perceptual range - differ only slightly in the value of their
exponents. These facts suggest that, for these species, the appearance of
L\'evy flights is the product of animals innate behaviour that emerges
spontaneously, as a neutral characteristic proper of a default movement mode
for alerted animals.
Gaussian Process Regression for Pricing Variable Annuities with Stochastic Volatility and Interest Rate
Date: 2019-07-22 00:00:00
Tasks: GPR, regression

In this paper we investigate price and Greeks computation of a Guaranteed
Minimum Withdrawal Benefit (GMWB) Variable Annuity (VA) when both stochastic
volatility and stochastic interest rate are considered together in the Heston
Hull-White model. We consider a numerical method the solves the dynamic control
problem due to the computing of the optimal withdrawal. Moreover, in order to
speed up the computation, we employ Gaussian Process Regression (GPR). Starting
from observed prices previously computed for some known combinations of model
parameters, it is possible to approximate the whole price function on a defined
domain. The regression algorithm consists of algorithm training and evaluation.
The first step is the most time demanding, but it needs to be performed only
once, while the latter is very fast and it requires to be performed only when
predicting the target function. The developed method, as well as for the
calculation of prices and Greeks, can also be employed to compute the
no-arbitrage fee, which is a common practice in the Variable Annuities sector.
Numerical experiments show that the accuracy of the values estimated by GPR is
high with very low computational cost. Finally, we stress out that the analysis
is carried out for a GMWB annuity but it could be generalized to other
insurance products.
Secrecy Performance of Antenna-Selection-Aided MIMOME Channels with BPSK/QPSK Modulations
Date: 2019-04-27 00:00:00
Tasks: Diversity

This paper studies the secrecy performance of multiple-input multiple-output
(MIMO) wiretap channels, also termed as multiple-input multiple-output
multiple-eavesdropper (MIMOME) channels, under transmit antenna selection (TAS)
and BPSK/QPSK modulations. In the main channel between the transmitter and the
legitimate receiver, a single transmit antenna is selected to maximizes the
instantaneous Signal to Noise Ratio (SNR) at the receiver. At the receiver and
the eavesdropper, selection combination (SC) is utilized. By assuming Rayleigh
flat fading, we first derive the closed-form approximated expression for the
ergodic secrecy rate when the channel state information of the eavesdropper
(CSIE) is available at the transmitter. Next, analytical formulas for the
approximated and asymptotic secrecy outage probability (SOP) are also developed
when CSIE is unavailable. Besides theoretical derivations, simulation results
are provided to demonstrate the approximation precision of the derived results.
Furthermore, the asymptotic results reveal that the secrecy diversity order
degrades into 0 due to the finitealphabet inputs, which is totally different
from that driven by the Gaussian inputs.
Solving Power System Differential Algebraic Equations Using Differential Transformation
Date: 2019-11-18 00:00:00

This paper proposes a novel non-iterative method to solve power system
differential algebraic equations (DAEs) using the differential transformation,
a mathematical tool that can obtain power series coefficients by transformation
rules instead of calculating high order derivatives and has proved to be
effective in solving state variables of nonlinear differential equations in our
previous study. This paper further solves non-state variables, e.g. current
injections and bus voltages, directly with a realistic DAE model of power
grids. These non-state variables, nonlinearly coupled in network equations, are
conventionally solved by numerical methods with time-consuming iterations, but
their differential transformations are proved to satisfy formally linear
equations in this paper. Thus, a non-iterative algorithm is designed to
analytically solve all variables of a power system DAE model with ZIP loads.
From test results on a Polish 2383-bus system, the proposed method demonstrates
fast and reliable time performance compared to traditional numerical methods.
Deep Inverse Tone Mapping Using LDR Based Learning for Estimating HDR Images with Absolute Luminance
Date: 2019-02-28 00:00:00
Tasks: inverse tone mapping, Inverse-Tone-Mapping, MS-SSIM, SSIM, Tone Mapping

In this paper, a novel inverse tone mapping method using a convolutional
neural network (CNN) with LDR based learning is proposed. In conventional
inverse tone mapping with CNNs, generated HDR images cannot have absolute
luminance, although relative luminance can. Moreover, loss functions suitable
for learning HDR images are problematic, so it is difficult to train CNNs by
directly using HDR images. In contrast, the proposed method enables us not only
to estimate absolute luminance, but also to train a CNN by using LDR images.
The CNN used in the proposed method learns a transformation from various input
LDR images to LDR images mapped by Reinhard's global operator. Experimental
results show that HDR images generated by the proposed method have
higher-quality than HDR ones generated by conventional inverse tone mapping
methods,in terms of HDR-VDP-2.2 and PU encoding + MS-SSIM.
Algorithms for Piecewise Constant Signal Approximations
Date: 2019-06-11 00:00:00

We consider the problem of finding optimal piecewise constant approximations
of one-dimensional signals. These approximations should consist of a specified
number of segments (samples) and minimise the mean squared error to the
original signal. We formalise this goal as a discrete nonconvex optimisation
problem, for which we study two algorithms. First we reformulate a recent
adaptive sampling method by Dar and Bruckstein in a compact and transparent
way. This allows us to analyse its limitations when it comes to violations of
its three key assumptions: signal smoothness, local linearity, and error
balancing. As a remedy, we propose a direct optimisation approach which does
not rely on any of these assumptions and employs a particle swarm optimisation
algorithm. Our experiments show that for nonsmooth signals or low sample
numbers, the direct optimisation approach offers substantial qualitative
advantages over the Dar--Bruckstein method. As a more general contribution, we
disprove the optimality of the principle of error balancing for optimising data
in the l^2 norm.
Dual-polarization NFDM transmission with continuous and discrete spectral modulation
Date: 2019-02-26 00:00:00

Nonlinear distortion experienced by signals during their propagation through
optical fibers strongly limits the throughput of optical communication systems.
Recently, a strong research focus has been dedicated to nonlinearity mitigation
and compensation techniques. At the same time, a more disruptive approach, the
nonlinear Fourier transform (NFT), aims at designing signaling schemes more
suited to the nonlinear fiber channel. In a short period, impressive results
have been reported by modulating either the continuous spectrum or the discrete
spectrum. Additionally, very recent works further introduced the opportunity to
modulate both spectra for single polarization transmission. Here, we extend the
joint modulation scheme to dual-polarization transmission by introducing the
framework to construct a dual-polarization optical signal with the desired
continuous and discrete spectra. After a brief analysis of the numerical
algorithms used to implement the proposed scheme, the first experimental
demonstration of dual-polarization joint nonlinear frequency division
multiplexing (NFDM) modulation is reported for up to 3200 km of low-loss
transmission fiber. The proposed dual-polarization joint modulation schemes
enables to exploit all the degrees of freedom for modulation (both
polarizations and both spectra) provided by a single-mode fiber (SMF).
A Subjective-Logic-based Reliability Estimation Mechanism for Cooperative Information with Application to IV's Safety
Date: 2019-05-22 00:00:00

Use of cooperative information, distributed by road-side units, offers large
potential for intelligent vehicles (IVs). As vehicle automation progresses and
cooperative perception is used to fill the blind spots of onboard sensors, the
question of reliability of the data becomes increasingly important in safety
considerations (SOTIF, Safety of the Intended Functionality).
  This paper addresses the problem to estimate the reliability of cooperative
information for in-vehicle use. We propose a novel method to infer the
reliability of received data based on the theory of Subjective Logic (SL).
Using SL, we fuse multiple information sources, which individually only provide
mild cues of the reliability, into a holistic estimate, which is statistically
sound through an end-to-end modeling within the theory of SL.
  Using the proposed scheme for probabilistic SL-based fusion, IVs are able to
separate faulty from correct data samples with a large margin of safety. Real
world experiments show the applicability and effectiveness of our approach.
When do common time series estimands have nonparametric causal meaning?
Authors: Ashesh Rambachan, Neil Shephard
Date: 2019-03-05 00:00:00
Tasks: Time Series

In this paper, we introduce the direct potential outcome system as a framework for analyzing dynamic causal effects of assignments on outcomes in observational time series settings. We provide conditions under which common predictive time series estimands, such as the impulse response function, generalized impulse response function, local projection, and local projection instrumental variables, have a nonparametric causal interpretation in terms of dynamic causal effects. The direct potential outcome system therefore provides a foundation for analyzing popular reduced-form methods for estimating the causal effect of macroeconomic shocks on outcomes in time series settings.
Scaling Features of Price-Volume Cross-Correlation
Authors: Jamshid Ardalankia, Mohammad Osoolian, Emmanuel Haven, G. Reza Jafari
Date: 2019-03-05 00:00:00

Price without transaction makes no sense. Trading volume authenticates its corresponding price, so there exist mutual information and correlation between price and trading volume. We are curious about fractal features of this correlation and need to know how structures in different scales translate information. To explore the influence of investment size (trading volume), price-wise (gain/loss), and time-scale effects, we analyzed the price and trading volume and their coupling by applying the MF-DXA method. Our results imply that price, trading volume, and price-volume coupling exhibit a power law and are also multifractal. Meanwhile, considering developed markets, the price-volume couplings are significantly negatively correlated. However, in emerging markets, the price has less of a contribution to price-volume coupling. In emerging markets in comparison with the developed markets, trading volume and price are more independent.