{"product_id":"bayesian-risk-management-a-guide-to-model-risk-and-sequential-learning-in-financial-markets-hardcover","title":"Bayesian Risk Management: A Guide to Model Risk and Sequential Learning in Financial Markets - Hardcover","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eMatt Sekerke\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003cb\u003eA risk measurement and management framework that takes model risk seriously\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eMost financial risk models assume the future will look like the past, but effective risk management depends on identifying fundamental changes in the marketplace as they occur. \u003ci\u003eBayesian Risk Management\u003c\/i\u003e details a more flexible approach to risk management, and provides tools to measure financial risk in a dynamic market environment. This book opens discussion about uncertainty in model parameters, model specifications, and model-driven forecasts in a way that standard statistical risk measurement does not. And unlike current machine learning-based methods, the framework presented here allows you to measure risk in a fully-Bayesian setting without losing the structure afforded by parametric risk and asset-pricing models. \u003c\/p\u003e\u003cul\u003e \u003cli\u003eRecognize the assumptions embodied in classical statistics\u003c\/li\u003e \u003cli\u003eQuantify model risk along multiple dimensions without backtesting\u003c\/li\u003e \u003cli\u003eModel time series without assuming stationarity\u003c\/li\u003e \u003cli\u003eEstimate state-space time series models online with simulation methods\u003c\/li\u003e \u003cli\u003eUncover uncertainty in workhorse risk and asset-pricing models\u003c\/li\u003e \u003cli\u003eEmbed Bayesian thinking about risk within a complex organization\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eIgnoring uncertainty in risk modeling creates an illusion of mastery and fosters erroneous decision-making. Firms who ignore the many dimensions of model risk measure too little risk, and end up taking on too much. \u003ci\u003eBayesian Risk Management\u003c\/i\u003e provides a roadmap to better risk management through more circumspect measurement, with comprehensive treatment of model uncertainty.\u003c\/p\u003e\u003ch3\u003eFront Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eA Risk Measurement and Management Framework that Takes Model Risk Seriously\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhy do risk models break down? The answer may lie in the way that statistical methods are conventionally used to draw inferences about market conditions and inform risk-taking behavior. \u003ci\u003eBayesian Risk Management \u003c\/i\u003eenables a discussion on the way standard statistical methods overlook uncertainty in model specifications, model parameters, and model-driven forecasts. In a simple and direct way, Bayesian methods are used throughout the book to: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eRecognize the assumptions embodied in classical statistics\u003c\/li\u003e \u003cli\u003eQuantify model risk along multiple dimensions\u003c\/li\u003e \u003cli\u003eModel time series without assuming continuity between past and future\u003c\/li\u003e \u003cli\u003eAdjust time-series estimates to maintain forecast accuracy\u003c\/li\u003e \u003cli\u003eUncover uncertainty in workhorse risk and asset-pricing models\u003c\/li\u003e \u003cli\u003eAchieve decentralized control of risk-taking in complex organizations\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eFor firms in financial services and other industries operating in a dynamic environment of incomplete information, \u003ci\u003eBayesian Risk Management\u003c\/i\u003e provides a thought-provoking challenge to the prevailing wisdom about the uses and limitations of statistical risk modeling.\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eMATT SEKERKE\u003c\/b\u003e is an economic consultant based in New York whose work focuses on the financial services industry and the application of advanced quantitative modeling techniques o financial data. He holds a BA in economics and mathematics from The Johns Hopkins University, an MA in history from The Johns Hopkins University, and an MBA in econometrics and statistics, analytic finance, and entrepreneurship from The University of Chicago Booth School of Business. He is also a CFA charterholder, a certified Financial Risk Manager, and a certified Energy Risk Professional.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 240\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1.1 x 9.1 x 6.1 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e September 15, 2015\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":52484355457331,"sku":"9781118708606","price":102.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0300\/5595\/6612\/files\/0_Mqb1FL8o9781118708606.webp?v=1759791259","url":"https:\/\/www.vysn.com\/products\/bayesian-risk-management-a-guide-to-model-risk-and-sequential-learning-in-financial-markets-hardcover","provider":"VYSN","version":"1.0","type":"link"}