Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Analytical Approaches To Calculating Value At Risk Of A Quadratic Approximation Of A Stock And Options Portfolio
Download Analytical Approaches To Calculating Value At Risk Of A Quadratic Approximation Of A Stock And Options Portfolio full books in PDF, epub, and Kindle. Read online Analytical Approaches To Calculating Value At Risk Of A Quadratic Approximation Of A Stock And Options Portfolio ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Analytical Approaches to Calculating Value-at-risk of a Quadratic Approximation of a Stock and Options Portfolio by : Daniel Xiaochen Wang
Download or read book Analytical Approaches to Calculating Value-at-risk of a Quadratic Approximation of a Stock and Options Portfolio written by Daniel Xiaochen Wang and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Comparative Analysis of Value at Risk (VAR) Methods for Portfolio with Non-Linear Return by : Manohar Lal
Download or read book Comparative Analysis of Value at Risk (VAR) Methods for Portfolio with Non-Linear Return written by Manohar Lal and published by . This book was released on 2013 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this study various value at risk methods such as Historical Simulation, Variance-Covariance Approach and Monte Carlo Simulation are calculated, compared and tested for accuracy. Backtesting for the VaR methods is applied to check the accuracy of the VaR methods. The portfolio includes equally weighted three banking stock and one at-the-money (ATM) call option for one of the banking stock in the portfolio. The log return for the portfolio and individual investments are calculated. Different VaR calculation methods are used to calculate the downside risk of the portfolio and individual investments. VaR is calculated at 95% and 99% confidence level for the portfolio and individual securities. The value at risk for the portfolio at 95% confidence level from all the three methods are within the defined level of downside risk, while at 99% confidence level only Mote Carlo Simulation method provides good approximation of downside risk for a portfolio with options. Thus from this study it is inferred that for instrument or portfolio with non-linear return structure Monte Carlo simulation method provide good approximation of the downside risk.
Book Synopsis Financial Risk Management by : Jimmy Skoglund
Download or read book Financial Risk Management written by Jimmy Skoglund and published by John Wiley & Sons. This book was released on 2015-09-08 with total page 580 pages. Available in PDF, EPUB and Kindle. Book excerpt: A global banking risk management guide geared toward the practitioner Financial Risk Management presents an in-depth look at banking risk on a global scale, including comprehensive examination of the U.S. Comprehensive Capital Analysis and Review, and the European Banking Authority stress tests. Written by the leaders of global banking risk products and management at SAS, this book provides the most up-to-date information and expert insight into real risk management. The discussion begins with an overview of methods for computing and managing a variety of risk, then moves into a review of the economic foundation of modern risk management and the growing importance of model risk management. Market risk, portfolio credit risk, counterparty credit risk, liquidity risk, profitability analysis, stress testing, and others are dissected and examined, arming you with the strategies you need to construct a robust risk management system. The book takes readers through a journey from basic market risk analysis to major recent advances in all financial risk disciplines seen in the banking industry. The quantitative methodologies are developed with ample business case discussions and examples illustrating how they are used in practice. Chapters devoted to firmwide risk and stress testing cross reference the different methodologies developed for the specific risk areas and explain how they work together at firmwide level. Since risk regulations have driven a lot of the recent practices, the book also relates to the current global regulations in the financial risk areas. Risk management is one of the fastest growing segments of the banking industry, fueled by banks' fundamental intermediary role in the global economy and the industry's profit-driven increase in risk-seeking behavior. This book is the product of the authors' experience in developing and implementing risk analytics in banks around the globe, giving you a comprehensive, quantitative-oriented risk management guide specifically for the practitioner. Compute and manage market, credit, asset, and liability risk Perform macroeconomic stress testing and act on the results Get up to date on regulatory practices and model risk management Examine the structure and construction of financial risk systems Delve into funds transfer pricing, profitability analysis, and more Quantitative capability is increasing with lightning speed, both methodologically and technologically. Risk professionals must keep pace with the changes, and exploit every tool at their disposal. Financial Risk Management is the practitioner's guide to anticipating, mitigating, and preventing risk in the modern banking industry.
Book Synopsis Approximations for the Value-at-Risk Approach to Risk-Return Analysis by : Dirk Tasche
Download or read book Approximations for the Value-at-Risk Approach to Risk-Return Analysis written by Dirk Tasche and published by . This book was released on 2014 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt: An evergreen debate in Finance concerns the rules for making portfolio hedge decisions. A traditional tool proposed in the literature is the well-known standard deviation based Sharpe Ratio, which has been recently generalized in order to involve also other popular risk measures p, such as VaR (Value-at-Risk) or CVaR (Conditional Value at Risk). This approach gives the correct choice of portfolio selection in a mean-p world as long as p is homogeneous of order 1. But, unfortunately, in important cases calculating the exact incremental Sharpe Ratio for ranking profitable portfolios turns out to be computationally too costly. Therefore, more easy-to-use rules for a rapid portfolio selection are needed. The research in this direction for VaR is just the aim of the paper. Approximation formulae are carried out which are based on certain derivatives of VaR and involve quantities similar to the skewness and kurtosis of the random variables under consid-eration. Starting point for the approximations is the observation that the partial derivatives of portfolio VaR with respect to the portfolio weights are just the conditional expectations of the asset returns given that the portfolio return equals VaR. Since the conditional expec-tation of a random variable Y given another random variable X can be considered the best possible regression of Y versus X in least squares sense, the idea is to replace the conditional expectation by polynomial regression or, more generally, by finite-dimensional regression of Y versus X. In case of the variables obeying an elliptical joint distribution, the resulting approximation formulae coincide with the exact formula for the standard deviation taken as risk measure. By means of a number of numerical examples and counter-examples the properties of the formulae are discussed.
Book Synopsis Risk and Portfolio Analysis by : Henrik Hult
Download or read book Risk and Portfolio Analysis written by Henrik Hult and published by Springer Science & Business Media. This book was released on 2012-07-20 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: Investment and risk management problems are fundamental problems for financial institutions and involve both speculative and hedging decisions. A structured approach to these problems naturally leads one to the field of applied mathematics in order to translate subjective probability beliefs and attitudes towards risk and reward into actual decisions. In Risk and Portfolio Analysis the authors present sound principles and useful methods for making investment and risk management decisions in the presence of hedgeable and non-hedgeable risks using the simplest possible principles, methods, and models that still capture the essential features of the real-world problems. They use rigorous, yet elementary mathematics, avoiding technically advanced approaches which have no clear methodological purpose and are practically irrelevant. The material progresses systematically and topics such as the pricing and hedging of derivative contracts, investment and hedging principles from portfolio theory, and risk measurement and multivariate models from risk management are covered appropriately. The theory is combined with numerous real-world examples that illustrate how the principles, methods, and models can be combined to approach concrete problems and to draw useful conclusions. Exercises are included at the end of the chapters to help reinforce the text and provide insight. This book will serve advanced undergraduate and graduate students, and practitioners in insurance, finance as well as regulators. Prerequisites include undergraduate level courses in linear algebra, analysis, statistics and probability.
Book Synopsis Market Risk Analysis, Boxset by : Carol Alexander
Download or read book Market Risk Analysis, Boxset written by Carol Alexander and published by John Wiley & Sons. This book was released on 2009-02-24 with total page 1691 pages. Available in PDF, EPUB and Kindle. Book excerpt: Market Risk Analysis is the most comprehensive, rigorous and detailed resource available on market risk analysis. Written as a series of four interlinked volumes each title is self-contained, although numerous cross-references to other volumes enable readers to obtain further background knowledge and information about financial applications. Volume I: Quantitative Methods in Finance covers the essential mathematical and financial background for subsequent volumes. Although many readers will already be familiar with this material, few competing texts contain such a complete and pedagogical exposition of all the basic quantitative concepts required for market risk analysis. There are six comprehensive chapters covering all the calculus, linear algebra, probability and statistics, numerical methods and portfolio mathematics that are necessary for market risk analysis. This is an ideal background text for a Masters course in finance. Volume II: Practical Financial Econometrics provides a detailed understanding of financial econometrics, with applications to asset pricing and fund management as well as to market risk analysis. It covers equity factor models, including a detailed analysis of the Barra model and tracking error, principal component analysis, volatility and correlation, GARCH, cointegration, copulas, Markov switching, quantile regression, discrete choice models, non-linear regression, forecasting and model evaluation. Volume III: Pricing, Hedging and Trading Financial Instruments has five very long chapters on the pricing, hedging and trading of bonds and swaps, futures and forwards, options and volatility as well detailed descriptions of mapping portfolios of these financial instruments to their risk factors. There are numerous examples, all coded in interactive Excel spreadsheets, including many pricing formulae for exotic options but excluding the calibration of stochastic volatility models, for which Matlab code is provided. The chapters on options and volatility together constitute 50% of the book, the slightly longer chapter on volatility concentrating on the dynamic properties the two volatility surfaces the implied and the local volatility surfaces that accompany an option pricing model, with particular reference to hedging. Volume IV: Value at Risk Models builds on the three previous volumes to provide by far the most comprehensive and detailed treatment of market VaR models that is currently available in any textbook. The exposition starts at an elementary level but, as in all the other volumes, the pedagogical approach accompanied by numerous interactive Excel spreadsheets allows readers to experience the application of parametric linear, historical simulation and Monte Carlo VaR models to increasingly complex portfolios. Starting with simple positions, after a few chapters we apply value-at-risk models to interest rate sensitive portfolios, large international securities portfolios, commodity futures, path dependent options and much else. This rigorous treatment includes many new results and applications to regulatory and economic capital allocation, measurement of VaR model risk and stress testing.
Book Synopsis Calculating Value-at-Risk by : William J. Fallon
Download or read book Calculating Value-at-Risk written by William J. Fallon and published by . This book was released on 1998 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The market risk of a portfolio refers to the possibility of financial loss due to the joint movement of systematic economic variables such as interest and exchange rates. Quantifying market risk is important to regulators in assessing solvency and to risk managers in allocating scarce capital. Moreover, market risk is often the central risk faced by financial institutions. The standard method for measuring market risk places a conservative, one-sided confidence interval on portfolio losses for short forecast horizons. This bound on losses is often called capital-at-risk or value-at-risk (VAR), for obvious reasons. Calculating the VAR or any similar risk metric requires a probability distribution of changes in portfolio value. In most risk management models, this distribution is derived by placing assumptions on (1) how the portfolio function is approximated, and (2) how the state variables are modeled. Using this framework, we first review four methods for measuring market risk. We then develop and illustrate two new market risk measurement models that use a second-order approximation to the portfolio function and a multivariate GARCH(l,1) model for the state variables. We show that when changes in the state variables are modeled as conditional or unconditional multivariate normal, first-order approximations to the portfolio function yield a univariate normal for the change in portfolio value while second-order approximations yield a quadratic normal. Using equity return data and a hypothetical portfolio of options, we then evaluate the performance of all six models by examining how accurately each calculates the VAR on an out-of-sample basis. We find that our most general model is superior to all others in predicting the VAR. In additional empirical tests focusing on the error contribution of each of the two model components, we find that the superior performance of our most general model is largely attributable to the use of the second-order approximation, and that the first-order approximations favored by practitioners perform quite poorly. Empirical evidence on the modeling of the state variables is mixed but supports usage of a model which reflects non-linearities in state variable return distributions.
Author :International Business Machines Corporation. Research Division. (IBMRD) Publisher : ISBN 13 : Total Pages :33 pages Book Rating :4.:/5 (247 download)
Book Synopsis Portfolio Value-at-risk with Heavy-tailed Risk Factors by : International Business Machines Corporation. Research Division. (IBMRD)
Download or read book Portfolio Value-at-risk with Heavy-tailed Risk Factors written by International Business Machines Corporation. Research Division. (IBMRD) and published by . This book was released on 2000 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "This paper develops efficient methods for computing portfolio value-at-risk (VAR) when the underlying risk factors have a heavy-tailed distribution. In modeling heavy tails, we focus on multivariate t distributions and some extensions thereof. We develop two methods for VAR calculation that exploit a quadratic approximation to the portfolio loss, such as the delta-gamma approximation. In the first method, we derive the characteristic function of the quadratic approximation and then use numerical transform inversion to approximate the portfolio loss distribution. Because the quadratic approximation may not always yield accurate VAR estimates, we also develop a low variance Monte Carlo method. This method uses the quadratic approximation to guide the selection of an effective importance sampling distribution that samples risk factors so that large losses occur more often. Variance is further reduced by combining the importance sampling with stratified sampling. Numerical results on a variety of test portfolios indicate that large variance reductions are typically obtained. Both methods developed in this paper overcome difficulties associated with VAR calculation with heavy-tailed risk factors. The Monte Carlo method also extends to the problem of estimating the conditional excess, sometimes known as the conditional VAR."
Book Synopsis Problems of Value At Risk - A Critical View by : Alexander Melichar
Download or read book Problems of Value At Risk - A Critical View written by Alexander Melichar and published by GRIN Verlag. This book was released on 2010-11-26 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: Seminar paper from the year 2009 in the subject Business economics - Controlling, grade: 1,5, University of Innsbruck (Institut für Banken und Finanzen), course: Seminar SBWL Risk Management, language: English, abstract: This seminar paper is divided in the following chapters: 1. Definition of Value at Risk: What is VaR, several definitions of this figure. 2. The three common approaches for calculating Value at Risk: Historical simulation, Monte Carlo simulation, Variance-Covariance model. 3. The critical view: Problems and limitations of Value at Risk. Which approach can be meaningfully used and when not? Why is Value at Risk not the “only truth” in financial institutions? What are the strengths and weaknesses of the several approaches in calculating Value at Risk?
Book Synopsis The Quadratic Approximation for the Value of American Options by : Andreas Andrikopoulos
Download or read book The Quadratic Approximation for the Value of American Options written by Andreas Andrikopoulos and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The valuation of put options with early-exercise opportunities constitutes a major challenge of asset pricing. The option-theoretic response to this challenge relies on the estimation of the optimal exercise boundary. This paper introduces a novel quadratic approximation for the valuation of American options on common stock. The paper's contribution lies in the tradition of semi-analytical approximation of American put options, which was put forward in Barone-Adesi and Whaley (1987). Assuming that the interest rate and the volatility are constant, the early-exercise premium is modeled as a product of two functions, one being a function of time and the other being a function of the stock price. The numerical results demonstrate the accuracy of the method, over competing alternatives such as the Barone-Adesi and Whaley (1987) algorithm.
Book Synopsis The Estimation of Market Risk in Portfolios of Stocks and Stock Options by : Hermann Locarek-Junge
Download or read book The Estimation of Market Risk in Portfolios of Stocks and Stock Options written by Hermann Locarek-Junge and published by . This book was released on 2008 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: Market risk can be described as potential losses in portfolio value caused by price changes in the investor's portfolio. Value-at-Risk (VaR) quantifies a loss bound that cannot be exceeded with a specified probability at a given time horizon, i.e., a quantile of the portfolio's loss distribution. We cannot determine this distribution of portfolio losses analytically for portfolios with nonlinear loss functions - especially those portfolios that include options - even if the distribution of risk factors is multivariatenormal. In such cases it is common practice to use extensive approximations and simulations under partly restrictive assumptions. To avoid such reductions, this paper uses approaches based on artificial neural networks (ANN).
Book Synopsis Asymptotic Optimization of Risk Measures by : Maria Teresa Quintanilla
Download or read book Asymptotic Optimization of Risk Measures written by Maria Teresa Quintanilla and published by . This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Market Risk Analysis, Quantitative Methods in Finance by : Carol Alexander
Download or read book Market Risk Analysis, Quantitative Methods in Finance written by Carol Alexander and published by John Wiley & Sons. This book was released on 2008-04-30 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by leading market risk academic, Professor Carol Alexander, Quantitative Methods in Finance forms part one of the Market Risk Analysis four volume set. Starting from the basics, this book helps readers to take the first step towards becoming a properly qualified financial risk manager and asset manager, roles that are currently in huge demand. Accessible to intelligent readers with a moderate understanding of mathematics at high school level or to anyone with a university degree in mathematics, physics or engineering, no prior knowledge of finance is necessary. Instead the emphasis is on understanding ideas rather than on mathematical rigour, meaning that this book offers a fast-track introduction to financial analysis for readers with some quantitative background, highlighting those areas of mathematics that are particularly relevant to solving problems in financial risk management and asset management. Unique to this book is a focus on both continuous and discrete time finance so that Quantitative Methods in Finance is not only about the application of mathematics to finance; it also explains, in very pedagogical terms, how the continuous time and discrete time finance disciplines meet, providing a comprehensive, highly accessible guide which will provide readers with the tools to start applying their knowledge immediately. 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 accompanying CD-ROM . Empirical examples and case studies specific to this volume include: Principal component analysis of European equity indices; Calibration of Student t distribution by maximum likelihood; Orthogonal regression and estimation of equity factor models; Simulations of geometric Brownian motion, and of correlated Student t variables; Pricing European and American options with binomial trees, and European options with the Black-Scholes-Merton formula; Cubic spline fitting of yields curves and implied volatilities; Solution of Markowitz problem with no short sales and other constraints; Calculation of risk adjusted performance metrics including generalised Sharpe ratio, omega and kappa indices.
Book Synopsis Applied Quantitative Finance by : Wolfgang Karl Härdle
Download or read book Applied Quantitative Finance written by Wolfgang Karl Härdle and published by Springer. This book was released on 2017-08-02 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume provides practical solutions and introduces recent theoretical developments in risk management, pricing of credit derivatives, quantification of volatility and copula modeling. This third edition is devoted to modern risk analysis based on quantitative methods and textual analytics to meet the current challenges in banking and finance. It includes 14 new contributions and presents a comprehensive, state-of-the-art treatment of cutting-edge methods and topics, such as collateralized debt obligations, the high-frequency analysis of market liquidity, and realized volatility. The book is divided into three parts: Part 1 revisits important market risk issues, while Part 2 introduces novel concepts in credit risk and its management along with updated quantitative methods. The third part discusses the dynamics of risk management and includes risk analysis of energy markets and for cryptocurrencies. Digital assets, such as blockchain-based currencies, have become popular b ut are theoretically challenging when based on conventional methods. Among others, it introduces a modern text-mining method called dynamic topic modeling in detail and applies it to the message board of Bitcoins. The unique synthesis of theory and practice supported by computational tools is reflected not only in the selection of topics, but also in the fine balance of scientific contributions on practical implementation and theoretical concepts. This link between theory and practice offers theoreticians insights into considerations of applicability and, vice versa, provides practitioners convenient access to new techniques in quantitative finance. Hence the book will appeal both to researchers, including master and PhD students, and practitioners, such as financial engineers. The results presented in the book are fully reproducible and all quantlets needed for calculations are provided on an accompanying website. The Quantlet platform quantlet.de, quantlet.com, quantlet.org is an integrated QuantNet environment consisting of different types of statistics-related documents and program codes. Its goal is to promote reproducibility and offer a platform for sharing validated knowledge native to the social web. QuantNet and the corresponding Data-Driven Documents-based visualization allows readers to reproduce the tables, pictures and calculations inside this Springer book.
Book Synopsis Delta-Gamma Value at Risk, a Nonlinear Quadratic Approximation Approach by : Eoin Moore
Download or read book Delta-Gamma Value at Risk, a Nonlinear Quadratic Approximation Approach written by Eoin Moore and published by . This book was released on 1998 with total page 62 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Proceedings of the IEEE/IAFE 1995 Computational Intelligence for Financial Engineering (CIFEr) by :
Download or read book Proceedings of the IEEE/IAFE 1995 Computational Intelligence for Financial Engineering (CIFEr) written by and published by . This book was released on 1995 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Properties and Computation of Value at Risk Efficient Portfolios Based on Historical Data by : Alexei A. Gaivoronski
Download or read book Properties and Computation of Value at Risk Efficient Portfolios Based on Historical Data written by Alexei A. Gaivoronski and published by . This book was released on 2002 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt: Value at risk (VaR) is an important and widely used measure of the extent to which a given portfolio is subject to risk present in financial markets. Considerable amount of research was dedicated during recent years to development of acceptable methods for evaluation of this risk measure.In this paper, we present a method of calculating the portfolio which gives the optimal VaR among those, which yield at least some specified expected return. This method allows to calculate the mean-VaR efficient frontier. The method is based on approximation of historic VaR by smoothed VaR (SVaR) which filters out local irregular behavior of historic VaR function.Moreover, we compare the VaR as a risk measure to other well known measures of risk such as the conditional value at risk (CVaR) and the standard deviation.It turns out, that the corresponding efficient frontiers are quite different. An investor, who wants to control his VaR should not look at portfolios lying on other than the VaR efficient frontier, although the calculation of this frontier is algorithmically more complex compared to other frontiers.
