金融/语音/音频处理学术速递[6.24]

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q-fin金融，共计7篇

cs.SD语音，共计3篇

eess.AS音频处理，共计2篇

1.q-fin金融:

【1】 Chebyshev Greeks: Smoothing Gamma without Bias 标题：契比雪夫希腊人：没有偏见地平滑伽马

作者：Andrea Maran,Andrea Pallavicini,Stefano Scoleri 备注：15 pages, 4 figures 链接：https://arxiv.org/abs/2106.12431 摘要：希腊语的计算是金融工具风险管理的一项基本任务。数值计算的标准方法是有限差分法。大多数奇异衍生品都是通过蒙特卡罗模拟定价的：在这种情况下，很难找到一个快速准确的希腊近似值，主要是因为需要在偏差和方差之间进行权衡。最近希腊计算的改进，如伴随算法微分，不幸的是，对二阶希腊（如Gamma）无效，后者受到最显著的不稳定性的困扰，因此仍然缺乏一种可行的替代标准有限差分的方法。我们将切比雪夫插值技术应用于spot-green的计算中，以一种简单而通用的方式说明了如何提高任意阶有限差分green的稳定性。对金融机构经常交易的一些实际收益，分析了所提出的技术所提高的性能。 摘要：The computation of Greeks is a fundamental task for risk managing of financial instruments. The standard approach to their numerical evaluation is via finite differences. Most exotic derivatives are priced via Monte Carlo simulation: in these cases, it is hard to find a fast and accurate approximation of Greeks, mainly because of the need of a tradeoff between bias and variance. Recent improvements in Greeks computation, such as Adjoint Algorithmic Differentiation, are unfortunately uneffective on second order Greeks (such as Gamma), which are plagued by the most significant instabilities, so that a viable alternative to standard finite differences is still lacking. We apply Chebyshev interpolation techniques to the computation of spot Greeks, showing how to improve the stability of finite difference Greeks of arbitrary order, in a simple and general way. The increased performance of the proposed technique is analyzed for a number of real payoffs commonly traded by financial institutions.

【2】 Portfolio Allocation under Asymmetric Dependence in Asset Returns using Local Gaussian Correlations 标题：基于局部高斯相关的资产收益非对称依赖下的投资组合配置

作者：Anders D. Sleire,Bård Støve,Håkon Otneim,Geir Drage Berentsen,Dag Tjøstheim,Sverre Hauso Haugen 链接：https://arxiv.org/abs/2106.12425 摘要：众所周知，金融收益之间存在着不对称的依赖结构。在本文中，我们使用一种新的非参数的局部相关性度量，即局部高斯相关性来改进投资组合的配置。我们扩展了经典的均值-方差框架，并证明使用我们的新方法，投资组合优化是简单的，只依赖于一个调整参数（带宽）。对于月度资产收益数据，新方法的表现优于等权（1/N）投资组合和经典Markowitz投资组合。 摘要：It is well known that there are asymmetric dependence structures between financial returns. In this paper we use a new nonparametric measure of local dependence, the local Gaussian correlation, to improve portfolio allocation. We extend the classical mean-variance framework, and show that the portfolio optimization is straightforward using our new approach, only relying on a tuning parameter (the bandwidth). The new method is shown to outperform the equally weighted (1/N) portfolio and the classical Markowitz portfolio for monthly asset returns data.

【3】 From Bachelier to Dupire via Optimal Transport 标题：通过最优运输从巴舍利耶到杜皮尔

作者：Mathias Beiglböck,Gudmund Pammer,Walter Schachermayer 链接：https://arxiv.org/abs/2106.12395 摘要：著名的数学金融学是由Bachelier在他1900年的博士论文中开始的，在他的许多其他成就中，他还提供了Kolmogorov正演方程的形式化推导。这也形成了杜皮尔（再次正式）解决问题的基础，即找到一个校准到波动率表面的无套利模型。后者的结果与Kellerer和Lowther的定理有严格的对应关系。在这篇调查文章中，我们重温了这些随机金融的特征，强调了一些最优运输结果在这方面所起的作用。 摘要：Famously mathematical finance was started by Bachelier in his 1900 PhD thesis where - among many other achievements - he also provides a formal derivation of the Kolmogorov forward equation. This forms also the basis for Dupire's (again formal) solution to the problem of finding an arbitrage free model calibrated to the volatility surface. The later result has rigorous counterparts in the theorems of Kellerer and Lowther. In this survey article we revisit these hallmarks of stochastic finance, highlighting the role played by some optimal transport results in this context.

【4】 More stochastic expansions for the pricing of vanilla options with cash dividends 标题：带现金红利的香草期权定价的更多随机展开式

作者：Fabien Le Floc'h 机构： 36lefloch˙expansion˙cash˙dividendMore stochastic expansions for the pricing of vanillaoptions with cash dividendsFabien Le Floc’hDelft Institute of Applied Mathematics, Delft InstituteofApplied Mathematics 链接：https://arxiv.org/abs/2106.12051 摘要：在标的资产价格服从逐段对数正态过程且除息日跳变的模型下，对于具有离散现金股利的欧式期权定价没有精确的封闭式公式。本文提出了一种基于Etore和Gobet技术的交替展开方法，使得一阶、二阶和三阶展开在罢工范围和分红日期范围内更为稳健。 摘要：There is no exact closed form formula for pricing of European options with discrete cash dividends under the model where the underlying asset price follows a piecewise lognormal process with jumps at dividend ex-dates. This paper presents alternative expansions based on the technique of Etore and Gobet, leading to more robust first, second and third-order expansions across the range of strikes and the range of dividend dates.

【5】 Pricing American options with the Runge-Kutta-Legendre finite difference scheme 标题：Runge-Kutta-Legendre有限差分格式的美式期权定价

作者：Fabien Le Floc'h 链接：https://arxiv.org/abs/2106.12049 摘要：本文提出了Runge-Kutta-Legendre有限差分格式，并考虑了其多项式表示的额外移位。与Runge-Kutta-Chebyshev格式相比，下面简要介绍了稳定域。然后我们研究了单因素Black-Scholes和双因素Heston随机波动模型下的Runge-Kutta-Legendre格式美式期权定价问题，以及不确定波动模型下的蝴蝶价差和数字期权定价问题，这里需要解一个Hamilton-Jacobi-Bellman偏微分方程。与文献和流行的Crank-Nicolson等具有Rannacher时间步的方案相比，我们探讨了这些问题的收敛顺序以及稳定性。 摘要：This paper presents the Runge-Kutta-Legendre finite difference scheme, allowing for an additional shift in its polynomial representation. A short presentation of the stability region, comparatively to the Runge-Kutta-Chebyshev scheme follows. We then explore the problem of pricing American options with the Runge-Kutta-Legendre scheme under the one factor Black-Scholes and the two factor Heston stochastic volatility models, as well as the pricing of butterfly spread and digital options under the uncertain volatility model, where a Hamilton-Jacobi-Bellman partial differential equation needs to be solved. We explore the order of convergence in these problems, as well as the option greeks stability, compared to the literature and popular schemes such as Crank-Nicolson, with Rannacher time-stepping.

【6】 Bailouts in Financial Networks 标题：金融网络中的救助

作者：Beni Egressy,Roger Wattenhofer 链接：https://arxiv.org/abs/2106.12315 摘要：我们考虑拥有资产和负债的银行网络。一些银行可能已经资不抵债，中央银行可以决定救助哪些资不抵债的银行（如果有的话）。我们将救助视为一个优化问题，中央银行在这个问题上提供了可支配的资源，并希望实现最大化的目标。我们证明了在不同的假设条件下，对于不同的自然目标，这个优化问题是NP困难的，在某些情况下甚至很难逼近。此外，我们还表明，在给定一个固定的央行救助目标的情况下，网络中的银行可以签订新的债务合同，以在发生救助时增加自身的市场价值（以央行为代价）。 摘要：We consider networks of banks with assets and liabilities. Some banks may be insolvent, and a central bank can decide which insolvent banks, if any, to bail out. We view bailouts as an optimization problem where the central bank has given resources at its disposal and an objective it wants to maximize. We show that under various assumptions and for various natural objectives this optimization problem is NP-hard, and in some cases even hard to approximate. Furthermore, we also show that given a fixed central bank bailout objective, banks in the network can make new debt contracts to increase their own market value in the event of a bailout (at the expense of the central bank).

【7】 The Effectiveness of Strategies to Contain SARS-CoV-2: Testing, Vaccinations, and NPIs 标题：控制SARS-CoV-2策略的有效性：检测、疫苗接种和NPIs

作者：Janoś Gabler,Tobias Raabe,Klara Röhrl,Hans-Martin von Gaudecker 机构：a Bonn Graduate School of Economics, b IZA Institute of Labor Economics, c Private sector, d Rheinische Friedrich-Wilhelms-Universität Bonn 链接：https://arxiv.org/abs/2106.11129 摘要：为了减缓CoViD-19大流行的传播，世界各国政府制定了一系列限制该疾病传播的政策。最初，这些措施侧重于非药物干预；最近，疫苗接种和大规模快速检测开始发挥重要作用。这项研究的目的是解释这些政策在决定大流行过程中的数量效应，考虑到季节性或具有不同传播模式的病毒株等因素。为此，该研究开发了一个基于代理的模拟模型，该模型是利用德国第二波和第三波CoViD-19大流行的数据进行估计的。研究发现，在疫苗接种率从5%上升到40%的时期，大规模快速检测对减少感染人数的作用最大。频繁的大规模快速检测仍然是控制CoViD-19的策略的一部分；它可以替代许多对个人、社会和经济造成更大代价的非药物干预措施。 摘要：In order to slow the spread of the CoViD-19 pandemic, governments around the world have enacted a wide set of policies limiting the transmission of the disease. Initially, these focused on non-pharmaceutical interventions; more recently, vaccinations and large-scale rapid testing have started to play a major role. The objective of this study is to explain the quantitative effects of these policies on determining the course of the pandemic, allowing for factors like seasonality or virus strains with different transmission profiles. To do so, the study develops an agent-based simulation model, which is estimated using data for the second and the third wave of the CoViD-19 pandemic in Germany. The paper finds that during a period where vaccination rates rose from 5% to 40%, large-scale rapid testing had the largest effect on reducing infection numbers. Frequent large-scale rapid testing should remain part of strategies to contain CoViD-19; it can substitute for many non-pharmaceutical interventions that come at a much larger cost to individuals, society, and the economy.

2.cs.SD语音:

【1】 Unsupervised Speech Enhancement using Dynamical Variational Auto-Encoders 标题：基于动态变分自动编码器的无监督语音增强

作者：Xiaoyu Bie,Simon Leglaive,Xavier Alameda-Pineda,Laurent Girin 链接：https://arxiv.org/abs/2106.12271 摘要：动态变分自动编码器（dynamicvariationauto-encoders，DVAEs）是一类具有潜变量的深生成模型，用于时间序列数据建模。DVAEs可以看作是变分自编码器（VAE）的扩展，包括对数据序列中连续观测向量和/或潜在向量之间的时间依赖性的建模。以往的研究表明，DVAEs在语音信号（谱图）建模中有着广泛的应用前景，其性能优于VAE。独立地，VAE已经成功地应用于噪声中的语音增强，在无监督的噪声不可知设置中，不需要使用干净和有噪声语音样本的并行数据集进行训练，而只需要干净的语音信号。在本文中，我们将这些工作扩展到基于DVAE的单通道无监督语音增强，从而开发了语音信号的无监督表示学习和动力学建模。我们提出了一种基于DVAEs最一般形式的无监督语音增强算法，并将其应用于三种特定的DVAE模型，以说明该框架的通用性。更准确地说，我们将基于DVAE的语音先验知识与基于非负矩阵分解的噪声模型相结合，提出了一种变分期望最大化（VEM）算法来进行语音增强。实验结果表明，基于DVAEs的语音增强方法优于VAE算法和有监督的语音增强基线。 摘要：Dynamical variational auto-encoders (DVAEs) are a class of deep generative models with latent variables, dedicated to time series data modeling. DVAEs can be considered as extensions of the variational autoencoder (VAE) that include the modeling of temporal dependencies between successive observed and/or latent vectors in data sequences. Previous work has shown the interest of DVAEs and their better performance over the VAE for speech signals (spectrogram) modeling. Independently, the VAE has been successfully applied to speech enhancement in noise, in an unsupervised noise-agnostic set-up that does not require the use of a parallel dataset of clean and noisy speech samples for training, but only requires clean speech signals. In this paper, we extend those works to DVAE-based single-channel unsupervised speech enhancement, hence exploiting both speech signals unsupervised representation learning and dynamics modeling. We propose an unsupervised speech enhancement algorithm based on the most general form of DVAEs, that we then adapt to three specific DVAE models to illustrate the versatility of the framework. More precisely, we combine DVAE-based speech priors with a noise model based on nonnegative matrix factorization, and we derive a variational expectation-maximization (VEM) algorithm to perform speech enhancement. Experimental results show that the proposed approach based on DVAEs outperforms its VAE counterpart and a supervised speech enhancement baseline.

【2】 Deep Neural Network Based Respiratory Pathology Classification Using Cough Sounds 标题：基于深度神经网络的咳嗽音呼吸系统病理分类

作者：Balamurali B T,Hwan Ing Hee,Saumitra Kapoor,Oon Hoe Teoh,Sung Shin Teng,Khai Pin Lee,Dorien Herremans,Jer Ming Chen 机构：����������, Citation: Lastname, F.; Lastname, F.;, Lastname, F. Title. Preprints ,. 链接：https://arxiv.org/abs/2106.12174 摘要：智能系统正在改变世界，也在改变我们的医疗体系。我们提出了一个基于深度学习的咳嗽音分类模型，可以区分哮喘、上呼吸道感染（URTI）和下呼吸道感染（LRTI）等健康咳嗽和病理性咳嗽的儿童。为了训练一个深层神经网络模型，我们收集了一个新的咳嗽声数据集，标记了临床医生的诊断。所选择的模型是基于Mel倒谱系数（mfcc）特征的双向长短时记忆网络（BiLSTM）。当对健康或病理（一般或属于特定的呼吸病理）两类咳嗽进行分类时，得到的训练模型在根据医生诊断提供的标签对咳嗽进行分类时达到了84%以上的准确率。为了对受试者的呼吸病理状况进行分类，将每个受试者的多个咳嗽时期的结果结合起来。三种呼吸疾病的预测准确率均超过91%。然而，当模型被训练来分类和区分四类咳嗽时，总体准确率下降：一类病理性咳嗽常常被误分类为另一类。然而，如果将健康咳嗽分为健康咳嗽和病理咳嗽分为某些病理类型，则四类模型的总体准确率在84%以上。对MFCC特征空间的纵向研究表明，病理性咳嗽与恢复性咳嗽在同一个受试者身上所占的特征空间是相同的，因此仅用MFCC特征很难区分。 摘要：Intelligent systems are transforming the world, as well as our healthcare system. We propose a deep learning-based cough sound classification model that can distinguish between children with healthy versus pathological coughs such as asthma, upper respiratory tract infection (URTI), and lower respiratory tract infection (LRTI). In order to train a deep neural network model, we collected a new dataset of cough sounds, labelled with clinician's diagnosis. The chosen model is a bidirectional long-short term memory network (BiLSTM) based on Mel Frequency Cepstral Coefficients (MFCCs) features. The resulting trained model when trained for classifying two classes of coughs -- healthy or pathology (in general or belonging to a specific respiratory pathology), reaches accuracy exceeding 84\% when classifying cough to the label provided by the physicians' diagnosis. In order to classify subject's respiratory pathology condition, results of multiple cough epochs per subject were combined. The resulting prediction accuracy exceeds 91\% for all three respiratory pathologies. However, when the model is trained to classify and discriminate among the four classes of coughs, overall accuracy dropped: one class of pathological coughs are often misclassified as other. However, if one consider the healthy cough classified as healthy and pathological cough classified to have some kind of pathologies, then the overall accuracy of four class model is above 84\%. A longitudinal study of MFCC feature space when comparing pathological and recovered coughs collected from the same subjects revealed the fact that pathological cough irrespective of the underlying conditions occupy the same feature space making it harder to differentiate only using MFCC features.

【3】 Enrollment-less training for personalized voice activity detection 标题：用于个性化语音活动检测的免注册训练

作者：Naoki Makishima,Mana Ihori,Tomohiro Tanaka,Akihiko Takashima,Shota Orihashi,Ryo Masumura 机构：NTT Media Intelligence Laboratories, NTT Corporation, Japan 备注：Accepted to INTERSPEECH 2021 链接：https://arxiv.org/abs/2106.12132 摘要：我们提出了一种新的个性化语音活动检测（PVAD）学习方法，在训练过程中不需要注册数据。PVAD是利用目标说话人的注册语音在帧级检测特定目标说话人的语音片段的任务。由于PVAD必须学习说话人的语音变化以明确说话人之间的界限，因此对PVAD的研究使用了包含每个说话人许多话语的大规模数据集。然而，用于训练PVAD模型的数据集通常是有限的，因为准备这样的数据集需要大量的成本。此外，我们不能利用用于训练标准VAD的数据集，因为它们通常缺少说话人标签。为了解决这些问题，我们的核心思想是在训练过程中同时使用一个语音作为注册语音和PVAD的输入，使得PVAD训练不需要注册语音。在我们提出的无注册训练方法中，我们在保持说话人身份的同时，增加一个话语，从而在输入和注册语音之间产生可变性，从而避免了训练和推理之间的不匹配。实验结果证明了该方法的有效性。 摘要：We present a novel personalized voice activity detection (PVAD) learning method that does not require enrollment data during training. PVAD is a task to detect the speech segments of a specific target speaker at the frame level using enrollment speech of the target speaker. Since PVAD must learn speakers' speech variations to clarify the boundary between speakers, studies on PVAD used large-scale datasets that contain many utterances for each speaker. However, the datasets to train a PVAD model are often limited because substantial cost is needed to prepare such a dataset. In addition, we cannot utilize the datasets used to train the standard VAD because they often lack speaker labels. To solve these problems, our key idea is to use one utterance as both a kind of enrollment speech and an input to the PVAD during training, which enables PVAD training without enrollment speech. In our proposed method, called enrollment-less training, we augment one utterance so as to create variability between the input and the enrollment speech while keeping the speaker identity, which avoids the mismatch between training and inference. Our experimental results demonstrate the efficacy of the method.

3.eess.AS音频处理:

【1】 Unsupervised Speech Enhancement using Dynamical Variational Auto-Encoders 标题：基于动态变分自动编码器的无监督语音增强

作者：Xiaoyu Bie,Simon Leglaive,Xavier Alameda-Pineda,Laurent Girin 链接：https://arxiv.org/abs/2106.12271 摘要：动态变分自动编码器（dynamicvariationauto-encoders，DVAEs）是一类具有潜变量的深生成模型，用于时间序列数据建模。DVAEs可以看作是变分自编码器（VAE）的扩展，包括对数据序列中连续观测向量和/或潜在向量之间的时间依赖性的建模。以往的研究表明，DVAEs在语音信号（谱图）建模中有着广泛的应用前景，其性能优于VAE。独立地，VAE已经成功地应用于噪声中的语音增强，在无监督的噪声不可知设置中，不需要使用干净和有噪声语音样本的并行数据集进行训练，而只需要干净的语音信号。在本文中，我们将这些工作扩展到基于DVAE的单通道无监督语音增强，从而开发了语音信号的无监督表示学习和动力学建模。我们提出了一种基于DVAEs最一般形式的无监督语音增强算法，并将其应用于三种特定的DVAE模型，以说明该框架的通用性。更准确地说，我们将基于DVAE的语音先验知识与基于非负矩阵分解的噪声模型相结合，提出了一种变分期望最大化（VEM）算法来进行语音增强。实验结果表明，基于DVAEs的语音增强方法优于VAE算法和有监督的语音增强基线。 摘要：Dynamical variational auto-encoders (DVAEs) are a class of deep generative models with latent variables, dedicated to time series data modeling. DVAEs can be considered as extensions of the variational autoencoder (VAE) that include the modeling of temporal dependencies between successive observed and/or latent vectors in data sequences. Previous work has shown the interest of DVAEs and their better performance over the VAE for speech signals (spectrogram) modeling. Independently, the VAE has been successfully applied to speech enhancement in noise, in an unsupervised noise-agnostic set-up that does not require the use of a parallel dataset of clean and noisy speech samples for training, but only requires clean speech signals. In this paper, we extend those works to DVAE-based single-channel unsupervised speech enhancement, hence exploiting both speech signals unsupervised representation learning and dynamics modeling. We propose an unsupervised speech enhancement algorithm based on the most general form of DVAEs, that we then adapt to three specific DVAE models to illustrate the versatility of the framework. More precisely, we combine DVAE-based speech priors with a noise model based on nonnegative matrix factorization, and we derive a variational expectation-maximization (VEM) algorithm to perform speech enhancement. Experimental results show that the proposed approach based on DVAEs outperforms its VAE counterpart and a supervised speech enhancement baseline.

【2】 Enrollment-less training for personalized voice activity detection 标题：用于个性化语音活动检测的免注册训练

作者：Naoki Makishima,Mana Ihori,Tomohiro Tanaka,Akihiko Takashima,Shota Orihashi,Ryo Masumura 机构：NTT Media Intelligence Laboratories, NTT Corporation, Japan 备注：Accepted to INTERSPEECH 2021 链接：https://arxiv.org/abs/2106.12132 摘要：我们提出了一种新的个性化语音活动检测（PVAD）学习方法，在训练过程中不需要注册数据。PVAD是利用目标说话人的注册语音在帧级检测特定目标说话人的语音片段的任务。由于PVAD必须学习说话人的语音变化以明确说话人之间的界限，因此对PVAD的研究使用了包含每个说话人许多话语的大规模数据集。然而，用于训练PVAD模型的数据集通常是有限的，因为准备这样的数据集需要大量的成本。此外，我们不能利用用于训练标准VAD的数据集，因为它们通常缺少说话人标签。为了解决这些问题，我们的核心思想是在训练过程中同时使用一个语音作为注册语音和PVAD的输入，使得PVAD训练不需要注册语音。在我们提出的无注册训练方法中，我们在保持说话人身份的同时，增加一个话语，从而在输入和注册语音之间产生可变性，从而避免了训练和推理之间的不匹配。实验结果证明了该方法的有效性。 摘要：We present a novel personalized voice activity detection (PVAD) learning method that does not require enrollment data during training. PVAD is a task to detect the speech segments of a specific target speaker at the frame level using enrollment speech of the target speaker. Since PVAD must learn speakers' speech variations to clarify the boundary between speakers, studies on PVAD used large-scale datasets that contain many utterances for each speaker. However, the datasets to train a PVAD model are often limited because substantial cost is needed to prepare such a dataset. In addition, we cannot utilize the datasets used to train the standard VAD because they often lack speaker labels. To solve these problems, our key idea is to use one utterance as both a kind of enrollment speech and an input to the PVAD during training, which enables PVAD training without enrollment speech. In our proposed method, called enrollment-less training, we augment one utterance so as to create variability between the input and the enrollment speech while keeping the speaker identity, which avoids the mismatch between training and inference. Our experimental results demonstrate the efficacy of the method.