pwc-archive/papers-with-abstracts huggingface dataset:
Pricing variance swaps with stochastic volatility and stochastic interest rate under full correlation structure
Authors: Teh Raihana Nazirah Roslan, Wenjun Zhang, Jiling Cao
Date: 2016-10-30 00:00:00

This paper considers the case of pricing discretely-sampled variance swaps under the class of equity-interest rate hybridization. Our modeling framework consists of the equity which follows the dynamics of the Heston stochastic volatility model, and the stochastic interest rate is driven by the Cox-Ingersoll-Ross (CIR) process with full correlation structure imposed among the state variables. This full correlation structure possess the limitation to have fully analytical pricing formula for hybrid models of variance swaps, due to the non-affinity property embedded in the model itself. We address this issue by obtaining an efficient semi-closed form pricing formula of variance swaps for an approximation of the hybrid model via the derivation of characteristic functions. Subsequently, we implement numerical experiments to evaluate the accuracy of our pricing formula. Our findings confirmed that the impact of the correlation between the underlying and the interest rate is significant for pricing discretely-sampled variance swaps.
Model-free bounds on Value-at-Risk using extreme value information and statistical distances
Date: 2018-11-17 00:00:00

We derive bounds on the distribution function, therefore also on the
Value-at-Risk, of $\varphi(\mathbf X)$ where $\varphi$ is an aggregation
function and $\mathbf X = (X_1,\dots,X_d)$ is a random vector with known
marginal distributions and partially known dependence structure. More
specifically, we analyze three types of available information on the dependence
structure: First, we consider the case where extreme value information, such as
the distributions of partial minima and maxima of $\mathbf X$, is available. In
order to include this information in the computation of Value-at-Risk bounds,
we utilize a reduction principle that relates this problem to an optimization
problem over a standard Fr\'echet class, which can then be solved by means of
the rearrangement algorithm or using analytical results. Second, we assume that
the copula of $\mathbf X$ is known on a subset of its domain, and finally we
consider the case where the copula of $\mathbf X$ lies in the vicinity of a
reference copula as measured by a statistical distance. In order to derive
Value-at-Risk bounds in the latter situations, we first improve the
Fr\'echet--Hoeffding bounds on copulas so as to include this additional
information on the dependence structure. Then, we translate the improved
Fr\'echet--Hoeffding bounds to bounds on the Value-at-Risk using the so-called
improved standard bounds. In numerical examples we illustrate that the
additional information typically leads to a significant improvement of the
bounds compared to the marginals-only case.
Long-range Correlation and Market Segmentation in Bond Market
Date: 2016-10-31 00:00:00

This paper looks into the analysis of the long-range auto-correlations and
cross-correlations in bond market. Based on Detrended Moving Average (DMA)
method, empirical results present a clear evidence of long-range persistence
that exists in one year scale. The degree of long-range correlation related to
maturities has an upward tendency with a peak in short term. These findings
confirm the expectations of fractal market hypothesis (FMH). Furthermore, we
have developed a method based on a complex network to study the long-range
cross-correlation structure and apply it to our data, and found a clear pattern
of market segmentation in the long run. We also detected the nature of
long-range correlation in the sub-period 2007 to 2012 and 2011 to 2016. The
result from our research shows that long-range auto-correlations are decreasing
in the recent years while long-range cross-correlations are strengthening.
The Nash equilibrium of forest ecosystems
Date: 2016-10-31 00:00:00

To find the possible equilibrium states of forest ecosystems one are
suggested to use the theory of differential games. At within the 4-tier model
of mosaic forest communities it established the existence of the Nash
equilibrium states in such ecosystems
Loading Pricing of Catastrophe Bonds and Other Long-Dated, Insurance-Type Contracts
Date: 2016-10-31 00:00:00

Catastrophe risk is a major threat faced by individuals, companies, and
entire economies. Catastrophe (CAT) bonds have emerged as a method to offset
this risk and a corresponding literature has developed that attempts to provide
a market-consistent pricing methodology for these and other long-dated,
insurance-type contracts. This paper aims to unify and generalize several of
the widely-used pricing approaches for long-dated contracts with a focus on
stylized CAT bonds and market-consistent valuation. It proposes a loading
pricing concept that combines the theoretically possible minimal price of a
contract with its formally obtained risk neutral price, without creating
economically meaningful arbitrage. A loading degree controls how much influence
the formally obtained risk neutral price has on the market price. A key finding
is that this loading degree has to be constant for a minimally fluctuating
contract, and is an important, measurable characteristic for prices of
long-dated contracts. Loading pricing allows long-dated, insurance-type
contracts to be priced less expensively and with higher return on investment
than under classical pricing approaches. Loading pricing enables insurance
companies to accumulate systematically reserves needed to manage its risk of
ruin in a market consistent manner.
Mean Field Game of Controls and An Application To Trade Crowding
Date: 2017-09-21 00:00:00

In this paper we formulate the now classical problem of optimal liquidation
(or optimal trading) inside a Mean Field Game (MFG). This is a noticeable
change since usually mathematical frameworks focus on one large trader in front
of a "background noise" (or "mean field"). In standard frameworks, the
interactions between the large trader and the price are a temporary and a
permanent market impact terms, the latter influencing the public price. In this
paper the trader faces the uncertainty of fair price changes too but not only.
He has to deal with price changes generated by other similar market
participants, impacting the prices permanently too, and acting strategically.
Our MFG formulation of this problem belongs to the class of "extended MFG", we
hence provide generic results to address these "MFG of controls", before
solving the one generated by the cost function of optimal trading. We provide a
closed form formula of its solution, and address the case of "heterogenous
preferences" (when each participant has a different risk aversion). Last but
not least we give conditions under which participants do not need to
instantaneously know the state of the whole system, but can "learn" it day
after day, observing others' behaviors.
Leveraging contact network structure in the design of cluster randomized trials
Date: 2016-10-28 00:00:00

Background: In settings where proof-of-principle trials have succeeded but
the effectiveness of different forms of implementation remains uncertain,
trials that not only generate information about intervention effects but also
provide public health benefit would be useful. Cluster randomized trials (CRT)
capture both direct and indirect intervention effects; the latter depends
heavily on contact networks within and across clusters. We propose a novel
class of connectivity-informed trial designs that leverages information about
such networks in order to improve public health impact and preserve ability to
detect intervention effects.
  Methods: We consider CRTs in which the order of enrollment is based on the
total number of ties between individuals across clusters (based either on the
total number of inter-cluster connections or on connections only to untreated
clusters). We include options analogous both to traditional Parallel and
Stepped Wedge designs. We also allow for control clusters to be "held-back"
from re-randomization for some period. We investigate the performance epidemic
control and power to detect vaccine effect performance of these designs by
simulating vaccination trials during an SEIR-type epidemic using a
network-structured agent-based model.
  Results: In our simulations, connectivity-informed designs have lower peak
infectiousness than comparable traditional designs and reduce cumulative
incidence by 20%, but with little impact on time to end of epidemic and reduced
power to detect differences in incidence across clusters. However even a brief
"holdback" period restores most of the power lost compared to traditional
approaches.
  Conclusion: Incorporating information about cluster connectivity in design of
CRTs can increase their public health impact, especially in acute outbreak
settings, with modest cost in power to detect an effective intervention.
COSPEDTree-II: Improved Couplet based Phylogenetic Supertree
Date: 2016-10-31 00:00:00

A Supertree synthesizes the topologies of a set of phylogenetic trees
carrying overlapping taxa set. In process, conflicts in the tree topologies are
aimed to be resolved with the consensus clades. Such a problem is proved to be
NP-hard. Various heuristics on local search, maximum parsimony, graph cut, etc.
lead to different supertree approaches, of which the most popular methods are
based on analyzing fixed size subtree topologies (such as triplets or
quartets). Time and space complexities of these methods, however, depend on the
subtree size considered. Our earlier proposed supertree method COSPEDTree, uses
evolutionary relationship among individual couplets (taxa pair), to produce
slightly conservative (not fully resolved) supertrees. Here we propose its
improved version COSPEDTree-II, which produces better resolved supertree with
lower number of missing branches, and incurs much lower running time. Results
on biological datasets show that COSPEDTree-II belongs to the category of high
performance and computationally efficient supertree methods.
Physical Basis of Large Microtubule Aster Growth
Date: 2016-10-31 00:00:00

Microtubule asters - radial arrays of microtubules organized by centrosomes -
play a fundamental role in the spatial coordination of animal cells. The
standard model of aster growth assumes a fixed number of microtubules
originating from the centrosomes. However, aster morphology in this model does
not scale with cell size, and we recently found evidence for non-centrosomal
microtubule nucleation. Here, we combine autocatalytic nucleation and
polymerization dynamics to develop a biophysical model of aster growth. Our
model predicts that asters expand as traveling waves and recapitulates all
major aspects of aster growth. As the nucleation rate increases, the model
predicts an explosive transition from stationary to growing asters with a
discontinuous jump of the growth velocity to a nonzero value. Experiments in
frog egg extract confirm the main theoretical predictions. Our results suggest
that asters observed in large frog and amphibian eggs are a meshwork of short,
unstable microtubules maintained by autocatalytic nucleation and provide a
paradigm for the assembly of robust and evolvable polymer networks.
Integration of continuous-time dynamics in a spiking neural network simulator
Date: 2016-10-31 00:00:00

Contemporary modeling approaches to the dynamics of neural networks consider
two main classes of models: biologically grounded spiking neurons and
functionally inspired rate-based units. The unified simulation framework
presented here supports the combination of the two for multi-scale modeling
approaches, the quantitative validation of mean-field approaches by spiking
network simulations, and an increase in reliability by usage of the same
simulation code and the same network model specifications for both model
classes. While most efficient spiking simulations rely on the communication of
discrete events, rate models require time-continuous interactions between
neurons. Exploiting the conceptual similarity to the inclusion of gap junctions
in spiking network simulations, we arrive at a reference implementation of
instantaneous and delayed interactions between rate-based models in a spiking
network simulator. The separation of rate dynamics from the general connection
and communication infrastructure ensures flexibility of the framework. We
further demonstrate the broad applicability of the framework by considering
various examples from the literature ranging from random networks to neural
field models. The study provides the prerequisite for interactions between
rate-based and spiking models in a joint simulation.
Numerical Facet Range Partition: Evaluation Metric and Methods
Authors: Liu Xueqing, Zhai Chengxiang, Han Wei, Gungor Onur
Date: 2017-02-27 00:00:00
Tasks: Attribute

Faceted navigation is a very useful component in today's search engines. It
is especially useful when user has an exploratory information need or prefer
certain attribute values than others. Existing work has tried to optimize
faceted systems in many aspects, but little work has been done on optimizing
numerical facet ranges (e.g., price ranges of product). In this paper, we
introduce for the first time the research problem on numerical facet range
partition and formally frame it as an optimization problem. To enable
quantitative evaluation of a partition algorithm, we propose an evaluation
metric to be applied to search engine logs. We further propose two range
partition algorithms that computationally optimize the defined metric.
Experimental results on a two-month search log from a major e-Commerce engine
show that our proposed method can significantly outperform baseline.
Off the Beaten Path: Let's Replace Term-Based Retrieval with k-NN Search
Authors: Boytsov Leonid, Novak David, Malkov Yury, Nyberg Eric
Date: 2016-10-31 00:00:00
Tasks: Retrieval

Retrieval pipelines commonly rely on a term-based search to obtain candidate
records, which are subsequently re-ranked. Some candidates are missed by this
approach, e.g., due to a vocabulary mismatch. We address this issue by
replacing the term-based search with a generic k-NN retrieval algorithm, where
a similarity function can take into account subtle term associations. While an
exact brute-force k-NN search using this similarity function is slow, we
demonstrate that an approximate algorithm can be nearly two orders of magnitude
faster at the expense of only a small loss in accuracy. A retrieval pipeline
using an approximate k-NN search can be more effective and efficient than the
term-based pipeline. This opens up new possibilities for designing effective
retrieval pipelines. Our software (including data-generating code) and
derivative data based on the Stack Overflow collection is available online.
Computed Flow and Fluorescence Over the Ocular Surface
Date: 2016-10-31 00:00:00

Fluorescein is perhaps the most commonly used substance to visualize tear
film thickness and dynamics; better understanding of this process aids
understanding of dry eye syndrome which afflicts millions of people. We study a
mathematical model for tear film flow, evaporation, solutal transport and
fluorescence over the exposed ocular surface during the interblink. Transport
of the fluorescein ion by fluid flow in the tear film affects the intensity of
fluorescence via changes in concentration and tear film thickness. Evaporation
causes increased osmolarity and potential irritation over the ocular surface;
it also alters fluorescein concentration and thus fluorescence. Using thinning
rates from in vivo measurements together with thin film equations for flow and
transport of multiple solutes, we compute dynamic results for tear film
quantities of interest. We compare our computed intensity distributions with in
vivo observations. A number of experimental features are recovered by the
model.
Meta-CTA Trading Strategies based on the Kelly Criterion
Date: 2016-10-31 00:00:00

The influence of Commodity Trading Advisors (CTA) on the price process is
explored with the help of a simple model. CTA managers are taken to be Kelly
optimisers, which invest a fixed proportion of their assets in the risky asset
and the remainder in a riskless asset. This requires regular adjustment of the
portfolio weights as prices evolve. The CTA trading activity impacts the price
change in the form of a power law. These two rules governing investment ratios
and price impact are combined and lead through updating at fixed time intervals
to a deterministic price dynamic. For different choices of the model parameters
one gets qualitatively different dynamics. The result can be expressed as a
phase diagram. Meta-CTA strategies can be devised to exploit the predictability
inherent in the model dynamics by avoiding critical areas of the phase diagram
or by taking a contrarian position at an opportune time.
Optimal retirement income tontines
Date: 2016-10-28 00:00:00

Tontines were once a popular type of mortality-linked investment pool. They
promised enormous rewards to the last survivors at the expense of those died
early. And, while this design appealed to the gambling instinc}, it is a
suboptimal way to generate retirement income. Indeed, actuarially-fair life
annuities making constant payments -- where the insurance company is exposed to
longevity risk -- induce greater lifetime utility. However, tontines do not
have to be structured the historical way, i.e. with a constant cash flow shared
amongst a shrinking group of survivors. Moreover, insurance companies do not
sell actuarially-fair life annuities, in part due to aggregate longevity risk.
  We derive the tontine structure that maximizes lifetime utility. Technically
speaking we solve the Euler-Lagrange equation and examine its sensitivity to
(i.) the size of the tontine pool $n$, and (ii.) individual longevity risk
aversion $\gamma$. We examine how the optimal tontine varies with $\gamma$ and
$n$, and prove some qualitative theorems about the optimal payout.
Interestingly, Lorenzo de Tonti's original structure is optimal in the limit as
longevity risk aversion $\gamma \to \infty$. We define the natural tontine as
the function for which the payout declines in exact proportion to the survival
probabilities, which we show is near-optimal for all $\gamma$ and $n$. We
conclude by comparing the utility of optimal tontines to the utility of loaded
life annuities under reasonable demographic and economic conditions and find
that the life annuity's advantage over the optimal tontine is minimal.
  In sum, this paper's contribution is to (i.) rekindle a discussion about a
retirement income product that has been long neglected, and (ii.) leverage
economic theory as well as tools from mathematical finance to design the next
generation of tontine annuities.
Wavelet algorithm for the identification of P300 ERP component
Date: 2016-07-26 00:00:00
Tasks: EEG, Electroencephalogram (EEG), ERP, Time Series, Time Series Analysis

Brain-computer interfaces have many algorithms based on the P300 component of
ERP. Modern industry has started to produce consumer grade EEG equipment which
is handy and not too expensive. This gives us an opportunity to use BCI in
everyday practice. In order to improve the performance of these devices, we
need effective algorithms for time series analysis and pattern recognition. We
have tested Emotiv Insight headset in a real live environment and we have
conducted several tests for series of standard wavelets in P300 pattern
recognition task.
ODE and Random Boolean networks in application to modelling of 6-mercaptopurine metabolism
Date: 2016-10-31 00:00:00

We consider two approaches to modelling of cell metabolism of
6-mercaptopurine, which is one of the important chemotherapy drugs used for
treating of acute lymphocytic leukemia: kinetic ordinary differential equations
and random Boolean networks, and analyse their interplay with respect to taking
into account ATP concentration as a key parameter of switching between
different pathways. It is shown that Boolean networks, which allow for avoiding
complexity of general kinetic modelling, preserve an opportunity to the
reproduction of the principal switching mechanism. To keep a detailed
quantitative measure of the control parameter, a combined Boolean-ODE method is
proposed.
Folding with a protein's native shortcut network
Date: 2017-12-13 00:00:00
Tasks: Protein Folding

A complex network approach to protein folding is proposed. The graph object
is the network of shortcut edges present in a native-state protein (SCN0).
Although SCN0s are found via an intuitive message passing algorithm (S.
Milgram, Psychology Today, May 1967 pp. 61-67), they are meaningful enough that
the logarithm form of their contact order (SCN0_lnCO) correlates significantly
with protein kinetic rates, regardless of protein size. Further, the clustering
coefficient of a SCN0 (CSCN0) can be used to combine protein segments
iteratively within the Restricted Binary Collision model to form the whole
native structure. This simple yet surprisingly effective strategy identified
reasonable folding pathways for 12 small single-domain two-state folders, and
three non-canonical proteins: ACBP (non-two-state), Top7 (non-cooperative) and
DHFR (non-single-domain, > 100 residues). For two-state folders, CSCN0 is
relatable to folding rates, transition-state placement and stability. The
influence of CSCN0 on folding extends to non-native structures. Moreover, SCN
analysis of non-native structures could suggest three fold success factors for
the fast folding Villin headpiece peptide. These results support the view of
protein folding as a bottom-up hierarchical process guided from above by
native-state topology, and could facilitate future constructive demonstrations
of this long held hypothesis for larger proteins.
Characterization of phase transitions in a model ecosystem of sessile species
Date: 2017-02-28 00:00:00
Tasks: Diversity

We consider a model ecosystem of sessile species competing for space. In
particular, we consider the system introduced in [Mathiesen et al. Phys. Rev.
Lett. 107, 188101 (2011)] where species compete according to a fixed
interaction network with links determined by a Bernoulli process. In the limit
of a small introduction rate of new species, the model exhibits a discontinuous
transition from a high-diversity state to a low-diversity state as the
interaction probability between species, $\gamma$, is increased from zero. Here
we explore the effects of finite introduction rates and system-size on the
phase transition by utilizing efficient parallel computing. We find that the
low state appears for $\gamma>\gamma_c$. As $\gamma$ is increased further, the
high state approaches to the low state, suggesting the possibility that the two
states merge at a high $\gamma$. We find that the fraction of time spent in the
high state becomes longer with higher introduction rates, but the availability
of the two states is rather insensitive to the value of the introduction rate.
Furthermore we establish a relation between the introduction rate and the
system size, which preserves the probability for the system to remain in the
high-diversity state.