Sample Size Skewness And Leverage Effects In Value At Risk And Expected Shortfall Estimation

Author : Laura García Jorcano
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.81/5 ( download)

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Download or read book Sample Size, Skewness and Leverage Effects in Value at Risk and Expected Shortfall Estimation written by Laura García Jorcano and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The estimation of risk measures is an area of highest importance in the financial industry. Risk measures play a major role in the risk-management and in the computation of regulatory capital. The Basel III document [13] has suggested to shift from Value-at-Risk (VaR) into Expected Shortfall (ES) as a risk measure and to consider stressed scenarios at a new con dence level of 97:5%. This change is motivated by the appealing theoretical properties of ES as a measure of risk and the poor properties of VaR. In particular, VaR fails to control for tail risk". In this transition, the major challenge faced by nancial institutions is the unavailability of simple tools for evaluation of ES forecasts (i.e. backtesting ES) The objective of this thesis is to compare the performance of a variety of models for VaR and ES estimation for a collection of assets of di erent nature: stock indexes, individual stocks, bonds, exchange rates, and commodities. Throughout the thesis, by a VaR or an ES model" is meant a given speci cation for conditional volatility, combined with an assumption on the probability distribution of return innovations...

Author : Laura García Jorcano
Publisher : Ed. Universidad de Cantabria
ISBN 13 : 8481029122
Total Pages : 162 pages
Book Rating : 4.23/5 ( download)

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Download or read book Sample Size, Skewness and Leverage Effects in Value at Risk and Expected Shortfall Estimation written by Laura García Jorcano and published by Ed. Universidad de Cantabria. This book was released on 2020-02-24 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: The thesis analyzes the effect that the sample size, the asymmetry in the distribution of returns and the leverage in their volatility have on the estimation and forecasting of market risk in financial assets. The goal is to compare the performance of a variety of models for the estimation and forecasting of Value at Risk (VaR) and Expected Shortfall (ES) for a set of assets of different nature: market indexes, individual stocks, bonds, exchange rates, and commodities. The three chapters of the thesis address issues of greatest interest for the measurement of risk in financial institutions and, therefore, for the supervision of risks in the financial system. They deal with technical issues related to the implementation of the Basel Committee's guidelines on some aspects of which very little is known in the academic world and in the specialized financial sector. In the first chapter, a numerical correction is proposed on the values usually estimatedwhen there is little statistical information, either because it is a financial asset (bond, investment fund...) recently created or issued, or because the nature or the structure of the asset or portfolio have recently changed. The second chapter analyzes the relevance of different aspects of risk modeling. The third and last chapter provides a characterization of the preferable methodology to comply with Basel requirements related to the backtesting of the Expected Shortfall.

Author : Jorge Mario Uribe Gil
Publisher : Ed. Universidad de Cantabria
ISBN 13 : 8417888756
Total Pages : 212 pages
Book Rating : 4.56/5 ( download)

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Download or read book Essays on Risk and Uncertainty in Economics and Finance written by Jorge Mario Uribe Gil and published by Ed. Universidad de Cantabria. This book was released on 2022-11-22 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book adds to the resolution of two problems in finance and economics: i) what is macro-financial uncertainty? : How to measure it? How is it different from risk? How important is it for the financial markets? And ii) what sort of asymmetries underlie financial risk and uncertainty propagation across the global financial markets? That is, how risk and uncertainty change according to factors such as market states or market participants. In Chapter 2, which is entitled “Momentum Uncertainties”, the relationship between macroeconomic uncertainty and the abnormal returns of a momentum trading strategy in the stock market is studies. We show that high levels of uncertainty in the economy impact negatively and significantly the returns of a portfolio of stocks that consist of buying past winners and selling past losers. High uncertainty reduces below zero the abnormal returns of momentum, extinguishes the Sharpe ratio of the momentum strategy, while increases the probability of momentum crashes both by increasing the skewness and the kurtosis of the momentum return distribution. Uncertainty acts as an economic regime that underlies abrupt changes over time of the returns generated by momentum strategies. In Chapter 3, “Measuring Uncertainty in the Stock Market”, a new index for measuring stock market uncertainty on a daily basis is proposed. The index considers the inherent differentiation between uncertainty and the common variations between the series. The second contribution of chapter 3 is to show how this financial uncertainty index can also serve as an indicator of macroeconomic uncertainty. Finally, the dynamic relationship between uncertainty and the series of consumption, interest rates, production and stock market prices, among others, is analized. In chapter 4: “Uncertainty, Systemic Shocks and the Global Banking Sector: Has the Crisis Modified their Relationship?” we explore the stability of systemic risk and uncertainty propagation among financial institutions in the global economy, and show that it has remained stable over the last decade. Additionally, a new simple tool for measuring the resilience of financial institutions to these systemic shocks is provided. We examine the characteristics and stability of systemic risk and uncertainty, in relation to the dynamics of the banking sector stock returns. This sort of evidence is supportive of past claims, made in the field of macroeconomics, which hold that during the global financial crisis the financial system may have faced stronger versions of traditional shocks rather than a new type of shock. In chapter 5, “Currency downside risk, liquidity, and financial stability”, downside risk propagation across global currency markets and the ways in which it is related to liquidity is analyzed. Two primary contributions to the literature follow. First, tail-spillovers between currencies in the global FX market are estimated. This index is easy to build and does not require intraday data, which constitutes an important advantage. Second, we show that turnover is related to risk spillovers in global currency markets. Chapter 6 is entitled “Spillovers from the United States to Latin American and G7 Stock Markets: A VAR-Quantile Analysis”. This chapter contributes to the studies of contagion, market integration and cross-border spillovers during both regular and crisis episodes by carrying out a multivariate quantile analysis. It focuses on Latin American stock markets, which have been characterized by a highly positive dynamic in recent decades, in terms of market capitalization and liquidity ratios, after a far-reaching process of market liberalization and reforms to pension funds across the continent during the 80s and 90s. We document smaller dependences between the LA markets and the US market than those between the US and the developed economies, especially in the highest and lowest quantiles.

Author : Timotheos Angelidis
Publisher :
ISBN 13 : 9781607410409
Total Pages : 0 pages
Book Rating : 4.00/5 ( download)

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Download or read book Econometric Modeling of Value-at-risk written by Timotheos Angelidis and published by . This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. This book provides a selective survey of the risk management techniques.

Author : Jon Danielsson
Publisher : John Wiley & Sons
ISBN 13 : 1119977118
Total Pages : 307 pages
Book Rating : 4.17/5 ( download)

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Download or read book Financial Risk Forecasting written by Jon Danielsson and published by John Wiley & Sons. This book was released on 2011-04-20 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: Financial Risk Forecasting is a complete introduction to practical quantitative risk management, with a focus on market risk. Derived from the authors teaching notes and years spent training practitioners in risk management techniques, it brings together the three key disciplines of finance, statistics and modeling (programming), to provide a thorough grounding in risk management techniques. Written by renowned risk expert Jon Danielsson, the book begins with an introduction to financial markets and market prices, volatility clusters, fat tails and nonlinear dependence. It then goes on to present volatility forecasting with both univatiate and multivatiate methods, discussing the various methods used by industry, with a special focus on the GARCH family of models. The evaluation of the quality of forecasts is discussed in detail. Next, the main concepts in risk and models to forecast risk are discussed, especially volatility, value-at-risk and expected shortfall. The focus is both on risk in basic assets such as stocks and foreign exchange, but also calculations of risk in bonds and options, with analytical methods such as delta-normal VaR and duration-normal VaR and Monte Carlo simulation. The book then moves on to the evaluation of risk models with methods like backtesting, followed by a discussion on stress testing. The book concludes by focussing on the forecasting of risk in very large and uncommon events with extreme value theory and considering the underlying assumptions behind almost every risk model in practical use – that risk is exogenous – and what happens when those assumptions are violated. Every method presented brings together theoretical discussion and derivation of key equations and a discussion of issues in practical implementation. Each method is implemented in both MATLAB and R, two of the most commonly used mathematical programming languages for risk forecasting with which the reader can implement the models illustrated in the book. The book includes four appendices. The first introduces basic concepts in statistics and financial time series referred to throughout the book. The second and third introduce R and MATLAB, providing a discussion of the basic implementation of the software packages. And the final looks at the concept of maximum likelihood, especially issues in implementation and testing. The book is accompanied by a website - www.financialriskforecasting.com – which features downloadable code as used in the book.

Author : Mr.Andreas A. Jobst
Publisher : International Monetary Fund
ISBN 13 : 1475557531
Total Pages : 93 pages
Book Rating : 4.34/5 ( download)

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Download or read book Systemic Contingent Claims Analysis written by Mr.Andreas A. Jobst and published by International Monetary Fund. This book was released on 2013-02-27 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: The recent global financial crisis has forced a re-examination of risk transmission in the financial sector and how it affects financial stability. Current macroprudential policy and surveillance (MPS) efforts are aimed establishing a regulatory framework that helps mitigate the risk from systemic linkages with a view towards enhancing the resilience of the financial sector. This paper presents a forward-looking framework ("Systemic CCA") to measure systemic solvency risk based on market-implied expected losses of financial institutions with practical applications for the financial sector risk management and the system-wide capital assessment in top-down stress testing. The suggested approach uses advanced contingent claims analysis (CCA) to generate aggregate estimates of the joint default risk of multiple institutions as a conditional tail expectation using multivariate extreme value theory (EVT). In addition, the framework also helps quantify the individual contributions to systemic risk and contingent liabilities of the financial sector during times of stress.

Author : David Ruppert
Publisher : Springer
ISBN 13 : 1493926144
Total Pages : 736 pages
Book Rating : 4.45/5 ( download)

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Download or read book Statistics and Data Analysis for Financial Engineering written by David Ruppert and published by Springer. This book was released on 2015-04-21 with total page 736 pages. Available in PDF, EPUB and Kindle. Book excerpt: The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. In doing so, it illustrates concepts using financial markets and economic data, R Labs with real-data exercises, and graphical and analytic methods for modeling and diagnosing modeling errors. These methods are critical because financial engineers now have access to enormous quantities of data. To make use of this data, the powerful methods in this book for working with quantitative information, particularly about volatility and risks, are essential. Strengths of this fully-revised edition include major additions to the R code and the advanced topics covered. Individual chapters cover, among other topics, multivariate distributions, copulas, Bayesian computations, risk management, and cointegration. Suggested prerequisites are basic knowledge of statistics and probability, matrices and linear algebra, and calculus. There is an appendix on probability, statistics and linear algebra. Practicing financial engineers will also find this book of interest.

Author : M. R. Leadbetter
Publisher : Springer Science & Business Media
ISBN 13 : 1461254493
Total Pages : 344 pages
Book Rating : 4.92/5 ( download)

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Download or read book Extremes and Related Properties of Random Sequences and Processes written by M. R. Leadbetter and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: Classical Extreme Value Theory-the asymptotic distributional theory for maxima of independent, identically distributed random variables-may be regarded as roughly half a century old, even though its roots reach further back into mathematical antiquity. During this period of time it has found significant application-exemplified best perhaps by the book Statistics of Extremes by E. J. Gumbel-as well as a rather complete theoretical development. More recently, beginning with the work of G. S. Watson, S. M. Berman, R. M. Loynes, and H. Cramer, there has been a developing interest in the extension of the theory to include, first, dependent sequences and then continuous parameter stationary processes. The early activity proceeded in two directions-the extension of general theory to certain dependent sequences (e.g., Watson and Loynes), and the beginning of a detailed theory for stationary sequences (Berman) and continuous parameter processes (Cramer) in the normal case. In recent years both lines of development have been actively pursued.

Author : Joseph G. Haubrich
Publisher : University of Chicago Press
ISBN 13 : 0226921964
Total Pages : 286 pages
Book Rating : 4.69/5 ( download)

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Download or read book Quantifying Systemic Risk written by Joseph G. Haubrich and published by University of Chicago Press. This book was released on 2013-01-24 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the aftermath of the recent financial crisis, the federal government has pursued significant regulatory reforms, including proposals to measure and monitor systemic risk. However, there is much debate about how this might be accomplished quantitatively and objectively—or whether this is even possible. A key issue is determining the appropriate trade-offs between risk and reward from a policy and social welfare perspective given the potential negative impact of crises. One of the first books to address the challenges of measuring statistical risk from a system-wide persepective, Quantifying Systemic Risk looks at the means of measuring systemic risk and explores alternative approaches. Among the topics discussed are the challenges of tying regulations to specific quantitative measures, the effects of learning and adaptation on the evolution of the market, and the distinction between the shocks that start a crisis and the mechanisms that enable it to grow.

Author : Ainura Tursunalieva
Publisher :
ISBN 13 :
Total Pages : 208 pages
Book Rating : 4.56/5 ( download)

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Download or read book Semi-parametric and Non-parametric Estimation of the Operational Risk and Expected Shortfall: Simulation and Empirical Evidence written by Ainura Tursunalieva and published by . This book was released on 2012 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the large losses incurred in recent times by internationally active banks and other financial institutions in many industrialised countries, the recent literature has paid a considerable amount of attention to measuring and managing operational risk (OR). The OR is defined as the difference between the 99.9% quantile and the mean of the loss distribution. In other words, the OR is the unexpected loss, measured with a high degree of confidence. In their recent document, Basel Committee on Banking Supervision (2011) emphasised the need for a reliable OR estimate on which to base the calculation of the economic capital charge required to cover operational losses.There are both pros and cons associated with the advanced measurement approach (AMA), which is currently widely used. Since the report by Basel Committee on Banking Supervision (2004), academics and practitioners have studied the applicability and flexibility of AMA for modelling the severity of operational losses, and have also contributed to the further development of AMA. However, Basel Committee on Banking Supervision (2011) recently provided new guidelines for estimating OR that can lead to an optimum level of capital requirement for banks. According to these guidelines, the tail of the severity loss distribution needs to be modelled adequately, capturing the high skewness and kurtosis of the loss distribution. In addition, scenario and sensitivity analyses have to be conducted, and a range of possible estimates of OR should be produced, rather than reporting only a single point estimate of OR.The objective of the thesis is to adapt and study the performances of some recent advanced semi-parametric and non-parametric methods for modelling and estimating heavy-tailed severity distributions, which can be used under Loss Distribution Approach in compliance with the quantitative AMA standards. AMA allows banks and financial institutions develop their own methods to calculate OR. Therefore, the emphasis of this thesis is to provide banks and financial institutions with the detailed analysis of the performance of various non-parametric methods which have been proposed in the statistics literature recently in the context of a heavy-tailed distribution. The attractive feature of these non-parametric methods is that there is no need to estimate the entire loss distribution, or even its right tail - a key part of the distribution used in the estimation of OR. This thesis pays a considerable amount of attention to estimating the key parameters, such as the threshold loss and the tail index, which are used in estimating the economic capital requirement. The existing AMA for estimating OR has some weaknesses: the expected loss, computed as the simple sample average of operational losses, is biased and inconsistent, since the loss distribution is right heavy-tailed. It is difficult to assess the reliability of the OR estimate when only a point estimate of OR is reported. It is more informative to report either the standard error or an interval estimate of the underlying true OR. This thesis proposes improvements aimed at overcoming these weaknesses.The objectives of this thesis are to: (i) propose some improvements to recently developed advanced semi-parametric and non-parametric methods for estimating OR; (ii) construct unbiased and consistent point and interval estimates for the mean of a heavy-tailed loss distribution; (iii) conduct a simulation study in order to assess the finite sample properties of the interval estimate of the mean of a right heavy-tailed distribution, in terms of coverage probabilities and lengths; (iv) construct point and interval estimates of the 99.9% quantile (operational value at risk, OpVaR) of a heavy-tailed distribution, and conduct a simulation study to assess the accuracy of interval estimates for the quantile; and (v) provide a step-by-step approach to estimating the two downside risks, OR and the expected shortfall, by using the methods adapted and studied in this thesis. These methods are illustrated by applying them to some business losses in the US. This thesis finds that the sample mean estimate and the adjusted mean (unbiased and consistent) estimate of the expected loss (that is, the population mean of the loss distribution) can differ considerably, depending on the size of the tail index that measures the tail thickness. The heavier the tail of the loss distribution, the larger the difference between the two mean estimates, with the latter being larger than the former; the estimates are close to each other if the loss distribution is close to normal. These findings indicate that the regulatory capital requirement can be overestimated, if the expected loss is not estimated appropriately. The coverage and other properties of the empirical likelihood-based confidence interval estimate of the mean are good when the tail index is not close to one. On the other hand, when the tail index is very close to one, the sub-sampling bootstrap based interval estimate can be used. In addition, the results of the simulation study indicate that the data tilting method, which is the weighted empirical likelihood method, produces reliable confidence interval estimates for the 99.9% OpVaR. This method is attractive because it gives nearly zero weights to losses in the non-tail region and large weights to losses in the tail region - an important region when estimating OR. The methods studied in this thesis are applied to the estimation of OR and the expected shortfall of some business losses in the US. To help practitioners who are working with heavy-tailed distributions, a step-by-step approach to constructing the point and interval estimates of operational risks is provided. The empirical results indicate that the operational risk and expected shortfall are close to each other when the tail index is close to two, which indicates that the loss distribution is close to normal. On the other hand, if the tail index is close to one, which indicates that the loss distribution is close to stable, then the expected shortfall can be noticeably greater than the OR. These findings have implications for risk management and regulators. Since the 2008 financial crisis, regulators would like to see large businesses and banks allocating large amounts of capital. The findings of this thesis show that if the tail index is close to one, then the expected shortfall provides a better cushion than does OR, while the economic regulatory capital can be based on either the expected shortfall or OR when the tail index is close to two.