pwc-archive/papers-with-abstracts huggingface dataset:
Portfolio optimization based on forecasting models using vine copulas: An empirical assessment for the financial crisis
Authors: Maziar Sahamkhadam, Andreas Stephan
Date: 2019-12-21 00:00:00
Tasks: Portfolio Optimization

We employ and examine vine copulas in modeling symmetric and asymmetric dependency structures and forecasting financial returns. We analyze the asset allocations performed during the 2008-2009 financial crisis and test different portfolio strategies such as maximum Sharpe ratio, minimum variance, and minimum conditional Value-at-Risk. We then specify the regular, drawable, and canonical vine copulas, such as the Student-t, Clayton, Frank, Joe, Gumbel, and mixed copulas, and analyze both in-sample and out-of-sample portfolio performances. Out-of-sample portfolio back-testing shows that vine copulas reduce portfolio risk better than simple copulas. Our econometric analysis of the outcomes of the various models shows that in terms of reducing conditional Value-at-Risk, D-vines appear to be better than R- and C-vines. Overall, we find that the Student-t drawable vine copula models perform best with regard to risk reduction, both for the entire period 2005-2012 as well as during the financial crisis.
Graphon-based sensitivity analysis of SIS epidemics
Date: 2020-01-30 00:00:00
Tasks: Sensitivity

In this work, we use the spectral properties of graphons to study stability
and sensitivity to noise of deterministic SIS epidemics over large networks. We
consider the presence of additive noise in a linearized SIS model and we derive
a noise index to quantify the deviation from the disease-free state due to
noise. For finite networks, we show that the index depends on the adjacency
eigenvalues of its graph. We then assume that the graph is a random sample from
a piecewise Lipschitz graphon with finite rank and, using the eigenvalues of
the associated graphon operator, we find an approximation of the index that is
tight when the network size goes to infinity. A numerical example is included
to illustrate the results.
The Black-Scholes-Merton dual equation
Authors: Shuxin Guo, Qiang Liu
Date: 2019-12-22 00:00:00

We derive the Black-Scholes-Merton dual equation, which has exactly the same form as the Black-Scholes-Merton equation. The novel and general equation works for options with a payoff of homogeneous of degree one, including European, American, Bermudan, Asian, barrier, lookback, etc., and leads to new insights into pricing and hedging. Perceptibly, a put-call equality emerges - all the put options can be priced as their corresponding calls by simultaneously swapping stock price (dividend yield) for strike price (risk-free rate), and vice versa. Equally important, we provide simple analytic formulas for hedging parameters delta and gamma, which elevate the put-call equality to true practical applicability. For futures options, the put-call equality leads to "symmetric" properties between puts and calls or among puts (calls).
Modeling and Analysis of sub-Terahertz Communication Channel via Mixture of Gamma Distribution
Authors: Kürşat Tekbıyık, Ali Rıza Ekti, Güneş Karabulut Kurt, Ali Görçin, Serhan Yarkan
Date: 2019-12-22 00:00:00

With the recent developments on opening the terahertz (THz) spectrum for experimental purposes by the Federal Communications Commission, transceivers operating in the range of 0.1THz-10THz, which are known as THz bands, will enable ultra-high throughput wireless communications. However, actual implementation of the high-speed and high-reliability THz band communication systems should start with providing extensive knowledge in regards to the propagation channel characteristics. Considering the huge bandwidth and the rapid changes in the characteristics of THz wireless channels, ray tracing and one-shot statistical modeling are not adequate to define an accurate channel model. In this work, we propose Gamma mixture-based channel modeling for the THz band via the expectation-maximization (EM) algorithm. First, maximum likelihood estimation (MLE) is applied to characterize the Gamma mixture model parameters, and then EM algorithm is used to compute MLEs of the unknown parameters of the measurement data. The accuracy of the proposed model is investigated by using the Weighted relative mean difference (WMRD) error metrics, Kullback-Leibler (KL)-divergence, and Kolmogorov-Smirnov test to show the difference between the proposed model and the actual probability density functions (PDFs) that are obtained via the designed test environment. According to WMRD error metrics, KL-divergence, and KS test results, PDFs generated by the mixture of Gamma distributions fit the actual histogram of the measurement data. It is shown that instead of taking pseudo-average characteristics of sub-bands in the wideband, using the mixture models allows for determining channel parameters more precisely.
End-Point Detection with State Transition Model based on Chunk-Wise Classification
Date: 2019-12-22 00:00:00
Tasks: Action Detection, Activity Detection, Classification

A state transition model (STM) based on chunk-wise classification was
proposed for end-point detection (EPD). In general, EPD is developed using
frame-wise voice activity detection (VAD) with additional STM, in which the
state transition is conducted based on VAD's frame-level decision (speech or
non-speech). However, VAD errors frequently occur in noisy environments, even
though we use state-of-the-art deep neural network based VAD, which causes the
undesired state transition of STM. In this work, to build robust STM, a state
transition is conducted based on chunk-wise classification as EPD does not need
to be conducted in frame-level. The chunk consists of multiple frames and the
classification of chunk between speech and non-speech is done by aggregating
the decisions of VAD for multiple frames, so that some undesired VAD errors in
a chunk can be smoothed by other correct VAD decisions. Finally, the model was
evaluated in both qualitative and quantitative measures including phone error
rate.
On the persistence of lytic bacteriophages in vivo and its consequences for bacteriophage therapy
Date: 2019-12-22 00:00:00

Bacteriophages are viruses infecting bacteria and archaea. Many phage species
cause infections which lead to the certain death of the infected prokaryotic
host cell and the release of a large batch of phage progeny, yet they have been
able to stably coexist with their bacterial hosts over the eons in nature, as
well as in the majority of laboratory experiments reported in the literature.
This possibility of a stable coexistence between populations of bacteria and
bacteriophages can be suspected to critically reduce the chances of a
successful therapeutic application of bacteriophages for combatting pathogenic
bacterial infections in vivo. Here, we extend an established differential
equation model describing the interaction of bacteria, bacteriophages, and the
host immune system, modelling different degrees of spatial heterogeneity of the
host organism by introducing a scaling parameter which alters the encounter
rates of the different cell populations. We demonstrate by rigorous
mathematical analysis that, depending on the degree of spatial heterogeneity,
the system will either converge to the desired state of bacterial and phage
extinction, to a state of persisting bacterial infection with a completely
eliminated bacteriophage population or to an equally undesirable state of a
stable long-term bacteria-bacteriophage coexistence. We additionally provide
numerical solutions of the model to illustrate the emerging dynamics and
discuss some implications for bacteriophage therapy.
Storage Control for Carbon Emission Reduction: Opportunities and Challenges
Date: 2019-12-22 00:00:00

Storage is vital to power systems as it provides the urgently needed
flexibility to the system. Meanwhile, it can contribute more than flexibility.
In this paper, we study the possibility of utilizing storage system for carbon
emission reduction. The opportunity arises due to the pending implementation of
carbon tax throughout the world. Without the right incentive, most system
operators have to dispatch the generators according to the merit order of the
fuel costs, without any control for carbon emissions. However, we submit that
storage may provide necessary flexibility in carbon emission reduction even
without carbon tax. We identify the non-convex structure to conduct storage
control for this task and propose an easy to implement dynamic programming
algorithm to investigate the value of storage in carbon emission reduction.
Efficient and Convergent Sequential Pseudo-Likelihood Estimation of Dynamic Discrete Games
Authors: Adam Dearing, Jason R. Blevins
Date: 2019-12-22 00:00:00

We propose a new sequential Efficient Pseudo-Likelihood (k-EPL) estimator for dynamic discrete choice games of incomplete information. k-EPL considers the joint behavior of multiple players simultaneously, as opposed to individual responses to other agents' equilibrium play. This, in addition to reframing the problem from conditional choice probability (CCP) space to value function space, yields a computationally tractable, stable, and efficient estimator. We show that each iteration in the k-EPL sequence is consistent and asymptotically efficient, so the first-order asymptotic properties do not vary across iterations. Furthermore, we show the sequence achieves higher-order equivalence to the finite-sample maximum likelihood estimator with iteration and that the sequence of estimators converges almost surely to the maximum likelihood estimator at a nearly-superlinear rate when the data are generated by any regular Markov perfect equilibrium, including equilibria that lead to inconsistency of other sequential estimators. When utility is linear in parameters, k-EPL iterations are computationally simple, only requiring that the researcher solve linear systems of equations to generate pseudo-regressors which are used in a static logit/probit regression. Monte Carlo simulations demonstrate the theoretical results and show k-EPL's good performance in finite samples in both small- and large-scale games, even when the game admits spurious equilibria in addition to one that generated the data. We apply the estimator to study the role of competition in the U.S. wholesale club industry.
Building general Langevin models from discrete data sets
Date: 2020-05-13 00:00:00
Tasks: Bayesian Inference

Many living and complex systems exhibit second order emergent dynamics.
Limited experimental access to the configurational degrees of freedom results
in data that appears to be generated by a non-Markovian process. This poses a
challenge in the quantitative reconstruction of the model from experimental
data, even in the simple case of equilibrium Langevin dynamics of Hamiltonian
systems. We develop a novel Bayesian inference approach to learn the parameters
of such stochastic effective models from discrete finite length trajectories.
We first discuss the failure of naive inference approaches based on the
estimation of derivatives through finite differences, regardless of the time
resolution and the length of the sampled trajectories. We then derive, adopting
higher order discretization schemes, maximum likelihood estimators for the
model parameters that provide excellent results even with moderately long
trajectories. We apply our method to second order models of collective motion
and show that our results also hold in the presence of interactions.
Building and Testing Yield Curve Generators for P&C Insurance
Date: 2019-12-22 00:00:00

Interest-rate risk is a key factor for property-casualty insurer capital. P&C
companies tend to be highly leveraged, with bond holdings much greater than
capital. For GAAP capital, bonds are marked to market but liabilities are not,
so shifts in the yield curve can have a significant impact on capital.
Yield-curve scenario generators are one approach to quantifying this risk. They
produce many future simulated evolutions of the yield curve, which can be used
to quantify the probabilities of bond-value changes that would result from
various maturity-mix strategies. Some of these generators are provided as
black-box models where the user gets only the projected scenarios. One focus of
this paper is to provide methods for testing generated scenarios from such
models by comparing to known distributional properties of yield curves.
  P&C insurers hold bonds to maturity and manage cash-flow risk by matching
asset and liability flows. Derivative pricing and stochastic volatility are of
little concern over the relevant time frames. This requires different models
and model testing than what is common in the broader financial markets.
  To complicate things further, interest rates for the last decade have not
been following the patterns established in the sixty years following WWII. We
are now coming out of the period of very low rates, yet are still not returning
to what had been thought of as normal before that. Modeling and model testing
are in an evolving state while new patterns emerge.
  Our analysis starts with a review of the literature on interest-rate model
testing, with a P&C focus, and an update of the tests for current market
behavior. We then discuss models, and use them to illustrate the fitting and
testing methods. The testing discussion does not require the model-building
section.
PDE-based multi-agent formation control using flatness and backstepping: analysis, design and robot experiments
Date: 2019-12-22 00:00:00
Tasks: Motion Planning

A PDE-based control concept is developed to deploy a multi-agent system into
desired formation profiles. The dynamic model is based on a coupled linear,
time-variant parabolic distributed parameter system. By means of a particular
coupling structure parameter information can be distributed within the agent
continuum. Flatness-based motion planning and feedforward control are combined
with a backstepping-based boundary controller to stabilise the distributed
parameter system of the tracking error. The tracking controller utilises the
required state information from a Luenberger-type state observer. By means of
an exogenous system the relocation of formation profiles is achieved. The
transfer of the control strategy to a finite-dimensional discrete multi-agent
system is obtained by a suitable finite difference discretization of the
continuum model, which in addition imposes a leader-follower communication
topology. The results are evaluated both in simulation studies and in
experiments for a swarm of mobile robots realizing the transition between
different stable and unstable formation profiles.
Pricing of the Geometric Asian Options Under a Multifactor Stochastic Volatility Model
Authors: Gifty Malhotra, R. Srivastava, H. C. Taneja
Date: 2019-12-23 00:00:00
Tasks: ARC

This paper focuses on the pricing of continuous geometric Asian options (GAOs) under a multifactor stochastic volatility model. The model considers fast and slow mean reverting factors of volatility, where slow volatility factor is approximated by a quadratic arc. The asymptotic expansion of the price function is assumed, and the first order price approximation is derived using the perturbation techniques for both floating and fixed strike GAOs. Much simplified pricing formulae for the GAOs are obtained in this multifactor stochastic volatility framework. The zeroth order term in the price approximation is the modified Black-Scholes price for the GAOs. This modified price is expressed in terms of the Black-Scholes price for the GAOs. The accuracy of the approximate option pricing formulae is established, and the model parameter is also estimated by capturing the volatility smiles.
Systematic clustering algorithm for chromatin accessibility data and its application to hematopoietic cells
Authors: Azusa Tanaka, Yasuhiro Ishitsuka, Hiroki Ohta, Akihiro Fujimoto, Jun-ichirou Yasunaga, Masao Matsuoka
Date: 2019-12-23 00:00:00
Tasks: Clustering

The huge amount of data acquired by high-throughput sequencing requires data reduction for effective analysis. Here we give a clustering algorithm for genome-wide open chromatin data using a new data reduction method. This method regards the genome as a string of $1$s and $0$s based on a set of peaks and calculates the Hamming distances between the strings. This algorithm with the systematically optimized set of peaks enables us to quantitatively evaluate differences between samples of hematopoietic cells and classify cell types, potentially leading to a better understanding of leukemia pathogenesis.
Intelligent Reflecting Surface-Assisted Cognitive Radio System
Date: 2019-12-23 00:00:00

Cognitive radio (CR) is an effective solution to improve the spectral
efficiency (SE) of wireless communications by allowing the secondary users
(SUs) to share spectrum with primary users. Meanwhile, intelligent reflecting
surface (IRS), also known as reconfigurable intelligent surface (RIS), has been
recently proposed as a promising approach to enhance energy efficiency (EE) of
wireless communication systems through intelligently reconfiguring the channel
environment. To improve both SE and EE, in this paper, we introduce multiple
IRSs to a downlink multiple-input single-output (MISO) CR system, in which a
single SU coexists with a primary network with multiple primary user receivers
(PU-RXs). Our design objective is to maximize the achievable rate of SU subject
to a total transmit power constraint on the SU transmitter (SU-TX) and
interference temperature constraints on the PU-RXs, by jointly optimizing the
beamforming at SU-TX and the reflecting coefficients at each IRS. Both perfect
and imperfect channel state information (CSI) cases are considered in the
optimization. Numerical results demonstrate that the introduction of IRS can
significantly improve the achievable rate of SU under both perfect and
imperfect CSI cases.
Spectrum-Energy-Economy Efficiency Trade-off of Wireless Communication Systems with Separated Indoor/Outdoor Scenarios for 5G and B5G
Authors: Yu Fu, Cheng-Xiang Wang, Zijun Zhao, Stephen McLaughlin
Date: 2019-12-23 00:00:00

In this paper, we consider a heterogeneous 5G cellular architecture that separates the outdoor and indoor scenarios and in particular study the trade-off between the spectrum efficiency (SE), energy efficiency (EE), economy efficiency (ECE). Mathematical expressions for the system capacity, EE, SE, and ECE respectively are derived using a proposed realistic power consumption model. The comparison of system performance in terms of SE, EE, and ECE, results in the observation that the proposed network architecture, which separates the outdoor and indoor scenarios, offers a promising solution for future communication systems that have strict requirements on the data rate and efficiency.
Model-Free Optimization for Reconfigurable Intelligent Surface with Statistical CSI
Date: 2019-12-23 00:00:00

In this paper, a single user multiple input single output downlink wireless
communication system is investigated, in which multiple reconfigurable
intelligent surfaces (RISs) are deployed to improve the propagation condition.
Our objective is to optimize the phase shift matrices of all the RISs by
exploiting the statistical channel state information (CSI). In particular, two
model-free algorithms are proposed, which are applicable for any channel
statistical assumptions. Numerical results show that the proposed algorithms
significantly outperform the random phase shift scheme, especially when the
channel randomness is low.
Economic Complexity: why we like "Complexity weighted diversification"
Date: 2019-12-23 00:00:00

A recent paper by Hausmann and collaborators (1) reaches the important
conclusion that Complexity-weighted diversification is the essential element to
predict country growth. We like this result because Complexity-weighted
diversification is precisely the first equation of the Fitness algorithm that
we introduced in 2012 (2,3). However, contrary to what is claimed in (1), it is
incorrect to say that diversification is contained also in the ECI algorithm
(4). We discuss the origin of this misunderstanding and show that the ECI
algorithm contains exactly zero diversification. This is actually one of the
reasons for the poor performances of ECI which leads to completely unrealistic
results, as for instance, the derivation that Qatar or Saudi Arabia are
industrially more competitive than China (5,6). Another important element of
our new approach is the representation of the economic dynamics of countries as
trajectories in the GDPpc-Fitness space (7-10). In some way also this has been
rediscovered by Hausmann and collaborators and renamed as "Stream plots", but,
given their weaker metrics and methods, they propose it to use it only for a
qualitative insight, while ours led to quantitative and successful forecasting.
The Fitness approach has paved the way to a robust and testable framework for
Economic Complexity resulting in a highly competitive scheme for growth
forecasting (7-10). According to a recent report by Bloomberg (9): The new
Fitness method, "systematically outperforms standard methods, despite requiring
much less data".
Statistical physics of interacting proteins: impact of dataset size and quality assessed in synthetic sequences
Date: 2020-03-16 00:00:00

Identifying protein-protein interactions is crucial for a systems-level
understanding of the cell. Recently, algorithms based on inverse statistical
physics, e.g. Direct Coupling Analysis (DCA), have allowed to use
evolutionarily related sequences to address two conceptually related inference
tasks: finding pairs of interacting proteins, and identifying pairs of residues
which form contacts between interacting proteins. Here we address two
underlying questions: How are the performances of both inference tasks related?
How does performance depend on dataset size and the quality? To this end, we
formalize both tasks using Ising models defined over stochastic block models,
with individual blocks representing single proteins, and inter-block couplings
protein-protein interactions; controlled synthetic sequence data are generated
by Monte-Carlo simulations. We show that DCA is able to address both inference
tasks accurately when sufficiently large training sets are available, and that
an iterative pairing algorithm (IPA) allows to make predictions even without a
training set. Noise in the training data deteriorates performance. In both
tasks we find a quadratic scaling relating dataset quality and size that is
consistent with noise adding in square-root fashion and signal adding linearly
when increasing the dataset. This implies that it is generally good to
incorporate more data even if its quality is imperfect, thereby shedding light
on the empirically observed performance of DCA applied to natural protein
sequences.
Forecasting Implied Volatility Smile Surface via Deep Learning and Attention Mechanism
Date: 2019-12-23 00:00:00

The implied volatility smile surface is the basis of option pricing, and the
dynamic evolution of the option volatility smile surface is difficult to
predict. In this paper, attention mechanism is introduced into LSTM, and a
volatility surface prediction method combining deep learning and attention
mechanism is pioneeringly established. LSTM's forgetting gate makes it have
strong generalization ability, and its feedback structure enables it to
characterize the long memory of financial volatility. The application of
attention mechanism in LSTM networks can significantly enhance the ability of
LSTM networks to select input features. The experimental results show that the
two strategies constructed using the predicted implied volatility surfaces have
higher returns and Sharpe ratios than that the volatility surfaces are not
predicted. This paper confirms that the use of AI to predict the implied
volatility surface has theoretical and economic value. The research method
provides a new reference for option pricing and strategy.