Three Essays on Estimation, Forecasting and Evaluation of Financial Risk PDF Download
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Author: Timo Dimitriadis
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Languages : en
Pages : 0
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Author: Timo Dimitriadis
Publisher:
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Languages : en
Pages : 0
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Languages : en
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Author: Sebastian Bayer
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Languages : en
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Author: Lidan Grossmass
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Languages : en
Pages : 0
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Author: Valeri Voev
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Languages : en
Pages : 0
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Author: Jon Danielsson
Publisher: John Wiley & Sons
ISBN: 1119977118
Category : Business & Economics
Languages : en
Pages : 307
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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: Serge Luther Nyawa Womo
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Languages : en
Pages : 0
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This thesis is about financial risks and high frequency data, with a particular focus on financial systemic risk, the risk of high dimensional portfolios and market microstructure noise. It is organized on three chapters. The first chapter provides a continuous time reduced-form model for the propagation of negative idiosyncratic shocks within a financial system. Using common factors and mutually exciting jumps both in price and volatility, we distinguish between sources of systemic failure such as macro risk drivers, connectedness and contagion. The estimation procedure relies on the GMM approach and takes advantage of high frequency data. We use models' parameters to define weighted, directed networks for shock transmission, and we provide new measures for the financial system fragility. We construct paths for the propagation of shocks, firstly within a number of key US banks and insurance companies, and secondly within the nine largest S&P sectors during the period 2000-2014. We find that beyond common factors, systemic dependency has two related but distinct channels: price and volatility jumps. In the second chapter, we develop a new factor-based estimator of the realized covolatility matrix, applicable in situations when the number of assets is large and the high-frequency data are contaminated with microstructure noises. Our estimator relies on the assumption of a factor structure for the noise component, separate from the latent systematic risk factors that characterize the cross-sectional variation in the frictionless returns. The new estimator provides theoretically more efficient and finite-sample more accurate estimates of large-scale integrated covolatility, correlation, and inverse covolatility matrices than other recently developed realized estimation procedures. These theoretical and simulation-based findings are further corroborated by an empirical application related to portfolio allocation and risk minimization involving several hundred individual stocks. The last chapter presents a factor-based methodology to estimate microstructure noise characteristics and frictionless prices under a high dimensional setup. We rely on factor assumptions both in latent returns and microstructure noise. The methodology is able to estimate rotations of common factors, loading coefficients and volatilities in microstructure noise for a huge number of stocks. Using stocks included in the S&P500 during the period spanning January 2007 to December 2011, we estimate microstructure noise common factors and compare them to some market-wide liquidity measures computed from real financial variables. We obtain that: the first factor is correlated to the average spread and the average number of shares outstanding; the second and third factors are related to the spread; the fourth and fifth factors are significantly linked to the closing log-price. In addition, volatilities of microstructure noise factors are widely explained by the average spread, the average volume, the average number of trades and the average trade size.
Author: Wei He
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Languages : en
Pages :
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This Ph.D. dissertation contains three individual and internally related essays. The first essay applies the least-squares Monte-Carlo (LSM) methodology to derive the distribution of the exotic option values at a future time. LSM presents a powerful statistical procedure that efficiently yields derivative distributions for exotic options that do not possess analytic solutions. By means of several examples, using options with closed-from solutions, this essay demonstrates the ability of LSM to produce excellent estimates of derivative distribution at a reasonable computational cost. The second and third essays compare two of the major credit risk portfolio models used by two prominent financial companies: J. P. Morgan's CreditMetrics and Credit Swiss First Boston's CreditRisk+. The second essay compares the two models from a methodological and an empirical point of view. Factor Analysis is utilized to link the different input data employed by these two models. The third essay creates a hypothetical world in which the true transition matrices are known so that a benchmark distribution of portfolio loss is derived to evaluate the model's performance. The results suggest that despite the fact that the recommendations made by each approach to a financial institution trying to determine how much economic capital to hold is different, these two models perform equally well when credit-rating-change risk is eliminated from the CreditMetrics approach.
Author: Guoshi Tong
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Christian Mücher
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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