Download or read book On the Measurement of Economic Tail Risk written by Steven Kou and published by . This book was released on 2015 with total page 51 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper attempts to provide a decision-theoretic foundation for the measurement of economic tail risk, which is not only closely related to utility theory but also relevant to statistical model uncertainty. The main result is that the only risk measures that satisfy a set of economic axioms for the Choquet expected utility and the statistical property of elicitability (i.e. there exists an objective function such that minimizing the expected objective function yields the risk measure) are the mean functional and the median shortfall, which is the median of tail loss distribution. Elicitability is important for backtesting. We also extend the result to address model uncertainty by incorporating multiple scenarios. As an application, we argue that median shortfall is a better alternative than expected shortfall for setting capital requirements in Basel Accords.

Download or read book Forward Looking Tail Risk Measures written by Markus Huggenberger and published by . This book was released on 2018 with total page 67 pages. Available in PDF, EPUB and Kindle. Book excerpt: We present an analytical framework for the forward-looking measurement of extreme market risk. In contrast to standard techniques relying on past return data, we propose to extract Value-at-Risk and Expected Shortfall under the physical measure from current option prices. Our empirical evidence suggests that the resulting estimates accurately capture the tail risk of the S&P 500 and that they quickly react to changing market conditions. Compared to dynamic tail risk forecasts driven by past returns, our forward-looking estimates are relatively higher during good times and lower during adverse economic conditions, which could reduce the amplification effects of conventional dynamic risk management policies.

Download or read book A Theory for Measures of Tail Risk written by Fangda Liu and published by . This book was released on 2020 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: The notion of "tail risk" has been a crucial consideration in modern risk management. To achieve a comprehensive understanding of the tail risk, we carry out an axiomatic study for risk measures which quantify the tail risk, that is, the behavior of a risk beyond a certain quantile. Such risk measures are referred to as tail risk measures in this paper. The two popular classes of regulatory risk measures in banking and insurance, the Value-at-Risk (VaR) and the Expected Shortfall (ES), are prominent, yet elementary, examples of tail risk measures. We establish a connection between a tail risk measure and a corresponding law-invariant risk measure, called its generator, and investigate their joint properties. A tail risk measure inherits many properties from its generator, but not subadditivity or convexity; nevertheless, a tail risk measure is coherent if and only if its generator is coherent. We explore further relevant issues on tail risk measures, such as bounds, distortion risk measures, risk aggregation, elicitability, and dual representations. In particular, there is no elicitable tail convex risk measure rather than the essential supremum, and under a continuity condition, the only elicitable and positively homogeneous monetary tail risk measures are the VaRs.

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.

Download or read book Financial Sector Tail Risk and Real Economic Activity written by Michael Neumann and published by . This book was released on 2014 with total page 46 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper documents that option-implied tail risk in the U.S. financial sector predicts real economic activity. The predictability is found to be incremental to the information content in a stock price-based measure of financial sector tail risk. This finding holds both in- and out-of-sample and is not driven by the 2007 to 2009 sub-prime crisis. The predictability can instead be attributed to the option market's ability to anticipate a rising funding cost differential between small and large banks which has a detrimental effect on real economic activity. The results have implications for the real-time monitoring of financial sector tail risk.

Download or read book A New Heuristic Measure of Fragility and Tail Risks written by Mr.Nassim N. Taleb and published by International Monetary Fund. This book was released on 2012-08-01 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper presents a simple heuristic measure of tail risk, which is applied to individual bank stress tests and to public debt. Stress testing can be seen as a first order test of the level of potential negative outcomes in response to tail shocks. However, the results of stress testing can be misleading in the presence of model error and the uncertainty attending parameters and their estimation. The heuristic can be seen as a second order stress test to detect nonlinearities in the tails that can lead to fragility, i.e., provide additional information on the robustness of stress tests. It also shows how the measure can be used to assess the robustness of public debt forecasts, an important issue in many countries. The heuristic measure outlined here can be used in a variety of situations to ascertain an ordinal ranking of fragility to tail risks.

Download or read book Measurement of Tail Risk written by Marta Mylyan and published by . This book was released on 2014 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Tail Risk and Pk Tail Risk written by Carlos Pedro dos Santos Gonçalves and published by . This book was released on 2005 with total page 29 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper discusses the notion of tail risk, and the ability of a tail risk measure to reflect this kind of risk. In particular, Yamai and Yoshiba's (2001, 2002) notion of strict risk measure tail risk is discussed and linked with a different notion of tail risk, the pK-tail risk, which is the risk associated with the probability measure conditional on the event that the losses are at least as large as K. A subset of pK-tail risk measures that are free of strict risk measure tail risk is introduced. These notions are then extended to Yaari's (1987) dual theory and the distorted risk measures framework.

Download or read book Systemic Tail Risk High frequency Measurement Evidence and Implications written by Deniz Erdemlioglu and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Measuring the Tail Risk written by Alexandru Vali Asimit and published by . This book was released on 2017 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt: The risk exposure of a business line could be perceived in many ways and is sensitive to the exercise that is performed. One way is to understand the effect of some common/reference risk over the performance of the business line in question, but irrespective of the modelling exercise, the exposure is evaluated under the presence of some suitable adverse scenarios. That is, measuring the tail risk is the main aim. We choose to evaluate the performance via an expectation, which is the most acceptable risk measure amongst academics, practitioners and regulators. In contrast to the common practice where the extreme region is chosen such that only the common/reference risk is explicitly allowed to be large, we assume in this paper an extreme region where both the business line in question and common/reference risks are explicitly allowed to be large. The advantage of this tail risk measure is that the asymptotic approximations are meaningful in all cases, especially in the asymptotic independence case, which helps in understanding the risk exposure in any possible setting. Our numerical examples illustrate these findings and provide a discussion about the sensitivity analysis of our approximations, which is a standard way of checking the importance of parameter estimation of the risk model. The numerical analysis shows strong evidence that our proposed tail risk measure has a lower sensitivity than the standard tail risk measure.

Download or read book Tail Risk and Asset Prices written by Bryan Kelly and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose a new measure of time-varying tail risk that is directly estimable from the cross section of returns. We exploit firm-level price crashes every month to identify common fluctuations in tail risk across stocks. Our tail measure is significantly correlated with tail risk measures extracted from S&P 500 index options, but is available for a longer sample since it is calculated from equity data. We show that tail risk has strong predictive power for aggregate market returns: A one standard deviation increase in tail risk forecasts an increase in excess market returns of 4.5% over the following year. Cross-sectionally, stocks with high loadings on past tail risk earn an annual three-factor alpha 5.4% higher than stocks with low tail risk loadings. These findings are consistent with asset pricing theories that relate equity risk premia to rare disasters or other forms of tail risk.

Download or read book Tail Risk Measurement In Crypto asset Markets written by Daniel Felix Ahelegbey and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Tail Risk Measures and Loss Distributions written by Tomer Shushi and published by . This book was released on 2016 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Measuring Tail Risks at High Frequency written by Brian Weller and published by . This book was released on 2018 with total page 61 pages. Available in PDF, EPUB and Kindle. Book excerpt: I exploit information in the cross section of bid-ask spreads to develop a new measure of extreme event risk. Spreads embed tail risk information because liquidity providers require compensation for the possibility of sharp changes in asset values. I show that simple regressions relating spreads and trading volume to factor betas recover this information and deliver high-frequency tail risk estimates for common factors in stock returns. My methodology disentangles financial and aggregate market risks during the 2007-2008 Financial Crisis; quantifies jump risks associated with Federal Open Market Committee announcements; and anticipates an extreme liquidity shock before the 2010 Flash Crash.

Download or read book A New Approach to Tail Risk written by Ana Cascon and published by . This book was released on 2015 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the fundamental requirements of investment management is the ability to assess risk and to adjust exposure to control tail risk, the risk of larger than acceptable losses. Since the onset of the recent credit crisis, the effects of widespread failure of standard techniques for tail risk management have been an almost daily feature in the financial news.The most widely used approach to risk assessment by large financial institutions is the statistical tool known as Value at Risk (VaR). In fact, VaR is the risk measure commonly accepted by bank regulators in the banks' internal models for regulatory capital calculations (International Money Fund 2007). Conventional VaR and Conditional Value at Risk (CVaR) are useful, however, only if their implementation is consistent with the nature of the data. Conventional VaR and CVaR assume that data come from a normal distribution. We examine some of the failings of using this approach with tail risk in a number of examples from 2007 and 2008.To make VaR and CVaR work, it is important to correctly identify the nature of the tails that these techniques try to estimate. For this we introduce tail risk bands, a practical risk measurement tool that categorizes risk levels and identifies assets and market conditions for which conventional VaR cannot be expected to adequately represent downside risk.We illustrate our approach on daily data from equity markets and monthly data from hedge funds. In particular we show that this analysis provided warning of the riskiness of AIG, JPMorgan Chase, Lehman Brothers, the S&P 500 Index, and other equity indexes well in advance of the credit crisis. For the cases where tail risk bands indicate that the conventional VaR model cannot work, we provide a simple, easy-to-implement alternative. This is appropriate in the case of moderately heavy tails, which are common in financial data. This alternative is easily substituted for the standard approach and, as we show in a number of examples, provides a more realistic estimate of risk. We show that this can be used to make risk-adjusted comparisons of assets, using Berkshire Hathaway and JPMorgan Chase shares and the S&P 500 Index as examples. Finally, we provide evidence that during periods of unusual market turmoil only specialized techniques designed to deal with the statistics of extremes are likely to adequately assess the probability or the size of large losses.

Download or read book Tail Risk in Production Networks written by Ian Dew-Becker and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper describes the response of the economy to large shocks in a nonlinear production network. While arbitrary combinations of shocks can be studied, it focuses on a sector's tail centrality, which quantifies the effect of a large negative shock to the sector - a measure of the systemic risk of each sector. Tail centrality is theoretically and empirically very different from local centrality measures such as sales share - in a benchmark case, it is measured as a sector's average downstream closeness to final production. The paper then uses the results to analyze the determinants of total tail risk in the economy. Increases in interconnectedness in the presence of complementarity can simultaneously reduce the sensitivity of the economy to small shocks while increasing the sensitivity to large shocks. Tail risk is strongest in economies that display conditional granularity, where some sectors become highly influential following negative shocks.

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.