pwc-archive/papers-with-abstracts huggingface dataset:
Efficient Volatility Estimation for Lévy Processes with Jumps of Unbounded Variation
Authors: B. Cooper Boniece, José E. Figueroa-López, Yuchen Han
Date: 2022-02-02 00:00:00

Statistical inference for stochastic processes based on high-frequency observations has been an active research area for more than a decade. One of the most well-known and widely studied problems is that of estimation of the quadratic variation of the continuous component of an It\^o semimartingale with jumps. Several rate- and variance-efficient estimators have been proposed in the literature when the jump component is of bounded variation. However, to date, very few methods can deal with jumps of unbounded variation. By developing new high-order expansions of the truncated moments of a L\'evy process, we construct a new rate- and variance-efficient estimator for a class of L\'evy processes of unbounded variation, whose small jumps behave like those of a stable L\'evy process with Blumenthal-Getoor index less than $8/5$. The proposed method is based on a two-step debiasing procedure for the truncated realized quadratic variation of the process. Our Monte Carlo experiments indicate that the method outperforms other efficient alternatives in the literature in the setting covered by our theoretical framework.
Long-Horizon Return Predictability from Realized Volatility in Pure-Jump Point Processes
Authors: Meng-Chen Hsieh, Clifford Hurvich, Philippe Soulier
Date: 2022-02-01 00:00:00
Tasks: Point Processes, regression

We develop and justify methodology to consistently test for long-horizon return predictability based on realized variance. To accomplish this, we propose a parametric transaction-level model for the continuous-time log price process based on a pure jump point process. The model determines the returns and realized variance at any level of aggregation with properties shown to be consistent with the stylized facts in the empirical finance literature. Under our model, the long-memory parameter propagates unchanged from the transaction-level drift to the calendar-time returns and the realized variance, leading endogenously to a balanced predictive regression equation. We propose an asymptotic framework using power-law aggregation in the predictive regression. Within this framework, we propose a hypothesis test for long horizon return predictability which is asymptotically correctly sized and consistent.
Minimum Wages and Optimal Redistribution
Authors: Damián Vergara
Date: 2022-02-02 00:00:00

This paper analyzes whether a minimum wage should be used for redistribution on top of taxes and transfers. I characterize optimal redistribution for a government with three policy instruments -- labor income taxes and transfers, corporate income taxes, and a minimum wage -- using an empirically grounded model of the labor market with positive firm profits. A minimum wage can increase social welfare when it increases the average post-tax wages of low-skill labor market participants and when corporate profit incidence is large. When chosen together with taxes, the minimum wage can help the government redistribute efficiently to low-skill workers by preventing firms from capturing low-wage income subsidies such as the EITC and from enjoying high profits that cannot be redistributed via corporate taxes due to capital mobility in unaffected industries. Event studies show that the average US state-level minimum wage reform over the last two decades increased average post-tax wages of low-skilled labor market participants and reduced corporate profits in affected industries, namely low-skill labor-intensive services. A sufficient statistics analysis implies that US minimum wages typically remain below their optimum under the current tax and transfer system.
Instability of financial markets by optimizing investment strategies investigated by an agent-based model
Authors: Takanobu Mizuta, Isao Yagi, Kosei Takashima
Date: 2022-02-02 00:00:00

Most finance studies are discussed on the basis of several hypotheses, for example, investors rationally optimize their investment strategies. However, the hypotheses themselves are sometimes criticized. Market impacts, where trades of investors can impact and change market prices, making optimization impossible. In this study, we built an artificial market model by adding technical analysis strategy agents searching one optimized parameter to a whole simulation run to the prior model and investigated whether investors' inability to accurately estimate market impacts in their optimizations leads to optimization instability. In our results, the parameter of investment strategy never converged to a specific value but continued to change. This means that even if all other traders are fixed, only one investor will use backtesting to optimize his/her strategy, which leads to the time evolution of market prices becoming unstable. Optimization instability is one level higher than "non-equilibrium of market prices." Therefore, the time evolution of market prices produced by investment strategies having such unstable parameters is highly unlikely to be predicted and have stable laws written by equations. This nature makes us suspect that financial markets include the principle of natural uniformity and indicates the difficulty of building an equation model explaining the time evolution of prices.
Reconciling Trends in U.S. Male Earnings Volatility: Results from Survey and Administrative Data
Authors: Robert Moffitt, John Abowd, Christopher Bollinger, Michael Carr, Charles Hokayem, Kevin McKinney, Emily Wiemers, Sisi Zhang, James Ziliak
Date: 2022-02-01 00:00:00
Tasks: Survey

There is a large literature on earnings and income volatility in labor economics, household finance, and macroeconomics. One strand of that literature has studied whether individual earnings volatility has risen or fallen in the U.S. over the last several decades. There are strong disagreements in the empirical literature on this important question, with some studies showing upward trends, some showing downward trends, and some showing no trends. Some studies have suggested that the differences are the result of using flawed survey data instead of more accurate administrative data. This paper summarizes the results of a project attempting to reconcile these findings with four different data sets and six different data series--three survey and three administrative data series, including two which match survey respondent data to their administrative data. Using common specifications, measures of volatility, and other treatments of the data, four of the six data series show a lack of any significant long-term trend in male earnings volatility over the last 20-to-30+ years when differences across the data sets are properly accounted for. A fifth data series (the PSID) shows a positive net trend but small in magnitude. A sixth, administrative, data set, available only since 1998, shows no net trend 1998-2011 and only a small decline thereafter. Many of the remaining differences across data series can be explained by differences in their cross-sectional distribution of earnings, particularly differences in the size of the lower tail. We conclude that the data sets we have analyzed, which include many of the most important available, show little evidence of any significant trend in male earnings volatility since the mid-1980s.
The Impact of Connectivity on the Production and Diffusion of Knowledge
Authors: Gustavo Manso, Farzad Pourbabaee
Date: 2022-02-01 00:00:00

We study a social bandit problem featuring production and diffusion of knowledge. While higher connectivity enhances knowledge diffusion, it may reduce knowledge production as agents shy away from experimentation with new ideas and free ride on the observation of other agents. As a result, under some conditions, greater connectivity can lead to homogeneity and lower social welfare.
ISS2: An Extension of Iterative Source Steering Algorithm for Majorization-Minimization-Based Independent Vector Analysis
Authors: Rintaro Ikeshita, Tomohiro Nakatani
Date: 2022-02-02 00:00:00

A majorization-minimization (MM) algorithm for independent vector analysis optimizes a separation matrix $W = [w_1, \ldots, w_m]^h \in \mathbb{C}^{m \times m}$ by minimizing a surrogate function of the form $\mathcal{L}(W) = \sum_{i = 1}^m w_i^h V_i w_i - \log | \det W |^2$, where $m \in \mathbb{N}$ is the number of sensors and positive definite matrices $V_1,\ldots,V_m \in \mathbb{C}^{m \times m}$ are constructed in each MM iteration. For $m \geq 3$, no algorithm has been found to obtain a global minimum of $\mathcal{L}(W)$. Instead, block coordinate descent (BCD) methods with closed-form update formulas have been developed for minimizing $\mathcal{L}(W)$ and shown to be effective. One such BCD is called iterative projection (IP) that updates one or two rows of $W$ in each iteration. Another BCD is called iterative source steering (ISS) that updates one column of the mixing matrix $A = W^{-1}$ in each iteration. Although the time complexity per iteration of ISS is $m$ times smaller than that of IP, the conventional ISS converges slower than the current fastest IP (called $\text{IP}_2$) that updates two rows of $W$ in each iteration. We here extend this ISS to $\text{ISS}_2$ that can update two columns of $A$ in each iteration while maintaining its small time complexity. To this end, we provide a unified way for developing new ISS type methods from which $\text{ISS}_2$ as well as the conventional ISS can be immediately obtained in a systematic manner. Numerical experiments to separate reverberant speech mixtures show that our $\text{ISS}_2$ converges in fewer MM iterations than the conventional ISS, and is comparable to $\text{IP}_2$.
Multi-armed Bandits for Link Configuration in Millimeter-wave Networks
Authors: Yi Zhang, Robert W. Heath Jr
Date: 2022-02-02 00:00:00
Tasks: Multi-Armed Bandits

Establishing and maintaining millimeter-wave (mmWave) links is challenging due to the changing environment and the high sensibility of mmWave signal to user mobility and channel conditions. MmWave link configuration problems often involve a search for optimal system parameter under environmental uncertainties, from a finite set of alternatives that are supported by the system hardware and protocol. For example, beam sweeping aims at identifying the optimal beam(s) for data transmission from a discrete codebook. Selecting parameters such as the beam sweeping period and the beamwidth are crucial to achieving high overall system throughput. In this article, we motivate the use of the multi-armed bandit (MAB) framework to intelligently search out the optimal configuration when establishing the mmWave links. MAB is a reinforcement learning framework that guides a decision-maker to sequentially select one action from a set of actions. As an example, we show that within the MAB framework, the optimal beam sweeping period, beamwidth, and beam directions could be dynamically learned with sample-computational-efficient bandit algorithms. We conclude by highlighting some future research directions on enhancing mmWave link configuration design with MAB.
Low-Rank and Row-Sparse Decomposition for Joint DOA Estimation and Distorted Sensor Detection
Authors: Huiping Huang, Qi Liu, Hing Cheung So, Abdelhak M. Zoubir
Date: 2022-02-02 00:00:00

Distorted sensors could occur randomly and may lead to the breakdown of a sensor array system. We consider an array model within which a small number of sensors are distorted by unknown sensor gain and phase errors. With such an array model, the problem of joint direction-of-arrival (DOA) estimation and distorted sensor detection is investigated and the problem is formulated under the framework of low-rank and row-sparse decomposition. We derive an iteratively reweighted least squares (IRLS) algorithm to solve the resulting problem in both noiseless and noisy cases. The convergence property of the IRLS algorithm is analyzed by means of the monotonicity and boundedness of the objective function. Extensive simulations are conducted regarding parameter selection, convergence speed, computational complexity, and performances of DOA estimation as well as distorted sensor detection. Even though the IRLS algorithm is slightly worse than the alternating direction method of multipliers in detecting the distorted sensors, the results show that our approach outperforms several state-of-the-art techniques in terms of convergence speed, computational cost, and DOA estimation performance.
Brain Source Localization by Alternating Projection
Authors: Amir Adler, Mati Wax, Dimitrios Pantazis
Date: 2022-02-02 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

We present a novel solution to the problem of localizing magnetoencephalography (MEG) and electroencephalography (EEG) brain signals. The solution is sequential and iterative, and is based on minimizing the least-squares criterion by the Alternating Projection algorithm. Results from simulated and experimental MEG data from a human subject demonstrated robust performance, with consistently superior localization accuracy than scanning methods belonging to the beamformer and multiple-signal classification (MUSIC) families. Importantly, the proposed solution is more robust to forward model errors resulting from head rotations and translations, with a significant advantage in highly correlated sources.
Self Interference Management in In-Band Full-Duplex Systems
Authors: Hossein Mohammadi, Maryam Sabbaghian, Vuk Marojevic
Date: 2022-02-01 00:00:00
Tasks: Management

The evolution of wireless systems has led to a continuous increase in the demand for radio frequency spectrum. To address this issue, a technology that has received a lot of attention is In-Band Full-Duplex (IBFD). The interest in IBFD systems stems from its capability to simultaneously transmit and receive data in the same frequency. Cancelling the self interference (SI) from the transmitter to the collocated receiver plays a pivotal role in the performance of the system. There are two types of SI cancellation (SIC) approaches, passive and active. In this research, the focus is on active cancellation and, in particular, SIC in the digital domain. Among the direct and backscattered SI, the former has been studied for a long time; therefore, the backscatter is considered in this research and two SIC approaches are analyzed. The first achieves SIC through beamforming. This requires knowing the angle of the received SI to put the beam null-space in this direction. The second method removes SI by employing an Artificial Neural Networks (ANNs). Using an ANN, there is no need to know the direction of the SI. The neural network is trained with pilots which results in the network being able to separate the desired signal from the SI at the receiver. Bayesian Neural Networks show the importance of the weights and assign a parameter that facilitates ignoring the less significant ones. Through comparative simulations we demonstrate that the ANN-based SIC achieves equivalent bit error rate performance as two beamforming methods.
Term structure modelling with overnight rates beyond stochastic continuity
Authors: Claudio Fontana, Zorana Grbac, Thorsten Schmidt
Date: 2022-02-02 00:00:00

Overnight rates, such as the SOFR (Secured Overnight Financing Rate) in the US, are central to the current reform of interest rate benchmarks. A striking feature of overnight rates is the presence of jumps and spikes occurring at predetermined dates due to monetary policy interventions and liquidity constraints. This corresponds to stochastic discontinuities (i.e., discontinuities occurring at ex-ante known points in time) in their dynamics. In this work, we propose a term structure modelling framework based on overnight rates and characterize absence of arbitrage in a generalised Heath-Jarrow-Morton (HJM) setting. We extend the classical short-rate approach to accommodate stochastic discontinuities, developing a tractable setup driven by affine semimartingales. In this context, we show that simple specifications allow to capture stylized facts of the jump behavior of overnight rates. In a Gaussian setting, we provide explicit valuation formulas for bonds and caplets. Furthermore, we investigate hedging in the sense of local risk-minimization when the underlying term structures feature stochastic discontinuities.
Mitigating cold start problems in drug-target affinity prediction with interaction knowledge transferring
Authors: Tri Minh Nguyen, Thin Nguyen, Truyen Tran
Date: 2022-01-16 00:00:00
Tasks: BIG-bench Machine Learning, Drug Discovery, Transfer Learning

Motivation: Predicting the drug-target interaction is crucial for drug discovery as well as drug repurposing. Machine learning is commonly used in drug-target affinity (DTA) problem. However, machine learning model faces the cold-start problem where the model performance drops when predicting the interaction of a novel drug or target. Previous works try to solve the cold start problem by learning the drug or target representation using unsupervised learning. While the drug or target representation can be learned in an unsupervised manner, it still lacks the interaction information, which is critical in drug-target interaction. Results: To incorporate the interaction information into the drug and protein interaction, we proposed using transfer learning from chemical-chemical interaction (CCI) and protein-protein interaction (PPI) task to drug-target interaction task. The representation learned by CCI and PPI tasks can be transferred smoothly to the DTA task due to the similar nature of the tasks. The result on the drug-target affinity datasets shows that our proposed method has advantages compared to other pretraining methods in the DTA task.
Stochastic fluctuations promote ordered pattern formation of cells in the Notch-Delta signaling pathway
Authors: Madeline Galbraith, Federico Bocci, José N. Onuchic
Date: 2022-02-01 00:00:00

The Notch-Delta signaling pathway mediates cell differentiation implicated in many regulatory processes including spatiotemporal patterning in tissues by promoting alternate cell fates between neighboring cells. At the multicellular level, this "lateral inhibition" principle leads to checkerboard patterns of Sender and Receiver cells. While it is well known that stochasticity modulates cell fate specification, little is known about how stochastic fluctuations at the cellular level propagate during multicell pattern formation. Here, we model stochastic fluctuations in the Notch-Delta pathway in the presence of two different noise types - shot and white - for a multicell system. The results show that intermediate fluctuations reduce disorder and guide the multicell lattice toward more checkerboard like patterns. By further analyzing cell fate transition events, we demonstrate that intermediate noise amplitudes provide enough perturbation to facilitate "proofreading" of disordered patterns and cause cells to switch to their correct ordered state. Conversely, high noise can override environmental signals coming from neighboring cells and lead to switching between ordered and disordered patterns. Therefore, in analogy with spin glass systems, intermediate noise levels allow the multicell Notch system to escape frustrated patterns and relax towards the lower energy checkerboard pattern while at large noise levels the system is unable to find this ordered base of attraction.
An Age-dependent Feedback Control Model for Calcium and Reactive Oxygen Species in Yeast Cells
Authors: Weijiu Liu
Date: 2022-02-02 00:00:00

Calcium and reactive oxygen species (ROS) interact with each other and play an important role in cell signaling networks. Based on the existing mathematical models, we develop an age-dependent feedback control model to simulate the interaction. The model consists of three subsystems: cytosolic calcium dynamics, ROS generation from the respiratory chain in mitochondria, and mitochondrial energy metabolism. In the model, we hypothesized that ROS induces calcium release from the yeast endoplasmic reticulum , Golgi apparatus, and vacuoles, and that ROS damages calmodulin and calcineurin by oxidizing them. The dependence of calcium uptake by Vcx1p on ATP is incorporated into the model. The model can approximately reproduce the log phase calcium dynamics. The simulated interaction between the cytosolic calcium and mitochondrial ROS shows that an increase in calcium results in a decrease in ROS initially (in log phase), but the increase-decrease relation is changed to an increase-increase relation when the cell is getting old. This could accord with the experimental observation that calcium diminishes ROS from complexes I and III of the respiratory chain under normal conditions, but enhances ROS when the complex formations are inhibited. The model predicts that the subsystem of the calcium regulators Pmc1p, Pmr1p, and Vex1p is stable, controllable, and observable. These structural properties of the dynamical system could mathematically confirm that cells have evolved delicate feedback control mechanisms to maintain their calcium homeostasis.
Optimal fenestration of the Fontan circulation
Authors: Zan Ahmad, Lynn H. Jin, Daniel J. Penny, Craig G. Rusin, Charles S. Peskin, Charles Puelz
Date: 2022-02-02 00:00:00

In this paper, we develop a pulsatile compartmental model of the Fontan circulation and use it to explore the effects of a fenestration added to this physiology. A fenestration is a shunt between the systemic and pulmonary veins that is added either at the time of Fontan conversion or at a later time for the treatment of complications. This shunt increases cardiac output and decreases systemic venous pressure. However, these hemodynamic benefits are achieved at the expense of a decrease in the arterial oxygen saturation. The model developed this paper incorporates fenestration size as a parameter and describes both blood flow and oxygen transport. It is calibrated to clinical data from Fontan patients, and we use it to study the impact of a fenestration on several hemodynamic variables. In certain scenarios corresponding to high-risk Fontan physiology, we demonstrate the existence of an optimal fenestration size that maximizes oxygen delivery to the systemic tissues.
A semi-static replication approach to efficient hedging and pricing of callable IR derivatives
Authors: Jori Hoencamp, Shashi Jain, Drona Kandhai
Date: 2022-02-02 00:00:00

We present a semi-static hedging algorithm for callable interest rate derivatives under an affine, multi-factor term-structure model. With a traditional dynamic hedge, the replication portfolio needs to be updated continuously through time as the market moves. In contrast, we propose a semi-static hedge that needs rebalancing on just a finite number of instances. We show, taking as an example Bermudan swaptions, that callable interest rate derivatives can be replicated with an options portfolio written on a basket of discount bonds. The static portfolio composition is obtained by regressing the target option's value using an interpretable, artificial neural network. Leveraging on the approximation power of neural networks, we prove that the hedging error can be arbitrarily small for a sufficiently large replication portfolio. A direct, a lower bound, and an upper bound estimator for the risk-neutral Bermudan swaption price is inferred from the hedging algorithm. Additionally, closed-form error margins to the price statistics are determined. We practically demonstrate the hedging and pricing performance through several numerical experiments.