pwc-archive/papers-with-abstracts huggingface dataset:
Magnitude and significance of the peak of early embryonic mortality
Authors: Qinghua Chen, Zengru Di, Eduardo M. Garcia-Roger, Hui Li, Peter Richmond, Bertrand M. Roehner
Date: 2020-03-05 00:00:00

Biologically, for any organism life does not start at birth but at fertilization of the embryo. Embryonic development is of great importance because it determines congenital anomalies and influences their severity. Whereas there is detailed qualitative knowledge of the successive steps of embryonic development, little is known about their probabilities of success or failure. Embryonic mortality as a function of post fertilization time provides a simple (albeit crude) way to identify major defects. We find that, in line with the few other species for which data are available, the embryonic mortality of zebrafish has a prominent peak shortly after fertilization. This is called the early embryonic mortality (EEM) effect. Although a number of immediate causes of death (e.g. infection, excess of carbon dioxide or of lactic acid, chromosomal defects) can be cited, the common underlying factor remains unknown. After reviewing embryonic mortality data available for chicken and a few other farm animals, we explain that zebrafish are particularly suited for such a study because embryogenesis can be followed from its very beginning and can be observed easily thanks to transparent egg shells. We report the following findings. (i) The mortality peak occurs in the first 15% of the 75-80 hours of embryogenesis and it is about 50 times higher than the low plateau which follows. (ii) The shape of the age-specific death rate is largely independent of the death level. Presently, little is known about the nature of embryonic defects. However, by reviewing two special cases we show that even small initial defects, e.g. spatial cellular asymmetries or irregularities in the timing of development, carry with them lethal effects in later stages of embryogenesis.
How heterogeneous thymic output and homeostatic proliferation shape naive T cell receptor clone abundance distributions
Authors: Renaud Dessalles, Yunbei Pan, Mingtao Xia, Davide Maestrini, Maria R. D'Orsogna, Tom Chou
Date: 2019-06-18 00:00:00

The set of T cells that express the same T cell receptor (TCR) sequence represents a T cell clone. The number of different naive T cell clones in an organism reflects the number of different T cell receptors (TCRs) arising from recombination of the V(D)J gene segments during T cell development in the thymus. TCR diversity and more specifically, the clone abundance distribution is an important factor in immune function. Specific recombination patterns occur more frequently than others while subsequent interactions between TCRs and self-antigens are known to trigger proliferation and sustain naive T cell survival. These processes are TCR-dependent, leading to clone-dependent thymic export and naive T cell proliferation rates. Using a mean-field approximation to the solution of a regulated birth-death-immigration model and a modification arising from sampling, we systematically quantify how TCR-dependent heterogeneities in immigration and proliferation rates affect the shape of clone abundance distributions (the number of different clones that are represented by a specific number of cells, or "clone counts"). By comparing predicted clone counts derived from our heterogeneous birth-death-immigration model with experimentally sampled clone abundances, we show that although heterogeneity in immigration rates causes very little change to predicted clone-counts, significant heterogeneity in proliferation rates is necessary to generate the observed abundances with reasonable physiological parameter values. Our analysis provides constraints among physiological parameters that are necessary to yield predictions that qualitatively match the data. Assumptions of the model and potentially other important mechanistic factors are discussed.
Cardiac kinematic parameters computed from video of $\textit{in situ}$ beating heart
Authors: Lorenzo Fassina, Giacomo Rozzi, Stefano Rossi, Simone Scacchi, Maricla Galetti, Francesco Paolo Lo Muzio, Fabrizio Del Bianco, Piero Colli Franzone, Giuseppe Petrilli, Giuseppe Faggian, Michele Miragoli
Date: 2020-02-05 00:00:00

Mechanical function of the heart during open-chest cardiac surgery is exclusively monitored by echocardiographic techniques. However, little is known about local kinematics, particularly for the reperfused regions after ischemic events. We report a novel imaging modality, which extracts local and global kinematic parameters from videos of $\textit{in situ}$ beating hearts, displaying live video cardiograms of the contraction events. A custom algorithm tracked the movement of a video marker positioned $\textit{ad hoc}$ onto a selected area and analyzed, during the entire recording, the contraction trajectory, displacement, velocity, acceleration, kinetic energy and force. Moreover, global epicardial velocity and vorticity were analyzed by means of Particle Image Velocimetry tool. We validated our new technique by i) computational modeling of cardiac ischemia, ii) video recordings of ischemic/reperfused rat hearts, iii) videos of beating human hearts before and after coronary artery bypass graft, and iv) local Frank-Starling effect. In rats, we observed a decrement of kinematic parameters during acute ischemia and a significant increment in the same region after reperfusion. We detected similar behavior in operated patients. This modality adds important functional values on cardiac outcomes and supports the intervention in a contact-free and non-invasive mode. Moreover, it does not require particular operator-dependent skills.
Population pharmacokinetics and dosing regimen optimization of tacrolimus in Chinese lung transplant recipients
Authors: Xiaojun Cai, Huizhu Song, Zheng Jiao, Hang Yang, Min Zhu, Chengyu Wang, Dong Wei, Lingzhi Shi, Bo Wu, Jinyu Chen
Date: 2020-02-01 00:00:00

We aimed to develop a population pharmacokinetic model of tacrolimus in Chinese lung transplant recipients, and propose model based dosing regimens for individualized treatment. We obtained 807 tacrolimus whole blood concentrations from 52 lung transplant patients and genotyped CYP3A5*3. Population pharmacokinetic analysis was performed using nonlinear mixed effects modeling. Monte Carlo simulations were employed to design initial dosing regimens. Tacrolimus pharmacokinetics was described by a one compartment model with first order absorption and elimination process. The mean estimated apparent clearance was 13.1 l/h with 20.1% inter subject variability in CYP3A5*3/*3 70kg patients with 30% hematocrit and voriconazole free therapy, which is lower than that in Caucasian(17.5 to 36.5 l/h). Hematocrit, postoperative days, tacrolimus daily dose, voriconazole cotherapy, and CYP3A5*3 genotype were identified as significant covariates for tacrolimus clearance. To achieve the target trough concentration (10 to 15 ng/ml) on the 8th day after transplantation, CYP3A5*1/*3 patients with voriconazole free cotherapy, a higher initial dosage than the current regimen of 0.04 mg/kg q12h should be recommened. Given the nonlinear kinetics of tacrolimus and large variability, population pharmacokinetic model should be combined with therapeutic drug monitoring to optimize individualized therapy.
Risk aggregation and capital allocation using a new generalized Archimedean copula
Authors: Fouad Marri, Khouzeima Moutanabbir
Date: 2021-03-19 00:00:00

In this paper, we address risk aggregation and capital allocation problems in the presence of dependence between risks. The dependence structure is defined by a mixed Bernstein copula which represents a generalization of the well-known Archimedean copulas. Using this new copula, the probability density function and the cumulative distribution function of the aggregate risk are obtained. Then, closed-form expressions for basic risk measures, such as tail value-at-risk(TVaR) and TVaR-based allocations, are derived.
Understanding Smart Contracts: Hype or Hope?
Authors: Elizaveta Zinovyeva, Raphael C. G. Reule, Wolfgang Karl Härdle
Date: 2021-03-15 00:00:00

Smart Contracts are commonly considered to be an important component or even a key to many business solutions in an immense variety of sectors and promises to securely increase their individual efficiency in an ever more digitized environment. Introduced in the early 1990s, the technology has gained a lot of attention with its application to blockchain technology to an extent, that can be considered a veritable hype. Reflecting the growing institutional interest, this intertwined exploratory study between statistics, information technology, and law contrasts these idealistic stories with the data reality and provides a mandatory step of understanding the matter, before any further relevant applications are discussed as being "factually" able to replace traditional constructions. Besides fundamental flaws and applica-tion difficulties of currently employed Smart Contracts, the technological drive and enthusiasm backing it may however serve as a jump-off board for future developments thrusting well in the presently unshakeable traditional structures.
Finite element solutions of the nonlinear RAPM Black-Scholes model
Authors: Dongming Wei, Yogi Ahmad Erlangga, Andrey Pak, Laila Zhexembay
Date: 2021-03-15 00:00:00

his paper presents finite element methods for solving numerically the Risk-Adjusted Pricing Methodology (RAPM) Black-Scholes model for option pricing with transaction costs. Spatial finite element models based on P1 and/or P2 elements are formulated using some group finite elements and numerical quadrature to handle the nonlinear term, in combination with a Crank-Nicolson-type temporal scheme. The temporal scheme is implemented using the Rannacher approach. Spatial-temporal mesh-size ratios are observed for controlling the stability of our method. Our results compare favorably with the finite difference results in the literature for the model.
Trading Signals In VIX Futures
Authors: M. Avellaneda, T. N. Li, A. Papanicolaou, G. Wang
Date: 2021-03-02 00:00:00
Tasks: Position

We propose a new approach for trading VIX futures. We assume that the term structure of VIX futures follows a Markov model. Our trading strategy selects a position in VIX futures by maximizing the expected utility for a day-ahead horizon given the current shape and level of the term structure. Computationally, we model the functional dependence between the VIX futures curve, the VIX futures positions, and the expected utility as a deep neural network with five hidden layers. Out-of-sample backtests of the VIX futures trading strategy suggest that this approach gives rise to reasonable portfolio performance, and to positions in which the investor will be either long or short VIX futures contracts depending on the market environment.
A deep learning model for gas storage optimization
Authors: Nicolas Curin, Michael Kettler, Xi Kleisinger-Yu, Vlatka Komaric, Thomas Krabichler, Josef Teichmann, Hanna Wutte
Date: 2021-02-03 00:00:00
Tasks: Deep Learning, Management, model, reinforcement-learning, Reinforcement Learning (RL)

To the best of our knowledge, the application of deep learning in the field of quantitative risk management is still a relatively recent phenomenon. In this article, we utilize techniques inspired by reinforcement learning in order to optimize the operation plans of underground natural gas storage facilities. We provide a theoretical framework and assess the performance of the proposed method numerically in comparison to a state-of-the-art least-squares Monte-Carlo approach. Due to the inherent intricacy originating from the high-dimensional forward market as well as the numerous constraints and frictions, the optimization exercise can hardly be tackled by means of traditional techniques.
Approaches to Asian Option Pricing with Discrete Dividends
Authors: Jacob Lundgren, Yuri Shpolyanskiy
Date: 2017-02-03 00:00:00

The method and characteristics of several approaches to the pricing of discretely monitored arithmetic Asian options on stocks with discrete, absolute dividends are described. The contrast between method behaviors for options with an Asian tail and those with monitoring throughout their lifespan is emphasized. Rates of convergence are confirmed, but greater focus is put on actual performance in regions of accuracy which are realistic for use by practitioners. A hybrid approach combining Curran's analytical approximation with a two-dimensional finite difference method is examined with respect to the errors caused by the approximating assumptions. For Asian tails of equidistant monitoring dates, this method performs very well, but as the scenario deviates from the method's ideal conditions, the errors in the approximation grow unfeasible. For general monitoring straightforward solution of the full three-dimensional partial differential equation by finite differences is highly accurate but suffers from rapid degradation in performance as the monitoring interval increases. For options with long monitoring intervals a randomized quasi-Monte Carlo method with control variate variance reduction stands out as a powerful alternative.
SWIFT calibration of the Heston model
Authors: Eudald Romo, Luis Ortiz-Gracia
Date: 2021-03-02 00:00:00
Tasks: model

In the present work, the European option pricing SWIFT method is extended for Heston model calibration. The computation of the option price gradient is simplified thanks to the knowledge of the characteristic function in closed form. The proposed calibration machinery appears to be extremely fast, in particular for a single expiry and multiples strikes, outperforming the state-of-the-art method we compare with. Further, the a priori knowledge of SWIFT parameters makes possible a reliable and practical implementation of the presented calibration method. A wide set of stress, speed and convergence numerical experiments is carried out, with deep in-the-money, at-the-money and deep out-of-the-money options for very short and very long maturities.
Stock market's physical properties description based on Stokes law
Authors: Geoffrey Ducournau
Date: 2021-03-01 00:00:00

We propose in this paper to consider the stock market as a physical system assimilate to a fluid evolving in a macroscopic space subject to a Force that influences its movement over time where this last is arising from the collision between the supply and the demand of Financial agents. In fluid mechanics, this Force also results from the collisions of fluid molecules led by its physical property such as density, viscosity, and surface tension. The purpose of this article is to show that the dynamism of the stock market behavior can be explained qualitatively and quantitatively by considering the supply & demand collision as the result of Financial agents physical properties defined by Stokes Law. The first objective of this article is to show theoretically that fluid mechanics equations can be used to describe stock market physical properties. The second objective based on the knowledge of stock market physical properties is to propose an Econophysics analog of the stock market viscosity and Reynolds number to measure stock market conditions, whether laminar, transitory, or turbulent. The Reynolds Number defined in this way can be applied in research into the study and classification of stock market dynamics phases through for instance the creation of Econophysics analog of Moddy diagram, this last could be seen as a physical way to quantify asset and stock index idiosyncratic risk. The last objective is to present evidence from a computer simulation that the stock market behavior can be a priori, and posteriori explained by physical properties (viscosity & density) quantifiable by fluid mechanics law (Stokes law) and measurable with the stock market Reynolds Number.
Deep Equal Risk Pricing of Financial Derivatives with Multiple Hedging Instruments
Authors: Alexandre Carbonneau, Frédéric Godin
Date: 2021-02-25 00:00:00
Tasks: ERP

This paper studies the equal risk pricing (ERP) framework for the valuation of European financial derivatives. This option pricing approach is consistent with global trading strategies by setting the premium as the value such that the residual hedging risk of the long and short positions in the option are equal under optimal hedging. The ERP setup of Marzban et al. (2020) is considered where residual hedging risk is quantified with convex risk measures. The main objective of this paper is to assess through extensive numerical experiments the impact of including options as hedging instruments within the ERP framework. The reinforcement learning procedure developed in Carbonneau and Godin (2020), which relies on the deep hedging algorithm of Buehler et al. (2019b), is applied to numerically solve the global hedging problems by representing trading policies with neural networks. Among other findings, numerical results indicate that in the presence of jump risk, hedging long-term puts with shorter-term options entails a significant decrease of both equal risk prices and market incompleteness as compared to trading only the stock. Monte Carlo experiments demonstrate the potential of ERP as a fair valuation approach providing prices consistent with observable market prices. Analyses exhibit the ability of ERP to span a large interval of prices through the choice of convex risk measures which is close to encompass the variance-optimal premium.
Variational Autoencoders: A Hands-Off Approach to Volatility
Authors: Maxime Bergeron, Nicholas Fung, John Hull, Zissis Poulos
Date: 2021-02-07 00:00:00

A volatility surface is an important tool for pricing and hedging derivatives. The surface shows the volatility that is implied by the market price of an option on an asset as a function of the option's strike price and maturity. Often, market data is incomplete and it is necessary to estimate missing points on partially observed surfaces. In this paper, we show how variational autoencoders can be used for this task. The first step is to derive latent variables that can be used to construct synthetic volatility surfaces that are indistinguishable from those observed historically. The second step is to determine the synthetic surface generated by our latent variables that fits available data as closely as possible. As a dividend of our first step, the synthetic surfaces produced can also be used in stress testing, in market simulators for developing quantitative investment strategies, and for the valuation of exotic options. We illustrate our procedure and demonstrate its power using foreign exchange market data.
Flashot: A Snapshot of Flash Loan Attack on DeFi Ecosystem
Authors: Yixin Cao, Chuanwei Zou, Xianfeng Cheng
Date: 2021-02-01 00:00:00

Flash Loan attack can grab millions of dollars from decentralized vaults in one single transaction, drawing increasing attention from the Decentralized Finance (DeFi) players. It has also demonstrated an exciting opportunity that a huge wealth could be created by composing DeFi's building blocks and exploring the arbitrage change. However, a fundamental framework to study the field of DeFi has not yet reached a consensus and there's a lack of standard tools or languages to help better describe, design and improve the running processes of the infant DeFi systems, which naturally makes it harder to understand the basic principles behind the complexity of Flash Loan attacks. In this paper, we are the first to propose Flashot, a prototype that is able to transparently illustrate the precise asset flows intertwined with smart contracts in a standardized diagram for each Flash Loan event. Some use cases are shown and specifically, based on Flashot, we study a typical Pump and Arbitrage case and present in-depth economic explanations to the attacker's behaviors. Finally, we conclude the development trends of Flash Loan attacks and discuss the great impact on DeFi ecosystem brought by Flash Loan. We envision a brand new quantitative financial industry powered by highly efficient automatic risk and profit detection systems based on the blockchain.
Nonstationary Portfolios: Diversification in the Spectral Domain
Authors: Bruno Scalzo, Alvaro Arroyo, Ljubisa Stankovic, Danilo P. Mandic
Date: 2021-01-31 00:00:00
Tasks: Portfolio Optimization

Classical portfolio optimization methods typically determine an optimal capital allocation through the implicit, yet critical, assumption of statistical time-invariance. Such models are inadequate for real-world markets as they employ standard time-averaging based estimators which suffer significant information loss if the market observables are non-stationary. To this end, we reformulate the portfolio optimization problem in the spectral domain to cater for the nonstationarity inherent to asset price movements and, in this way, allow for optimal capital allocations to be time-varying. Unlike existing spectral portfolio techniques, the proposed framework employs augmented complex statistics in order to exploit the interactions between the real and imaginary parts of the complex spectral variables, which in turn allows for the modelling of both harmonics and cyclostationarity in the time domain. The advantages of the proposed framework over traditional methods are demonstrated through numerical simulations using real-world price data.
Sample path generation of the stochastic volatility CGMY process and its application to path-dependent option pricing
Authors: Young Shin Kim
Date: 2021-01-25 00:00:00
Tasks: regression

This paper proposes the sample path generation method for the stochastic volatility version of CGMY process. We present the Monte-Carlo method for European and American option pricing with the sample path generation and calibrate model parameters to the American style S\&P 100 index options market, using the least square regression method. Moreover, we discuss path-dependent options such as Asian and Barrier options.
Extensive networks would eliminate the demand for pricing formulas
Authors: Jaegi Jeon, Kyunghyun Park, Jeonggyu Huh
Date: 2021-01-22 00:00:00
Tasks: GPU

In this study, we generate a large number of implied volatilities for the Stochastic Alpha Beta Rho (SABR) model using a graphics processing unit (GPU) based simulation and enable an extensive neural network to learn them. This model does not have any exact pricing formulas for vanilla options, and neural networks have an outstanding ability to approximate various functions. Surprisingly, the network reduces the simulation noises by itself, thereby achieving as much accuracy as the Monte-Carlo simulation. Extremely high accuracy cannot be attained via existing approximate formulas. Moreover, the network is as efficient as the approaches based on the formulas. When evaluating based on high accuracy and efficiency, extensive networks can eliminate the necessity of the pricing formulas for the SABR model. Another significant contribution is that a novel method is proposed to examine the errors based on nonlinear regression. This approach is easily extendable to other pricing models for which it is hard to induce analytic formulas.
Least Squares Monte Carlo applied to Dynamic Monetary Utility Functions
Authors: Hampus Engsner
Date: 2021-01-15 00:00:00

In this paper we explore ways of numerically computing recursive dynamic monetary risk measures and utility functions. Computationally, this problem suffers from the curse of dimensionality and nested simulations are unfeasible if there are more than two time steps. The approach considered in this paper is to use a Least Squares Monte Carlo (LSM) algorithm to tackle this problem, a method which has been primarily considered for valuing American derivatives, or more general stopping time problems, as these also give rise to backward recursions with corresponding challenges in terms of numerical computation. We give some overarching consistency results for the LSM algorithm in a general setting as well as explore numerically its performance for recursive Cost-of-Capital valuation, a special case of a dynamic monetary utility function.