Download or read book Essays in Robust and Data Driven Risk Management written by Elcin Cetinkaya and published by . This book was released on 2014 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the sixth chapter (Log-Robust Portfolio Management with Factor Model), we investigate robust optimization models that address uncertainty for asset pricing and portfolio management. We use factor model to predict asset returns and treat randomness by a budget of uncertainty. We obtain a tractable robust model to maximize the wealth and gain theoretical insights into the optimal investment strategies.

Download or read book Powering the Digital Economy Opportunities and Risks of Artificial Intelligence in Finance written by El Bachir Boukherouaa and published by International Monetary Fund. This book was released on 2021-10-22 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.

Download or read book Essays on Supply Chain Management with Model Uncertainty written by Mengshi Lu and published by . This book was released on 2014 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: Traditional supply chain management models typically require complete model information, including structural relationships (e.g., how pricing decisions affect customer demand), probabilistic distributions, and parameters. However, in practice, the model information may be uncertain. My dissertation research seeks to address model uncertainty in supply chain management problems using data-driven and robust methods. Incomplete information typically comes in two forms, namely, historical data and partial information. When historical data are available, data-driven methods can be used to obtain decisions directly from data, instead of estimating the model information and then using these estimates to find the optimal solution. When partial information is available, robust methods consider all possible scenarios and make decisions to hedge against the worst-case scenario effectively, instead of making simplified assumptions that could lead to significant loss. Chapter 1 provides an overview of model uncertainty in supply chain management, and discusses the limitations of the traditional methods. The main part of the dissertation is on the application of data-driven and robust methods to three widely-studied supply chain management problems with model uncertainty. Chapter 2 studies the reliable facility location problem where the joint-distribution of facility disruptions is uncertain. For this problem, usually, only partial information in the form of marginal facility disruption probabilities is available. Most existing models require the assumption that the disruptions at different locations are independent of each other. However, in practice, correlated disruptions are widely observed. We present a model that allows disruptions to be correlated with an uncertain joint distribution, and apply distributionally-robust optimization to minimize the expected cost under the worst-case distribution with the given marginal disruption probabilities. The worst-case distribution has a practical interpretation, and its sparse structure allows us to solve the problem efficiently. We find that ignoring disruption correlation could lead to significant loss. The robust method can significantly reduce the regret from model misspecification. It outperforms the traditional approach even under very mild correlation. Most of the benefit of the robust model can be captured at a relatively small cost, which makes it easy to implement in practice. Chapter 3 studies the pricing newsvendor problem where the structural relationship between pricing decisions and customer demand is unknown. Traditional methods for this problem require the selection of a parametric demand model and fitting the model using historical data, while model selection is usually a hard problem in itself. Furthermore, most of the existing literature on pricing requires certain conditions on the demand model, which may not be satisfied by the estimates from data. We present a data-driven approach based only on the historical observations and the basic domain knowledge. The conditional demand distribution is estimated using non-parametric quantile regression with shape constraints. The optimal pricing and inventory decisions are determined numerically using the estimated quantiles. Smoothing and kernelization methods are used to achieve regularization and enhance the performance of the approach. Additional domain knowledge, such as concavity of demand with respect to price, can also be easily incorporated into the approach. Numerical results show that the data-driven approach is able to find close-to-optimal solutions. Smoothing, kernelization, and the incorporation of additional domain knowledge can significantly improve the performance of the approach. Chapter 4 studies inventory management for perishable products where a parameter of the demand distribution is unknown. The traditional separated estimation-optimization approach for this problem has been shown to be suboptimal. To address this issue, an integrated approach called operational statistics has been proposed. We study several important properties of operational statistics. We find that the operational statistics approach is consistent and guaranteed to outperform the traditional approach. We also show that the benefit of using operational statistics is larger when the demand variability is higher. We then generalize the operational statistics approach to the risk-averse newsvendor problem under the conditional value-at-risk (CVaR) criterion. Previous results in operational statistics can be generalized to maximize the expectation of conditional CVaR. In order to model risk-aversion to both the uncertainty in demand sampling and the uncertainty in future demand, we introduce a new criterion called the total CVaR, and find the optimal operational statistic for this new criterion.

Download or read book Risk Analytics written by Eduardo Rodriguez and published by CRC Press. This book was released on 2023-08-08 with total page 483 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 2022 World Economic Forum surveyed 1,000 experts and leaders who indicated their risk perception that the earth’s conditions for humans are a main concern in the next 10 years. This means environmental risks are a priority to study in a formal way. At the same time, innovation risks are present in theminds of leaders, newknowledge brings new risk, and the adaptation and adoption of risk knowledge is required to better understand the causes and effects can have on technological risks. These opportunities require not only adopting new ways of managing and controlling emerging processes for society and business, but also adapting organizations to changes and managing newrisks. Risk Analytics: Data-Driven Decisions Under Uncertainty introduces a way to analyze and design a risk analytics system (RAS) that integrates multiple approaches to risk analytics to deal with diverse types of data and problems. A risk analytics system is a hybrid system where human and artificial intelligence interact with a data gathering and selection process that uses multiple sources to the delivery of guidelines to make decisions that include humans and machines. The RAS system is an integration of components, such as data architecture with diverse data, and a risk analytics process and modeling process to obtain knowledge and then determine actions through the new knowledge that was obtained. The use of data analytics is not only connected to risk modeling and its implementation, but also to the development of the actionable knowledge that can be represented by text in documents to define and share explicit knowledge and guidelines in the organization for strategy implementation. This book moves from a review of data to the concepts of a RAS. It reviews RAS system components required to support the creation of competitive advantage in organizations through risk analytics. Written for executives, analytics professionals, risk management professionals, strategy professionals, and postgraduate students, this book shows a way to implement the analytics process to develop a risk management practice that creates an adaptive competitive advantage under uncertainty.

Download or read book Operational Risk Management written by Ron S. Kenett and published by John Wiley & Sons. This book was released on 2011-06-20 with total page 339 pages. Available in PDF, EPUB and Kindle. Book excerpt: Models and methods for operational risks assessment and mitigation are gaining importance in financial institutions, healthcare organizations, industry, businesses and organisations in general. This book introduces modern Operational Risk Management and describes how various data sources of different types, both numeric and semantic sources such as text can be integrated and analyzed. The book also demonstrates how Operational Risk Management is synergetic to other risk management activities such as Financial Risk Management and Safety Management. Operational Risk Management: a practical approach to intelligent data analysis provides practical and tested methodologies for combining structured and unstructured, semantic-based data, and numeric data, in Operational Risk Management (OpR) data analysis. Key Features: The book is presented in four parts: 1) Introduction to OpR Management, 2) Data for OpR Management, 3) OpR Analytics and 4) OpR Applications and its Integration with other Disciplines. Explores integration of semantic, unstructured textual data, in Operational Risk Management. Provides novel techniques for combining qualitative and quantitative information to assess risks and design mitigation strategies. Presents a comprehensive treatment of "near-misses" data and incidents in Operational Risk Management. Looks at case studies in the financial and industrial sector. Discusses application of ontology engineering to model knowledge used in Operational Risk Management. Many real life examples are presented, mostly based on the MUSING project co-funded by the EU FP6 Information Society Technology Programme. It provides a unique multidisciplinary perspective on the important and evolving topic of Operational Risk Management. The book will be useful to operational risk practitioners, risk managers in banks, hospitals and industry looking for modern approaches to risk management that combine an analysis of structured and unstructured data. The book will also benefit academics interested in research in this field, looking for techniques developed in response to real world problems.

Download or read book Essays in Innovative Risk Management Methods written by Marco Desogus and published by . This book was released on 2018-10 with total page 62 pages. Available in PDF, EPUB and Kindle. Book excerpt: This analysis works towards overcoming the current business valuation logic as prevalently set by banks and other credit entities or, more generally, within risk capital markets. Current banking practice applies rigorous deterministic valuations that are based entirely on indices and ratios. Present accounting models are also poor representations of the correct money-credit-production-income mechanisms. This research proposes reforms for methods of business evaluation and determining the relative solidity or probability of insolvency. Each of the themes treated has its own identity, however they are integrated in relation to problems related to bank risk management and relevant creditworthiness assessments.

Download or read book Data Driven Security written by Jay Jacobs and published by John Wiley & Sons. This book was released on 2014-02-24 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncover hidden patterns of data and respond with countermeasures Security professionals need all the tools at their disposal to increase their visibility in order to prevent security breaches and attacks. This careful guide explores two of the most powerful data analysis and visualization. You'll soon understand how to harness and wield data, from collection and storage to management and analysis as well as visualization and presentation. Using a hands-on approach with real-world examples, this book shows you how to gather feedback, measure the effectiveness of your security methods, and make better decisions. Everything in this book will have practical application for information security professionals. Helps IT and security professionals understand and use data, so they can thwart attacks and understand and visualize vulnerabilities in their networks Includes more than a dozen real-world examples and hands-on exercises that demonstrate how to analyze security data and intelligence and translate that information into visualizations that make plain how to prevent attacks Covers topics such as how to acquire and prepare security data, use simple statistical methods to detect malware, predict rogue behavior, correlate security events, and more Written by a team of well-known experts in the field of security and data analysis Lock down your networks, prevent hacks, and thwart malware by improving visibility into the environment, all through the power of data and Security Using Data Analysis, Visualization, and Dashboards.

Download or read book Robust Simulation for Mega Risks written by Craig E. Taylor and published by Springer. This book was released on 2015-11-11 with total page 179 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces a new way of analyzing, measuring and thinking about mega-risks, a “paradigm shift” that moves from single-solutions to multiple competitive solutions and strategies. “Robust simulation” is a statistical approach that demonstrates future risk through simulation of a suite of possible answers. To arrive at this point, the book systematically walks through the historical statistical methods for evaluating risks. The first chapters deal with three theories of probability and statistics that have been dominant in the 20th century, along with key mathematical issues and dilemmas. The book then introduces “robust simulation” which solves the problem of measuring the stability of simulated losses, incorporates outliers, and simulates future risk through a suite of possible answers and stochastic modeling of unknown variables. This book discusses various analytical methods for utilizing divergent solutions in making pragmatic financial and risk-mitigation decisions. The book emphasizes the importance of flexibility and attempts to demonstrate that alternative credible approaches are helpful and required in understanding a great many phenomena.

Download or read book Applied Data Science written by Martin Braschler and published by Springer. This book was released on 2019-06-13 with total page 465 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry.

Download or read book Risk Analytics written by Eduardo Rodríguez Taborda and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Organizations and institutions are looking for implementing data-driven decision-making processes, business process improvement and methods for advancing faster in innovation. This book is about implementing the analytics process to develop a risk management practice that creates competitive advantages under uncertainty"--

Download or read book Three Essays on High Frequency Financial Data and Their Use for Risk Management written by Maria Pacurar and published by . This book was released on 2006 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays in Risk Management written by Amiyatosh Kumar Purnanandam and published by . This book was released on 2005 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays in Honor of M Hashem Pesaran written by Alexander Chudik and published by Emerald Group Publishing. This book was released on 2022-01-18 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: The collection of chapters in Volume 43 Part A of Advances in Econometrics serves as a tribute to one of the most innovative, influential, and productive econometricians of his generation, Professor M. Hashem Pesaran.

Download or read book Two Essays on Risk Management written by Chen-Miao Lin and published by . This book was released on 2003 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Three Essays on Financial Risk Management and Fat Tails written by Mamiko Yamashita and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, we investigate the various impacts of model misspecification and examine how to handle a model uncertainty. We analyze the impact of ignoring fat tails on an outcome of forecast comparison tests in the first chapter, and then study the effects of ignoring the dynamics of the risk premium of returns on the amount of capital requirements for banks in the second chapter. The third chapter provides a robust way to determine the capital requirements when facing a model uncertainty, that is, a lack of knowledge of the true data generating process. In the first chapter, we analyze forecast comparison tests under fat tails. Forecast comparison tests are widely implemented to compare the performances of two or more competing forecasts. The critical value is often obtained by the classical central limit theorem (CLT) or by the stationary bootstrap (Politis and Romano, 1994) with regularity conditions, including the one where the second moment of the loss difference is bounded. We show that if the moment condition is violated, the size of the test using the classical Normal asymptotics can be heavily distorted. As an alternative approach, we propose to use a subsampling method (Politis, Romano, and Wolf, 1999) that is robust to fat tails. In the empirical study, we analyze several variance forecast tests. Examining several tail index estimators, we show that the second moment of the loss difference is likely to be unbounded especially when the popular squared error (SE) function is used as a loss function.We also find that the outcome of the tests may change if the subsampling is used. The second chapter explores the effect of misspecification in the conditional mean dynamics on the determination of capital requirements for banks. In the Basel II accord (Basel Committee on Banking Supervision, 2010), the capital requirements for market risk are determined based upon a risk measure called Value-at-Risk (VaR). When VaR is computed, it is often assumed that the conditional mean of an asset return is constant over time. However, it is well documented that the predictability of returns increases as the prediction horizon becomes longer. The contribution of this chapter is to demonstrate the problems of ignoring the conditional mean dynamics when we compute VaR. We find that even though the models with a constant and a time-varying conditional mean may be statistically indistinguishable, the implied VaR can differ. This finding then raises another question on how to produce VaR when we acknowledge the time-variability of the conditional mean but there is an uncertainty of its current value. The third chapter puts forward a solution to the question raised in the second chapter by examining a robust way to determine the capital requirements when there is an uncertainty in the conditional mean of returns. We focus on Expected Shortfall (ES) rather than Value-at-Risk (VaR), since the capital reserves are now determined by ES in the Basel III accord. We propose to determine the capital reserves based on the worst-case ES. That is, we choose the maximum value within a set of ES forecasts mapped from the set of models that are pre-selected by the forecaster. With an assumption that the risk premium is believed to be non-negative, we show that the robust ES can in fact be achieved with a model in which the conditional mean is constant and the risk premium is always zero. This finding serves as an answer to the question raised in Chapter 2, and is one justification for assuming a constant conditional mean. We then consider a more general setting in which the forecaster is uncertain not only about the conditional mean but also about other aspects of the conditional distribution, such as the second or higher moments or the tails. There are many ways to define the set of models, and we focus on those defined with respect to the relative entropy, applying the robust control theory of Hansen and Sargent (2001).

Download or read book Essays on Robust Portfolio Management written by Lukas Plachel and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern Portfolio Theory (MPT) provides an elegant mathematical framework for the efficient portfolio allocation problem. Despite its exceptional popularity, MPT poses a number of well-documented problems in practical applications. Especially the fact that it generates notoriously extreme and non-robust allocations which may seriously impair the out-of-sample performance. This thesis introduces three methods with the common objective to remedy those shortcomings. Chapter 1 addresses the problems of traditional mean-variance optimization originating from model- and estimation errors. In order to simultaneously tackle both error sources, a joint method for covariance regularization and robust optimization is proposed which exploits the inherent complementarity between the two concepts. An application of the method to equity markets reveals similarly attractive behaviour as pure covariance regularization during normal times and improved performance as measured by out-of-sample volatility if a jump in systematic risk occurs. Chapter 2 introduces a covariance estimation approach which is based solely on characteristic company information. In contrast to traditional, time series based estimation procedures which typically lead to extreme and unreliable estimates, the proposed method produces stable covariance matrices which can be used if no time series data is available, or complementary to traditional methods. We derive characteristics-based covariance matrices for a US stock universe and use them as shrinkage targets in a minimum variance optimization example. The resulting strategies clearly dominate the benchmark case of identity shrinkage in terms of out-of-sample volatility. Chapter 3 bridges the gap between MPT and one of the most vivid fields of contemporary research: Artificial Intelligence. A model is introduced which uses a Neural Network to learn the relation between portfolio weights and arbitrary measures of portfolio.

Download or read book Essays on Belief Updating Forecasting and Robust Policy Making Based on Macroeconomic Variables written by Yizhou Kuang and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation consists of three essays that delve into the intersection of econometrics and macroeconomics. The essays employ econometric tools to investigate various topics related to macroeconomic forecasting and policy-making. The first essay aims to help policy-makers conduct robust inference on parameters that may suffer identification issues from DSGE models, and perform reliable counterfactual analysis based on available macroeconomic indicators. The second essay from a non-structural perspective, explores how to optimally forecast these variables in real-time utilizing available macroeconomic variables under model uncertainty. The last essay looks at Survey of Professional Forecasters and studies how agents update their beliefs based on common and private signals during business cycles.The first chapter introduces a new algorithm to conduct robust Bayesian estimation and inference in dynamic stochastic general equilibrium models. The algorithm combines standard Bayesian methods with an equivalence characterization of model solutions. This algorithm allows researchers to perform the following analysis: First, find the complete range of posterior means of both the deep parameters and any parameters of interest robust to the choice of priors in a sense I make precise. Second, derive the robust Bayesian credible region for these parameters. I prove the validity of this algorithm and apply this method to the models in Cochrane (2011) and An and Schorfheide (2007) to achieve robust estimations for structural parameters and impulse responses. In addition, I conduct a sensitivity analysis of optimal monetary policy rules with respect to the choice of priors and provide bounds to the optimal Taylor rule parameters.In the second chapter, my coauthors Yongmiao Hong, Yuying Sun and I focus on real-time monitoring of economic activities, also known as nowcasting. Nowcasting can be particularly challenging in the era of Big Data because it requires the management of a substantial amount of time series data that exhibit different frequencies and release dates. In this paper, we propose a novel now-casting strategy that utilizes dynamic factor models, which we call leave-b-out forward validation model averaging with penalization (LboFVMA). We demonstrate that the selected weight converges asymptotically to an optimal and consistent estimator, even in cases where all candidate models are misspecified. Further-more, the proposed estimator is consistent and follows an asymptotic Gaussian distribution if the true model is included among the candidate models. Our simulation results demonstrate that the LboFVMA approach performs well, as it generates low mean square forecast errors. This highlights its effectiveness and accuracy in the field of nowcasting.In the third chapter, my coauthors Nathan Mislang, Kristoffer Nimark and I propose a method to empirically decompose a cross-section of observed belief revisions into components driven by private and common signals under weak assumptions. We define a common signal as the single signal that if observed by all agents can explain the maximum amount of belief revisions across agents. Private signals are defined to explain the residual belief revisions unaccounted for by the common signal. When applied to probability forecasts from the Survey of Professional Forecasters we find that private signals account for more of the observed belief revisions than common signals. There is a large cross-sectional heterogeneity in signal precision across forecasters, with about 1/2 of them observing private signals that are less precise than the common signal. Unconditionally, the precision of private and common signals are positively correlated, suggesting that private and common information are complements. Inflation volatility, perceived stock market volatility and a high risk of recession are all factors associated with increased informativeness and precision of both private and common signals. Disagreement between the private and common signals can partly explain increases in uncertainty about macro variables. We discuss the implications of our findings for theoretical models of information acquisition.