pwc-archive/papers-with-abstracts huggingface dataset:
Testing the Number of Regimes in Markov Regime Switching Models
Date: 2018-01-30 00:00:00

Markov regime switching models have been used in numerous empirical studies
in economics and finance. However, the asymptotic distribution of the
likelihood ratio test statistic for testing the number of regimes in Markov
regime switching models has been an unresolved problem. This paper derives the
asymptotic distribution of the likelihood ratio test statistic for testing the
null hypothesis of $M_0$ regimes against the alternative hypothesis of $M_0 +
1$ regimes for any $M_0 \geq 1$ both under the null hypothesis and under local
alternatives. We show that the contiguous alternatives converge to the null
hypothesis at a rate of $n^{-1/8}$ in regime switching models with normal
density. The asymptotic validity of the parametric bootstrap is also
established.
Ranking Causal Influence of Financial Markets via Directed Information Graphs
Date: 2018-01-21 00:00:00

A non-parametric method for ranking stock indices according to their mutual
causal influences is presented. Under the assumption that indices reflect the
underlying economy of a country, such a ranking indicates which countries exert
the most economic influence in an examined subset of the global economy. The
proposed method represents the indices as nodes in a directed graph, where the
edges' weights are estimates of the pair-wise causal influences, quantified
using the directed information functional. This method facilitates using a
relatively small number of samples from each index. The indices are then ranked
according to their net-flow in the estimated graph (sum of the incoming weights
subtracted from the sum of outgoing weights). Daily and minute-by-minute data
from nine indices (three from Asia, three from Europe and three from the US)
were analyzed. The analysis of daily data indicates that the US indices are the
most influential, which is consistent with intuition that the indices
representing larger economies usually exert more influence. Yet, it is also
shown that an index representing a small economy can strongly influence an
index representing a large economy if the smaller economy is indicative of a
larger phenomenon. Finally, it is shown that while inter-region interactions
can be captured using daily data, intra-region interactions require more
frequent samples.
An Efficient Density-based Clustering Algorithm for Higher-Dimensional Data
Authors: Boonchoo Thapana, Ao Xiang, He Qing
Date: 2018-01-22 00:00:00
Tasks: Clustering

DBSCAN is a typically used clustering algorithm due to its clustering ability
for arbitrarily-shaped clusters and its robustness to outliers. Generally, the
complexity of DBSCAN is O(n^2) in the worst case, and it practically becomes
more severe in higher dimension. Grid-based DBSCAN is one of the recent
improved algorithms aiming at facilitating efficiency. However, the performance
of grid-based DBSCAN still suffers from two problems: neighbour explosion and
redundancies in merging, which make the algorithms infeasible in
high-dimensional space. In this paper, we propose a novel algorithm named GDPAM
attempting to extend Grid-based DBSCAN to higher data dimension. In GDPAM, a
bitmap indexing is utilized to manage non-empty grids so that the neighbour
grid queries can be performed efficiently. Furthermore, we adopt an efficient
union-find algorithm to maintain the clustering information in order to reduce
redundancies in the merging. The experimental results on both real-world and
synthetic datasets demonstrate that the proposed algorithm outperforms the
state-of-the-art exact/approximate DBSCAN and suggests a good scalability.
Accurate Evaluation of Asset Pricing Under Uncertainty and Ambiguity of Information
Date: 2018-03-26 00:00:00
Tasks: Bayesian Inference

Since exchange economy considerably varies in the market assets, asset prices
have become an attractive research area for investigating and modeling
ambiguous and uncertain information in today markets. This paper proposes a new
generative uncertainty mechanism based on the Bayesian Inference and
Correntropy (BIC) technique for accurately evaluating asset pricing in markets.
This technique examines the potential processes of risk, ambiguity, and
variations of market information in a controllable manner. We apply the new BIC
technique to a consumption asset-pricing model in which the consumption
variations are modeled using the Bayesian network model with observing the
dynamics of asset pricing phenomena in the data. These dynamics include the
procyclical deviations of price, the countercyclical deviations of equity
premia and equity volatility, the leverage impact and the mean reversion of
excess returns. The key findings reveal that the precise modeling of asset
information can estimate price changes in the market effectively.
A Novel Contourlet Domain Watermark Detector for Copyright Protection
Date: 2018-01-22 00:00:00

Digital media can be distributed via Internet easily, so, media owners are
eagerly seeking methods to protect their rights. A typical solution is digital
watermarking for copyright protection. In this paper, we propose a novel
contourlet domain image watermarking scheme for copyright protection. In the
embedding phase, we insert the watermark into the image using an additive
contourlet domain spread spectrum approach. In the detection phase, we design a
detector using likelihood ratio test (LRT). Since the performance of the LRT
detector is completely dependent on the accuracy of the employed statistical
model, we first study the statistical properties of the contourlet
coefficients. This study demonstrates the heteroscedasticity and heavy-tailed
marginal distribution of these coefficients. Therefore, we propose using two
dimensional generalized autoregressive conditional heteroscedasticity
(2D-GARCH) model that is compatible with the contourlet coefficients. Motivated
by the modeling results, we design a new watermark detector based on 2D-GARCH
model. Also, we analyze its performance by computing the receiver operating
characteristics. Experimental results confirm the high efficiency of the
proposed detector. Since a watermark detector for copyright protection should
be robust against attacks, we examine the robustness of the proposed detector
under different kinds of attacks.
Quantization Under the Real-world Measure: Fast and Accurate Valuation of Long-dated Contracts
Date: 2018-01-24 00:00:00
Tasks: Quantization

This paper provides a methodology for fast and accurate pricing of the
long-dated contracts that arise as the building blocks of insurance and pension
fund agreements. It applies the recursive marginal quantization (RMQ) and joint
recursive marginal quantization (JRMQ) algorithms outside the framework of
traditional risk-neutral methods by pricing options under the real-world
probability measure, using the benchmark approach. The benchmark approach is
reviewed, and the real-world pricing theorem is presented and applied to
various long-dated claims to obtain less expensive prices than suggested by
traditional risk-neutral valuation. The growth-optimal portfolio (GOP), the
central object of the benchmark approach, is modelled using the time-dependent
constant elasticity of variance model (TCEV). Analytic European option prices
are derived and the RMQ algorithm is used to efficiently and accurately price
Bermudan options on the GOP. The TCEV model is then combined with a $3/2$
stochastic short-rate model and RMQ is used to price zero-coupon bonds and
zero-coupon bond options, highlighting the departure from risk-neutral pricing.
Wireless Network Coding in Network MIMO: A New Design for 5G and Beyond
Date: 2018-05-21 00:00:00

Physical layer network coding (PNC) has been studied to serve wireless
network MIMO systems with much lower backhaul load than approaches such as
Cloud Radio Access Network (Cloud-RAN) and coordinated multipoint (CoMP). In
this paper, we present a design guideline of engineering applicable PNC to
fulfil the request of high user densities in 5G wireless RAN infrastructure.
Unlike compute-and-forward and PNC design criteria for two-way relay channels,
the proposed guideline is designed for uplink of network MIMO (N-MIMO) systems.
We show that the proposed design criteria guarantee that 1) the whole system
operates over binary system; 2) the PNC functions utilised at each access point
overcome all singular fade states; 3) the destination can unambiguously recover
all source messages while the overall backhaul load remains at the lowest
level. We then develop a two-stage search algorithm to identify the optimum PNC
mapping functions which greatly reduces the real-time computational complexity.
The impact of estimated channel information and reduced number of singular fade
states in different QAM modulation schemes is studied in this paper. In
addition, a sub-optimal search method based on lookup table mechanism to
achieve further reduced computational complexity with limited performance loss
is presented. Numerical results show that the proposed schemes achieve low
outage probability with reduced backhaul load.
Optimized Cell Planning for Network Slicing in Heterogeneous Wireless Communication Networks
Date: 2018-05-30 00:00:00

We propose a cell planning scheme to maximize the resource efficiency of a
wireless communication network while considering quality-of-service
requirements imposed by different mobile services. In dense and heterogeneous
cellular 5G networks, the available time-frequency resources are orthogonally
partitioned among different slices, which are serviced by the cells. The
proposed scheme achieves a joint optimization of the resource distribution
between network slices, the allocation of cells to operate on different slices,
and the allocation of users to cells. Since the original problem formulation is
computationally intractable, we propose a convex inner approximation.
Simulations show that the proposed approach optimizes the resource efficiency
and enables a service-centric network design paradigm.
Sex-biased dispersal: a review of the theory
Date: 2018-11-27 00:00:00

Dispersal is ubiquitous throughout the tree of life: factors selecting for
dispersal include kin competition, inbreeding avoidance and spatiotemporal
variation in resources or habitat suitability. These factors differ in whether
they promote male and female dispersal equally strongly, and often selection on
dispersal of one sex depends on how much the other disperses. For example, for
inbreeding avoidance it can be sufficient that one sex disperses away from the
natal site. Attempts to understand sex-specific dispersal evolution have
created a rich body of theoretical literature, which we review here. We
highlight an interesting gap between empirical and theoretical literature. The
former associates different patterns of sex-biased dispersal with mating
systems, such as female-biased dispersal in monogamous birds and male-biased
dispersal in polygynous mammals. The predominant explanation is traceable back
to Greenwood's (1980) ideas of how successful philopatric or dispersing
individuals are at gaining mates or resources required to attract them. Theory,
however, has developed surprisingly independently of these ideas: predominant
ideas in theoretical work track how immigration and emigration change
relatedness patterns and alleviate competition for limiting resources,
typically considered sexually distinct, with breeding sites and fertilisable
females limiting reproductive success for females and males, respectively. We
show that the link between mating system and sex-biased dispersal is far from
resolved: there are studies showing that mating systems matter, but the
oft-stated association between polygyny and male-biased dispersal is not a
straightforward theoretical expectation... (full abstract in the PDF)
Distributed Adaptive Learning with Multiple Kernels in Diffusion Networks
Date: 2018-07-25 00:00:00

We propose an adaptive scheme for distributed learning of nonlinear functions
by a network of nodes. The proposed algorithm consists of a local adaptation
stage utilizing multiple kernels with projections onto hyperslabs and a
diffusion stage to achieve consensus on the estimates over the whole network.
Multiple kernels are incorporated to enhance the approximation of functions
with several high and low frequency components common in practical scenarios.
We provide a thorough convergence analysis of the proposed scheme based on the
metric of the Cartesian product of multiple reproducing kernel Hilbert spaces.
To this end, we introduce a modified consensus matrix considering this specific
metric and prove its equivalence to the ordinary consensus matrix. Besides, the
use of hyperslabs enables a significant reduction of the computational demand
with only a minor loss in the performance. Numerical evaluations with synthetic
and real data are conducted showing the efficacy of the proposed algorithm
compared to the state of the art schemes.
Network flow of mobile agents enhances the evolution of cooperation
Date: 2018-03-20 00:00:00

We study the effect of contingent movement on the persistence of cooperation
on complex networks with empty nodes. Each agent plays Prisoner's Dilemma game
with its neighbors and then it either updates the strategy depending on the
payoff difference with neighbors or it moves to another empty node if not
satisfied with its own payoff. If no neighboring node is empty, each agent
stays at the same site. By extensive evolutionary simulations, we show that the
medium density of agents enhances cooperation where the network flow of mobile
agents is also medium. Moreover, if the movements of agents are more frequent
than the strategy updating, cooperation is further promoted. In scale-free
networks, the optimal density for cooperation is lower than other networks
because agents get stuck at hubs. Our study suggests that keeping a smooth
network flow is significant for the persistence of cooperation in ever-changing
societies.
A Mathematical Model for Tumor Cell Population Dynamics Based on Target Theory and Tumor Lifespan
Date: 2018-01-15 00:00:00

Radiation Therapy (XRT) is one of the most common cancer treatment methods.
In this paper, a new mathematical model is proposed for the population dynamics
of heterogeneous tumor cells following external beam radiation treatment.
According to the Target Theory, the tumor population is divided into m
different subpopulations based on the diverse effects of ionizing radiation on
human cells. A hybrid model con- sists of a system of differential equations
with random variable coefficients representing the transition rates between
subpopulations is proposed. This model is utilized to sim- ulate the dynamics
of cell subpopulations within a tumor. The model also describes the cell damage
heterogeneity and the repair mechanism between two consecutive dose fractions.
As such, a new definition of tumor lifespan based on population size is intro-
duced. Finally, the stability of the system is studied by using the Gershgorin
theorem. It is proven that the probability of target inactivity post radiation
plays the most important role in the stability of the system.
Computational Protein Design with Deep Learning Neural Networks
Date: 2018-02-24 00:00:00
Tasks: Deep Learning, Protein Design

Computational protein design has a wide variety of applications. Despite its
remarkable success, designing a protein for a given structure and function is
still a challenging task. On the other hand, the number of solved protein
structures is rapidly increasing while the number of unique protein folds has
reached a steady number, suggesting more structural information is being
accumulated on each fold. Deep learning neural network is a powerful method to
learn such big data set and has shown superior performance in many machine
learning fields. In this study, we applied the deep learning neural network
approach to computational protein design for predicting the probability of 20
natural amino acids on each residue in a protein. A large set of protein
structures was collected and a multi-layer neural network was constructed. A
number of structural properties were extracted as input features and the best
network achieved an accuracy of 38.3%. Using the network output as residue type
restraints was able to improve the average sequence identity in designing three
natural proteins using Rosetta. Moreover, the predictions from our network show
~3% higher sequence identity than a previous method. Results from this study
may benefit further development of computational protein design methods.
RF Lens-Embedded Antenna Array for mmWave MIMO: Design and Performance
Date: 2018-01-22 00:00:00

The requirement of high data-rate in the fifth generation wireless systems
(5G) calls for the ultimate utilization of the wide bandwidth in the mmWave
frequency band. Researchers seeking to compensate for mmWave's high path loss
and to achieve both gain and directivity have proposed that mmWave
multiple-input multiple-output (MIMO) systems make use of beamforming systems.
Hybrid beamforming in mmWave demonstrates promising performance in achieving
high gain and directivity by using phase shifters at the analog processing
block. What remains a problem, however, is the actual implementation of mmWave
beamforming systems; to fabricate such a system is costly and complex. With the
aim of reducing such cost and complexity, this article presents actual
prototypes of the lens antenna as an effective device to be used in the future
5G mmWave hybrid beamforming systems. Using a lens as a passive phase shifter
enables beamforming without the heavy network of active phase shifters, while
gain and directivity are achieved by the energy-focusing property of the lens.
Proposed in this article are two types of lens antennas, one for static and the
other for mobile usage. Their performance is evaluated using measurements and
simulation data along with link-level analysis via a software defined radio
(SDR) platform. Results show the promising potential of the lens antenna for
its high gain and directivity, and its improved beam-switching feasibility
compared to when a lens is not used. System-level evaluations reveal the
significant throughput enhancement in both real indoor and outdoor
environments. Moreover, the lens antenna's design issues are also discussed by
evaluating different lens sizes.
Characterization of catastrophic instabilities: Market crashes as paradigm
Date: 2018-01-22 00:00:00
Tasks: Time Series Analysis

Catastrophic events, though rare, do occur and when they occur, they have
devastating effects. It is, therefore, of utmost importance to understand the
complexity of the underlying dynamics and signatures of catastrophic events,
such as market crashes. For deeper understanding, we choose the US and Japanese
markets from 1985 onward, and study the evolution of the cross-correlation
structures of stock return matrices and their eigenspectra over different short
time-intervals or "epochs". A slight non-linear distortion is applied to the
correlation matrix computed for any epoch, leading to the emerging spectrum of
eigenvalues. The statistical properties of the emerging spectrum display: (i)
the shape of the emerging spectrum reflects the market instability, (ii) the
smallest eigenvalue may be able to statistically distinguish the nature of a
market turbulence or crisis -- internal instability or external shock, and
(iii) the time-lagged smallest eigenvalue has a statistically significant
correlation with the mean market cross-correlation. The smallest eigenvalue
seems to indicate that the financial market has become more turbulent in a
similar way as the mean does. Yet we show features of the smallest eigenvalue
of the emerging spectrum that distinguish different types of market
instabilities related to internal or external causes. Based on the paradigmatic
character of financial time series for other complex systems, the capacity of
the emerging spectrum to understand the nature of instability may be a new
feature, which can be broadly applied.
To jump or not to jump: The Bereitschaftspotential required to jump into 192-meter abyss
Date: 2018-01-22 00:00:00
Tasks: Decision Making, EEG, Electroencephalogram (EEG)

Self-initiated voluntary acts, such as pressing a button, are preceded by a
negative electrical brain potential, the Bereitschaftspotential (BP), that can
be recorded over the human scalp using electroencephalography (EEG). Up to now,
the BP required to initiate voluntary acts has only been recorded under
well-controlled laboratory conditions. It is thus not known if this form of
brain activity also underlies motor initiation in possible life-threatening
decision making, such as jumping into a 192-meter abyss, an act requiring
extraordinary willpower. Here, we report BP before self-initiated 192-meter
extreme bungee jumping across two semi-professional cliff divers (both male,
mean age 19.3 years). We found that the spatiotemporal dynamics of the BP is
comparable to that recorded under laboratory conditions. These results,
possible through recent advancements in wireless and portable EEG technology,
document for the first time pre-movement brain activity preceding possible
life-threatening decision making.
Numeraire markets
Date: 2018-01-22 00:00:00

In a stock market, the numeraire portfolio, if it exists, is the portfolio
with the highest expected logarithmic growth rate at all times. A numeraire
market is a stock market for which the market portfolio is the numeraire
portfolio. We study open markets, markets comprising the higher capitalization
stocks within a broad equity universe. The stocks we consider are represented
by continuous semimartingales, and we construct an example of a numeraire
market that is asymptotically stable.
A 3D Non-Stationary Wideband Geometry-Based Channel Model for MIMO Vehicle-to-Vehicle Communication System
Date: 2018-01-22 00:00:00

In this paper, we present a three-dimensional (3D) non-wide-sense stationary
(non-WSS) wideband geometry-based channel model for vehicle-to-vehicle (V2V)
communication environments. We introduce a two-cylinder model to describe
moving vehicles as well as multiple confocal semi-ellipsoid models to depict
stationary roadside scenarios. The received signal is constructed as a sum of
the line-of-sight (LoS), single-, and double-bounced rays with different
energies. Accordingly, the proposed channel model is sufficient for depicting a
wide variety of V2V environments, such as macro-, micro-, and picocells. The
relative movement between the mobile transmitter (MT) and mobile receiver (MR)
results in time-variant geometric statistics that make our channel model
non-stationary. Using this channel model, the proposed channel statistics,
i.e., the time-variant space correlation functions (CFs), frequency CFs, and
corresponding Doppler power spectral density (PSD), were studied for different
relative moving time instants. The numerical results demonstrate that the
proposed 3D non-WSS wideband channel model is practical for characterizing real
V2V channels.
Capital allocation under the Fundamental Review of Trading Book
Date: 2019-01-14 00:00:00

Facing the FRTB, banks need to allocate their capital to each business units
or risk positions to evaluate the capital efficiency of their strategies. This
paper proposes two computationally efficient allocation methods which are
weighted according to liquidity horizon. Both methods provide more stable and
less negative allocations under the FRTB than under the current regulatory
framework.