The individual risks faced by banks, insurers, and marketers are less well understood than aggregate risks such as market-price changes. But the risks incurred or carried by individual people, companies, insurance policies, or credit agreements can be just as devastating as macroevents such as share-price fluctuations. A comprehensive introduction, The Econometrics of Individual Risk is the first book to provide a complete econometric methodology for quantifying and managing this underappreciated but important variety of risk. The book presents a course in the econometric theory of individual risk illustrated by empirical examples. And, unlike other texts, it is focused entirely on solving the actual individual risk problems businesses confront today. Christian Gourieroux and Joann Jasiak emphasize the microeconometric aspect of risk analysis by extensively discussing practical problems such as retail credit scoring, credit card transaction dynamics, and profit maximization in promotional mailing. They address regulatory issues in sections on computing the minimum capital reserve for coverage of potential losses, and on the credit-risk measure CreditVar. The book will interest graduate students in economics, business, finance, and actuarial studies, as well as actuaries and financial analysts.
This edited book contains several state-of-the-art papers devoted to econometrics of risk. Some papers provide theoretical analysis of the corresponding mathematical, statistical, computational, and economical models. Other papers describe applications of the novel risk-related econometric techniques to real-life economic situations. The book presents new methods developed just recently, in particular, methods using non-Gaussian heavy-tailed distributions, methods using non-Gaussian copulas to properly take into account dependence between different quantities, methods taking into account imprecise ("fuzzy") expert knowledge, and many other innovative techniques. This versatile volume helps practitioners to learn how to apply new techniques of econometrics of risk, and researchers to further improve the existing models and to come up with new ideas on how to best take into account economic risks.
Covers credit risk and credit derivatives. This book offers several points of view on credit risk when looked at from the perspective of Econometrics and Financial Mathematics. It addresses the challenge of modeling defaults and their correlations, and results on copula, reduced form and structural models, and the top-down approach.
Risk Econometrics: A Practical Guide to Bayesian and Frequentist Methods serves as a guide to mastering a growing number of applications in network analysis, environmental science and healthcare. By avoiding a focus either on time series or cross-sectional/panel data methods and adopting either Frequentist (Classical) or Bayesian approaches, it trains readers to recognize the most important aspects of applied Frequentist and Bayesian statistics, emphasizing methods, insights, and popular advances widely used during the last ten years. Sections dive deeply into the assumptions and pros and cons of statistical methods. Based on R and Python, and accompanied by both exercises and research projects, this book reinforces a balance between theory and practice that other books, wedded to only one statistical method, cannot match. Combines Frequentist and Bayesian methods in time series, cross sectional and panel data settings with an emphasis on risk modeling using R and Python Includes exercises and applications in new industry projects, such as Risk and return of environmental funds, Systemic risk measures using Bayesian and Frequentist methods, Initial margin setting for Central Clearing Counterparties (CCPs), and Measuring overall risk associated with a security relative to the market using MSCI Barra Factor Models
Recently risk management has become a standard prerequisite for all financial institutions. Value-at-Risk is the main tool of reporting to the bank regulators the risk that the financial institutions face. As it is essential to estimate it accurately, numerous methods have been proposed in order to minimise the forecast error. This book provides a selective survey of the risk management techniques that have been applied and discusses potential improvements in estimating, evaluating and adjusting Value-at-Risk and Expected Shortfall.
|Author||: Georg Bol,Gholamreza Nakhaeizadeh,Karl-Heinz Vollmer|
|Publisher||: Springer Science & Business Media|
|Release Date||: 2012-12-06|
|ISBN 10||: 3642582729|
|Pages||: 306 pages|
This book comprises the articles of the 6th Econometric Workshop in Karlsruhe, Germany. In the first part approaches from traditional econometrics and innovative methods from machine learning such as neural nets are applied to financial issues. Neural Networks are successfully applied to different areas such as debtor analysis, forecasting and corporate finance. In the second part various aspects from Value-at-Risk are discussed. The proceedings describe the legal framework, review the basics and discuss new approaches such as shortfall measures and credit risk.
|Author||: Carol Alexander|
|Publisher||: John Wiley & Sons|
|Release Date||: 2008-04-30|
|ISBN 10||: 0470771038|
|Pages||: 426 pages|
Written by leading market risk academic, Professor Carol Alexander, Practical Financial Econometrics forms part two of the Market Risk Analysis four volume set. It introduces the econometric techniques that are commonly applied to finance with a critical and selective exposition, emphasising the areas of econometrics, such as GARCH, cointegration and copulas that are required for resolving problems in market risk analysis. The book covers material for a one-semester graduate course in applied financial econometrics in a very pedagogical fashion as each time a concept is introduced an empirical example is given, and whenever possible this is illustrated with an Excel spreadsheet. All together, the Market Risk Analysis four volume set illustrates virtually every concept or formula with a practical, numerical example or a longer, empirical case study. Across all four volumes there are approximately 300 numerical and empirical examples, 400 graphs and figures and 30 case studies many of which are contained in interactive Excel spreadsheets available from the the accompanying CD-ROM . Empirical examples and case studies specific to this volume include: Factor analysis with orthogonal regressions and using principal component factors; Estimation of symmetric and asymmetric, normal and Student t GARCH and E-GARCH parameters; Normal, Student t, Gumbel, Clayton, normal mixture copula densities, and simulations from these copulas with application to VaR and portfolio optimization; Principal component analysis of yield curves with applications to portfolio immunization and asset/liability management; Simulation of normal mixture and Markov switching GARCH returns; Cointegration based index tracking and pairs trading, with error correction and impulse response modelling; Markov switching regression models (Eviews code); GARCH term structure forecasting with volatility targeting; Non-linear quantile regressions with applications to hedging.
Using Applied Econometrics with SAS: Modeling Demand, Supply, and Risk, you will quickly master SAS applications for implementing and estimating standard models in the field of econometrics. This guide introduces you to the major theories underpinning applied demand and production economics. For each of its three main topics—demand, supply, and risk—a concise theoretical orientation leads directly into consideration of specific economic models and econometric techniques, collectively covering the following: Double-log demand systems Linear expenditure systems Almost ideal demand systems Rotterdam models Random parameters logit demand models Frequency-severity models Compound distribution models Cobb-Douglas production functions Translogarithmic cost functions Generalized Leontief cost functions Density estimation techniques Copula models SAS procedures that facilitate estimation of demand, supply, and risk models include the following, among others: PROC MODEL PROC COPULA PROC SEVERITY PROC KDE PROC LOGISTIC PROC HPCDM PROC IML PROC REG PROC COUNTREG PROC QLIM An empirical example, SAS programming code, and a complete data set accompany each econometric model, empowering you to practice these techniques while reading. Examples are drawn from both major scholarly studies and business applications so that professors, graduate students, government economic researchers, agricultural analysts, actuaries, and underwriters, among others, will immediately benefit. This book is part of the SAS Press program.
|Author||: Simone Manganelli|
|Release Date||: 2000|
|Pages||: 240 pages|
|Author||: Jeongseok Song|
|Release Date||: 2004|
|Pages||: 324 pages|
|Author||: Mark Ollunga Odhiambo|
|Release Date||: 1983|
|Pages||: 448 pages|
|Author||: Michael Melvin (Economist),Don Schlagenhauf|
|Release Date||: 1984|
|Pages||: 70 pages|