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CS代写|计算机网络代写Computer Networking代考|Contagion in Interbank Networks

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CS代写|计算机网络代写Computer Networking代考|Contagion in Interbank Networks

CS代写|计算机网络代写Computer Networking代考|Introduction

Financial intermediation serves the purpose of reallocating funds from the net savers to the net borrowers of the economy. Without financial intermediation, economic entities with excess funds would have difficulties finding and providing financing to other economic agents in need of funds. As a result of its role in intermediating between savers and borrowers, the financial system consists of a large number of interlinkages. Financial transactions thus create links both between financial intermediaries (i.e., banks and other financial institutions) and the nonfinancial sectors of the economy (i.e., households, firms, government entities), and also among financial intermediaries inside the financial system. While it is generally acknowledged that the financial system-at least in normal times-helps smooth the wheels of the economy by making the resource allocation more efficient (as financial intermediaries are specialised in and have comparative advantages in reallocating savings), this intermediation process can be disrupted when, for example, key financial institutions get into trouble or when macroeconomic and asset price shocks affect the financial system as a whole. Such adverse shocks can lead to cascading contagion effects throughout the financial system due to the many, and often highly complex, interrelationships that exist between the actors in the financial system. Disruptions to the provision of financial services can in turn have serious implications for the real economy.
For theses reasons, there has been increasing interest among economists and policy makers to understand and measure the risks posed by the complex system of financial interrelations characterising the modern economy. In order to be able to identify, assess, and possibly address the potential contagion risks existing within the financial system, network-based models have proved particularly useful. In this light, and especially triggered by the financial crisis erupting in 2007, an extensive literature on the contagion analysis using network theory and modeling has emerged (see also Section 0.2 later for a survey of some of the most recent studies). The approaches to analyzing financial networks have often relied on network-based tools developed in other sciences, such as biology, physics, and medicine. Compared to many other sciences, network applications in finance are constrained by the considerable computational challenges related to the fact that sufficiently granular data on financial networks are often not available. Moreover, also in contrast to some other sciences, it is difficult to construct realistic counterfactual simulations that are able to fully capture the multi-layered and dynamic complexity characterising financial networks in the real world.
In order to shed light on some of the key computational issues in financial networks, in this chapter, we demonstrate two recent applications related to the interbank networks. The illustrations serve to highlight approaches to overcome the computational issues related to the limited data availability and the highly complex dynamic interactions underlying the financial interrelations (here exemplified with applications for the interbank market). We furthermore demonstrate how regulatory measures can be employed to contain contagion risk embedded in the interbank market structures, to the extent that prudential actions can be shown-using network-based models-to be effective in pulling the interbank network structures in a direction that makes them more resilient, such network applications can be useful for policy purposes; for example, to help calibrating prudential policy measures.

CS代写|计算机网络代写Computer Networking代考|Research Context

The recent crisis events have highlighted the systemic risks to the financial system of individual bank failures via the interlinkages that exist between banks; especially in the unsecured interbank market. Particular attention has been paid to the potential counterparty risks banks are exposed to via their bilateral interbank exposures. $\stackrel{2}{2}$ This, in turn has led to a flurry of academic research to help understand, measure, and assess the impact of contagion within the network of banks and other institutions that constitute the financial system. In addition, a number of policy initiatives have been introduced in recent years to counter the potential contagion risks of the interlinked banking networks; especially exemplified by the additional capital requirements on globally systemic institutions (G-SIBs).
The academic literature analyzing financial contagion has followed different strands. One area of research has focused on capturing contagion using financial market data. Kodres and Pritsker (2002) provides a theoretical model, whereby in an environment of shared macroeconomic risks and asymmetric information, asset price contagion can occur even under the assumption of rational expectations. On the empirical side, some early studies attempted to capture contagion using event studies to detect the impact of bank failures on stock (or debt) prices of other banks in the system. ${ }^3$ The evidence from these studies was, however, rather mixed. This may be due to the fact that stock price reactions typically observed during normal periods do not capture well the non-linear and more extreme asset price movements typically observed during periods of systemic events where large-scale contagion effects could be expected. In this light, some more recent market data studies have applied extreme-value theory to better capture such extraordinary events. ${ }^4$ In a similar vein, Polson and Scott (2011) apply an explosive volatility model to capture stock market contagion measured by excess cross-sectional correlations. Stock market and CDS spread correlations were investigated with network-based techniques by Emmert-Streib and Drehmer (2010), and Peltonen, Scheicher, and Vuillemey (2013). Other studies have tried to capture the conditional spillover probabilities at the tail of the distribution by using quantile regressions. $\stackrel{5}{ }$ Diebold and Yilmaz (2011) proposes in turn to use variance decompositions as connectedness measures to construct networks among financial institutions based on market data.

A different strand of the literature has been based on balance sheet exposures (such as interbank exposures and bank capital) with the aim of conducting counterfactual simulations of the potential effects on the network of exposures if one or more financial institutions encounter problems. This may overcome some of the deficiencies of the market data-based literature, such as the fact that asset prices can be subject to periods of significant mis-pricing, which may distort the signals retrieved from the analysis. The starting point to analyze bank contagion risks and interconnectedness on the basis of balance sheet data is having reliable information on the interbank networks. One can view a financial exposure or liability within a network as a relationship (or edge) of an institution (node) vis-à-vis another, whereby the relationship portrays a potential channel of shock transmission among the institutions. Mutual exposures of financial intermediaries are generally beneficial as they allow for a more efficient allocation of financial assets and liabilities, and are a sign of better diversified financial institutions. $\underline{6}$ At the same time, when large shocks hit the financial system, financial networks-especially if exposures are concentrated among a few main players-can act as an accelerator of the shock’s initial impact by propagating it throughout the financial system via network links. As emphasized by Allen and Gale (2000), the underlying structure of the network determines how vulnerable it is to contagion. – For example, Allen and Gale (2000) emphasize the contagion risk prevailing in complete networks, that is, those having $\left(\begin{array}{c}N \ 2\end{array}\right)$ linkages, where $N$ is the number of nodes. $\frac{8}{}$ It is, furthermore, emphasized in the literature that in the presence of asymmetric information about the quality of counterparties and of the underlying collateral, adverse selection problems may arise which can render the interbank networks dysfunctional in periods of distress. $\underline{9}$

CS代写|计算机网络代写Computer Networking代考|Contagion in Interbank Networks

计算机网络代写

CS代写|计算机网络代写Computer Networking代考|Introduction

金融中介的目的是将资金从净储蓄者重新分配给经济中的净借款人。如果没有金融中介,资金过剩的经济实体将难以找到和向需要资金的其他经济主体提供融资。由于其在存款人和借款人之间扮演中介角色,金融体系由大量的相互联系组成。因此,金融交易在金融中介机构(即银行和其他金融机构)和经济的非金融部门(即家庭、公司、政府实体)之间以及金融体系内的金融中介机构之间建立了联系。虽然人们普遍承认,金融体系——至少在正常时期——通过使资源配置更有效(因为金融中介机构专门从事储蓄再配置并具有相对优势)帮助平稳经济车轮,但当关键金融机构陷入困境或宏观经济和资产价格冲击影响整个金融体系时,这一中介过程可能会中断。由于金融体系中参与者之间存在许多且往往高度复杂的相互关系,这种不利冲击可能导致整个金融体系的级联传染效应。金融服务供应中断可能反过来对实体经济产生严重影响。
由于这些原因,经济学家和政策制定者对理解和衡量现代经济中金融相互关系的复杂系统所带来的风险越来越感兴趣。为了能够识别、评估并可能解决金融体系中存在的潜在传染风险,基于网络的模型已被证明特别有用。在这种情况下,尤其是在2007年爆发的金融危机的触发下,使用网络理论和模型进行传染分析的大量文献已经出现(参见稍后的第0.2节对一些最新研究的调查)。分析金融网络的方法通常依赖于其他科学中开发的基于网络的工具,如生物学、物理学和医学。与许多其他科学相比,金融领域的网络应用受到相当大的计算挑战的限制,这些挑战与金融网络上通常无法获得足够细粒度的数据有关。此外,与其他一些科学相比,很难构建能够完全捕捉现实世界中金融网络特征的多层和动态复杂性的现实反事实模拟。
为了阐明金融网络中的一些关键计算问题,在本章中,我们展示了与银行间网络相关的两个最新应用。这些插图突出了克服与有限的数据可用性和高度复杂的动态交互相关的计算问题的方法,这些交互是金融相互关系的基础(这里以银行间市场的应用为例)。我们进一步展示了如何采用监管措施来遏制嵌入在银行间市场结构中的传染风险,在某种程度上,可以显示审慎行动-使用基于网络的模型-有效地将银行间网络结构拉向使其更具弹性的方向,此类网络应用可用于政策目的;例如,帮助校准审慎的政策措施。

CS代写|计算机网络代写Computer Networking代考|Research Context

最近的危机事件凸显了银行间存在的相互联系给金融体系带来的单个银行倒闭的系统性风险;尤其是在无担保的银行间市场。特别关注的是银行通过双边银行间风险敞口所面临的潜在交易对手风险。$\stackrel{2}{2}$这反过来又引发了一系列学术研究,以帮助理解、衡量和评估在构成金融体系的银行和其他机构网络内传染的影响。此外,近年来出台了一些政策举措,以应对相互关联的银行网络的潜在传染风险;特别是对全球系统性机构(g – sib)的额外资本要求。
分析金融传染的学术文献遵循了不同的思路。一个研究领域侧重于利用金融市场数据捕捉传染。Kodres和Pritsker(2002)提供了一个理论模型,即在宏观经济风险共享和信息不对称的环境下,即使假设存在理性预期,资产价格传染也会发生。在实证方面,一些早期研究试图通过事件研究来捕捉传染,以检测银行倒闭对系统中其他银行股票(或债务)价格的影响。${ }^3$然而,来自这些研究的证据相当复杂。这可能是因为通常在正常时期观察到的股票价格反应不能很好地捕捉到通常在系统性事件期间观察到的非线性和更极端的资产价格变动,而系统性事件可能会产生大规模的传染效应。有鉴于此,最近的一些市场数据研究应用了极值理论来更好地捕捉这类非同寻常的事件。${ }^4$与此类似,Polson和Scott(2011)采用爆炸性波动模型来捕捉股市传染,该模型通过过度横断面相关性来衡量。Emmert-Streib和Drehmer(2010)以及Peltonen、Scheicher和Vuillemey(2013)使用基于网络的技术研究了股票市场和CDS价差的相关性。其他研究试图通过使用分位数回归来捕捉分布尾部的条件溢出概率。$\stackrel{5}{ }$ Diebold和Yilmaz(2011)反过来提出使用方差分解作为连通性度量,基于市场数据构建金融机构之间的网络。

另一种文献基于资产负债表风险敞口(如银行间风险敞口和银行资本),目的是对一个或多个金融机构遇到问题时对风险敞口网络的潜在影响进行反事实模拟。这可能会克服基于市场数据的文献的一些缺陷,例如资产价格可能会受到严重错误定价的影响,这可能会扭曲从分析中检索到的信号。在资产负债表数据的基础上分析银行传染风险和相互关联性的出发点是拥有银行间网络的可靠信息。人们可以将网络中的财务风险或责任视为机构(节点)与-à-vis另一个机构(节点)的关系(或边缘),由此关系描绘了机构之间潜在的冲击传播渠道。金融中介机构的相互敞口通常是有益的,因为它们允许更有效地分配金融资产和负债,并且是金融机构更多样化的标志。$\underline{6}$与此同时,当重大冲击冲击金融体系时,金融网络——尤其是当风险集中在少数主要参与者身上时——可以通过网络链接将冲击传播到整个金融体系,从而加速冲击的初始影响。正如Allen和Gale(2000)所强调的,网络的底层结构决定了它对传染的脆弱程度。-例如,Allen和Gale(2000)强调在完整网络中普遍存在的传染风险,即具有$\left(\begin{array}{c}N \ 2\end{array}\right)$链接的网络,其中$N$为节点数。$\frac{8}{}$此外,文献中还强调,在交易对手方和基础抵押品质量信息不对称的情况下,逆向选择问题可能会出现,从而导致银行间网络在危机时期功能失调。 $\underline{9}$

CS代写|计算机网络代写Computer Networking代考

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微观经济学代写

微观经济学是主流经济学的一个分支,研究个人和企业在做出有关稀缺资源分配的决策时的行为以及这些个人和企业之间的相互作用。my-assignmentexpert™ 为您的留学生涯保驾护航 在数学Mathematics作业代写方面已经树立了自己的口碑, 保证靠谱, 高质且原创的数学Mathematics代写服务。我们的专家在图论代写Graph Theory代写方面经验极为丰富,各种图论代写Graph Theory相关的作业也就用不着 说。

线性代数代写

线性代数是数学的一个分支,涉及线性方程,如:线性图,如:以及它们在向量空间和通过矩阵的表示。线性代数是几乎所有数学领域的核心。

博弈论代写

现代博弈论始于约翰-冯-诺伊曼(John von Neumann)提出的两人零和博弈中的混合策略均衡的观点及其证明。冯-诺依曼的原始证明使用了关于连续映射到紧凑凸集的布劳威尔定点定理,这成为博弈论和数学经济学的标准方法。在他的论文之后,1944年,他与奥斯卡-莫根斯特恩(Oskar Morgenstern)共同撰写了《游戏和经济行为理论》一书,该书考虑了几个参与者的合作游戏。这本书的第二版提供了预期效用的公理理论,使数理统计学家和经济学家能够处理不确定性下的决策。

微积分代写

微积分,最初被称为无穷小微积分或 “无穷小的微积分”,是对连续变化的数学研究,就像几何学是对形状的研究,而代数是对算术运算的概括研究一样。

它有两个主要分支,微分和积分;微分涉及瞬时变化率和曲线的斜率,而积分涉及数量的累积,以及曲线下或曲线之间的面积。这两个分支通过微积分的基本定理相互联系,它们利用了无限序列和无限级数收敛到一个明确定义的极限的基本概念 。

计量经济学代写

什么是计量经济学?
计量经济学是统计学和数学模型的定量应用,使用数据来发展理论或测试经济学中的现有假设,并根据历史数据预测未来趋势。它对现实世界的数据进行统计试验,然后将结果与被测试的理论进行比较和对比。

根据你是对测试现有理论感兴趣,还是对利用现有数据在这些观察的基础上提出新的假设感兴趣,计量经济学可以细分为两大类:理论和应用。那些经常从事这种实践的人通常被称为计量经济学家。

MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中,其中问题和解决方案以熟悉的数学符号表示。典型用途包括:数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发,包括图形用户界面构建MATLAB 是一个交互式系统,其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题,尤其是那些具有矩阵和向量公式的问题,而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问,这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展,得到了许多用户的投入。在大学环境中,它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域,MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要,工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数(M 文件)的综合集合,可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

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