LEADER 06019nam 2200817Ia 450 001 9910139552503321 005 20230622192328.0 010 $a1-119-97711-8 010 $a1-119-20586-7 010 $a1-283-40512-1 010 $a9786613405128 010 $a1-119-97710-X 035 $a(CKB)2550000000064895 035 $a(EBL)699340 035 $a(OCoLC)760884478 035 $a(SSID)ssj0000648807 035 $a(PQKBManifestationID)12295967 035 $a(PQKBTitleCode)TC0000648807 035 $a(PQKBWorkID)10600951 035 $a(PQKB)10878834 035 $a(MiAaPQ)EBC699340 035 $a(Au-PeEL)EBL699340 035 $a(CaPaEBR)ebr10510640 035 $a(CaONFJC)MIL340512 035 $a(EXLCZ)992550000000064895 100 $a20120816d2011 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aFinancial risk forecasting$b[electronic resource] $ethe theory and practice of forecasting market risk, with implementation in R and Matlab /$fJo?n Dani?elsson 210 $aChichester, West Sussex, U.K. $cWiley$d2011 215 $a1 online resource (298 p.) 225 1 $aWiley finance series 300 $aDescription based upon print version of record. 311 $a0-470-66943-8 320 $aIncludes bibliographical references (p. [255]-258) and index. 327 $aFinancial Risk Forecasting; Contents; Preface; Acknowledgments; Abbreviations; Notation; 1 Financial markets, prices and risk; 1.1 Prices, returns and stock indices; 1.1.1 Stock indices; 1.1.2 Prices and returns; 1.2 S&P 500 returns; 1.2.1 S&P 500 statistics; 1.2.2 S&P 500 statistics in R and Matlab; 1.3 The stylized facts of financial returns; 1.4 Volatility; 1.4.1 Volatility clusters; 1.4.2 Volatility clusters and the ACF; 1.5 Nonnormality and fat tails; 1.6 Identification of fat tails; 1.6.1 Statistical tests for fat tails; 1.6.2 Graphical methods for fat tail analysis 327 $a1.6.3 Implications of fat tails in finance1.7 Nonlinear dependence; 1.7.1 Sample evidence of nonlinear dependence; 1.7.2 Exceedance correlations; 1.8 Copulas; 1.8.1 The Gaussian copula; 1.8.2 The theory of copulas; 1.8.3 An application of copulas; 1.8.4 Some challenges in using copulas; 1.9 Summary; 2 Univariate volatility modeling; 2.1 Modeling volatility; 2.2 Simple volatility models; 2.2.1 Moving average models; 2.2.2 EWMA model; 2.3 GARCH and conditional volatility; 2.3.1 ARCH; 2.3.2 GARCH; 2.3.3 The ''memory'' of a GARCH model; 2.3.4 Normal GARCH; 2.3.5 Student-t GARCH 327 $a2.3.6 (G)ARCH in mean2.4 Maximum likelihood estimation of volatility models; 2.4.1 The ARCH(1) likelihood function; 2.4.2 The GARCH(1,1) likelihood function; 2.4.3 On the importance of ?1; 2.4.4 Issues in estimation; 2.5 Diagnosing volatility models; 2.5.1 Likelihood ratio tests and parameter significance; 2.5.2 Analysis of model residuals; 2.5.3 Statistical goodness-of-fit measures; 2.6 Application of ARCH and GARCH; 2.6.1 Estimation results; 2.6.2 Likelihood ratio tests; 2.6.3 Residual analysis; 2.6.4 Graphical analysis; 2.6.5 Implementation; 2.7 Other GARCH-type models 327 $a2.7.1 Leverage effects and asymmetry2.7.2 Power models; 2.7.3 APARCH; 2.7.4 Application of APARCH models; 2.7.5 Estimation of APARCH; 2.8 Alternative volatility models; 2.8.1 Implied volatility; 2.8.2 Realized volatility; 2.8.3 Stochastic volatility; 2.9 Summary; 3 Multivariate volatility models; 3.1 Multivariate volatility forecasting; 3.1.1 Application; 3.2 EWMA; 3.3 Orthogonal GARCH; 3.3.1 Orthogonalizing covariance; 3.3.2 Implementation; 3.3.3 Large-scale implementations; 3.4 CCC and DCC models; 3.4.1 Constant conditional correlations (CCC); 3.4.2 Dynamic conditional correlations (DCC) 327 $a3.4.3 Implementation3.5 Estimation comparison; 3.6 Multivariate extensions of GARCH; 3.6.1 Numerical problems; 3.6.2 The BEKK model; 3.7 Summary; 4 Risk measures; 4.1 Defining and measuring risk; 4.2 Volatility; 4.3 Value-at-risk; 4.3.1 Is VaR a negative or positive number?; 4.3.2 The three steps in VaR calculations; 4.3.3 Interpreting and analyzing VaR; 4.3.4 VaR and normality; 4.3.5 Sign of VaR; 4.4 Issues in applying VaR; 4.4.1 VaR is only a quantile; 4.4.2 Coherence; 4.4.3 Does VaR really violate subadditivity?; 4.4.4 Manipulating VaR; 4.5 Expected shortfall 327 $a4.6 Holding periods, scaling and the square root of time 330 $aFinancial Risk Forecasting is a complete introduction to practical quantitative risk management, with a focus on market risk. Derived from the authors teaching notes and years spent training practitioners in risk management techniques, it brings together the three key disciplines of finance, statistics and modeling (programming), to provide a thorough grounding in risk management techniques. Written by renowned risk expert Jon Danielsson, the book begins with an introduction to financial markets and market prices, volatility clusters, fat tails and nonlinear dependence. It then goes o 410 0$aWiley finance series. 606 $aFinancial risk management$xForecasting 606 $aFinancial risk management$xSimulation methods 606 $aR (Computer program language) 606 $aGestió financera$2thub 606 $aGestió del risc$2thub 606 $aPrevisió$2thub 606 $aMètodes de simulació$2thub 608 $aLlibres electrònics$2thub 615 0$aFinancial risk management$xForecasting. 615 0$aFinancial risk management$xSimulation methods. 615 0$aR (Computer program language). 615 7$aGestió financera 615 7$aGestió del risc 615 7$aPrevisió 615 7$aMètodes de simulació 676 $a658.155 676 $a658.1550112 700 $aDani?elsson$b Jo?n$0375914 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910139552503321 996 $aFinancial risk forecasting$91136804 997 $aUNINA