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| Author | : Carlos Martins-Filho |
| Publisher | : |
| Total Pages | : |
| Release | : 2013 |
| Genre | : |
| ISBN | : |
| Author | : Serguei Y. Novak |
| Publisher | : CRC Press |
| Total Pages | : 397 |
| Release | : 2011-12-20 |
| Genre | : Mathematics |
| ISBN | : 1439835756 |
Extreme value theory (EVT) deals with extreme (rare) events, which are sometimes reported as outliers. Certain textbooks encourage readers to remove outliers-in other words, to correct reality if it does not fit the model. Recognizing that any model is only an approximation of reality, statisticians are eager to extract information about unknown di
| Author | : Dipak K. Dey |
| Publisher | : CRC Press |
| Total Pages | : 538 |
| Release | : 2016-01-06 |
| Genre | : Mathematics |
| ISBN | : 1498701310 |
Extreme Value Modeling and Risk Analysis: Methods and Applications presents a broad overview of statistical modeling of extreme events along with the most recent methodologies and various applications. The book brings together background material and advanced topics, eliminating the need to sort through the massive amount of literature on the subje
| Author | : Christopher F. Parmeter |
| Publisher | : Emerald Group Publishing |
| Total Pages | : 487 |
| Release | : 2024-04-05 |
| Genre | : Business & Economics |
| ISBN | : 1837978735 |
It is the editor’s distinct privilege to gather this collection of papers that honors Subhal Kumbhakar’s many accomplishments, drawing further attention to the various areas of scholarship that he has touched.
| Author | : Chuan-Sheng Wang |
| Publisher | : |
| Total Pages | : |
| Release | : 2018 |
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| ISBN | : |
Conditional Value-at-Risk (hereafter, CVaR) and Expected Shortfall (CES) play an important role in financial risk management. Parametric CVaR and CES enjoy both nice interpretation and capability of multi-dimensional modeling, however they are subject to errors from mis-specification of the noise distribution. On the other hand, nonparametric estimations are robust but suffer from the ''curse of dimensionality'' and slow convergence rate. To overcome these issues, we study semiparametric CVaR and CES estimation and inference for parametric model with nonparametric noise distribution. In this dissertation, under a general framework that allows for many widely used time series models, we propose a semiparametric CVaR estimator and a semiparametric CES estimator that both achieve the parametric convergence rate. Asymptotic properties of the estimators are provided to support the inference. Furthermore, to draw simultaneous inference for CVaR at multiple confidence levels, we establish a functional central limit theorem for CVaR process indexed by the confidence level and use it to study the conditional expected shortfall. A user-friendly bootstrap approach is introduced to facilitate non-expert practitioners to perform confidence interval construction for CVaR and CES. The methodology is illustrated through both Monte Carlo studies and an application to S&P 500 index.
| Author | : Song Xi Chen |
| Publisher | : |
| Total Pages | : |
| Release | : 2010 |
| Genre | : |
| ISBN | : |
The expected shortfall is an increasingly popular risk measure in financial risk management and it possesses the desired sub-additivity property, which is lacking for the value at risk (VaR). We consider two nonparametric expected shortfall estimators for dependent financial losses. One is a sample average of excessive losses larger than a VaR. The other is a kernel smoothed version of the first estimator (Scaillet, 2004 Mathematical Finance), hoping that more accurate estimation can be achieved by smoothing. Our analysis reveals that the extra kernel smoothing does not produce more accurate estimation of the shortfall. This is different from the estimation of the VaR where smoothing has been shown to produce reduction in both the variance and the mean square error of estimation. Therefore, the simpler ES estimator based on the sample average of excessive losses is attractive for the shortfall estimation.
| Author | : Sumi K. V. |
| Publisher | : Bentham Science Publishers |
| Total Pages | : 152 |
| Release | : 2024-07-03 |
| Genre | : Business & Economics |
| ISBN | : 981523837X |
Business Analytics for Effective Decision Making is a comprehensive reference that explores the role of business analytics in driving informed decision-making. The book begins with an introduction to business analytics, highlighting its significance in today's dynamic business landscape. The subsequent chapters review various tools and software available for data analytics, addressing both the opportunities and challenges for professionals in different sectors. Readers will find practical insights and real-world case studies across diverse industries, including banking, retail, marketing, and supply chain management. Each chapter provides actionable insights and concludes with implications for implementing data-driven strategies. Key Features: Practical Examples: Real-world case studies and examples make complex concepts easy to understand. Ethical Considerations: Guidance on responsible data usage and addressing ethical implications. Comprehensive Coverage: From data collection to analysis and interpretation, the book covers all aspects of business analytics. Diverse Perspectives: Contributions from industry experts offer diverse insights into data analytics applications in business research, marketing, supply chain and the retail industry. Actionable Insights: Each chapter concludes with practical implications for implementing data-driven strategies.
| Author | : Marc S. Paolella |
| Publisher | : John Wiley & Sons |
| Total Pages | : 896 |
| Release | : 2018-10-10 |
| Genre | : Mathematics |
| ISBN | : 1119431859 |
A comprehensive and timely edition on an emerging new trend in time series Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH sets a strong foundation, in terms of distribution theory, for the linear model (regression and ANOVA), univariate time series analysis (ARMAX and GARCH), and some multivariate models associated primarily with modeling financial asset returns (copula-based structures and the discrete mixed normal and Laplace). It builds on the author's previous book, Fundamental Statistical Inference: A Computational Approach, which introduced the major concepts of statistical inference. Attention is explicitly paid to application and numeric computation, with examples of Matlab code throughout. The code offers a framework for discussion and illustration of numerics, and shows the mapping from theory to computation. The topic of time series analysis is on firm footing, with numerous textbooks and research journals dedicated to it. With respect to the subject/technology, many chapters in Linear Models and Time-Series Analysis cover firmly entrenched topics (regression and ARMA). Several others are dedicated to very modern methods, as used in empirical finance, asset pricing, risk management, and portfolio optimization, in order to address the severe change in performance of many pension funds, and changes in how fund managers work. Covers traditional time series analysis with new guidelines Provides access to cutting edge topics that are at the forefront of financial econometrics and industry Includes latest developments and topics such as financial returns data, notably also in a multivariate context Written by a leading expert in time series analysis Extensively classroom tested Includes a tutorial on SAS Supplemented with a companion website containing numerous Matlab programs Solutions to most exercises are provided in the book Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH is suitable for advanced masters students in statistics and quantitative finance, as well as doctoral students in economics and finance. It is also useful for quantitative financial practitioners in large financial institutions and smaller finance outlets.
| Author | : Alfonso Novales Cinca |
| Publisher | : |
| Total Pages | : 52 |
| Release | : 2017 |
| Genre | : |
| ISBN | : |
We use stock market data to analyze the quality of alternative models and procedures to estimate Expected Shortfall (ES) at different significance levels. We consider conditional models applied to the full distribution of returns as well as models that focus on tail events using extreme value theory (EVT) under the two-step procedure proposed by McNeil&Frey (2000). The performance of the different models is assessed using a variety of ES backtests recently proposed in the literature. Our results suggest that conditional EVT-based models produce more accurate 1- and 10-day ES forecasts than non-EVT based models. Under either approach, asymmetric probability distributions for return innovations are clearly more appropriate. These qualitative results are also valid for the recent crisis period, even though all models then undervalue the level of risk. Filtered Historic Simulation narrows the range of numerical forecasts obtained from alternative models, thereby reducing model risk. Combining EVT and FHS seems to be the best approach to obtain accurate ES forecasts.
| Author | : Xinxin Huang |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2014 |
| Genre | : |
| ISBN | : |
Value at Risk (VaR) and Expected Shortfall (ES) are methods often used to measure market risk. Inaccurate and unreliable Value at Risk and Expected Shortfall models can lead to underestimation of the market risk that a firm or financial institution is exposed to, and therefore may jeopardize the well-being or survival of the firm or financial institution during adverse markets. The objective of this study is therefore to examine various Value at Risk and Expected Shortfall models, including fatter tail models, in order to analyze the accuracy and reliability of these models. Thirteen VaR and ES models under three main approaches (Parametric, Non-Parametric and Semi-Parametric) are examined in this study. The results of this study show that the proposed model (ARMA(1,1)-GJR-GARCH(1,1)-SGED) gives the most balanced Value at Risk results. The semi-parametric model (Extreme Value Theory, EVT) is the most accurate Value at Risk model in this study for S&P 500.
