Holdings: Applied quantitative finance

Applied quantitative finance:

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Bibliographic Details
Other Authors:	Härdle, Wolfgang 1953- (Editor)
Format:	Book
Language:	English
Published:	Berlin [u.a.] Springer 2009
Edition:	2. ed.
Subjects:	Mathematisches Modell Finance > Mathematical models Risk > Mathematical models Financial Engineering Finanzmathematik
Online Access:	Inhaltsverzeichnis
Physical Description:	XXVI, 447 S. zahlr. graph. Darst. 24 cm
ISBN:	9783540691778

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adam_text	Contents Preface to the 2nd Edition v Preface to the 1st Edition vii Contributors xxi Frequently Used Notation xxv I Value at Risk 1 1 Modeling Dependencies with Copulae 3 Wolfgang Hardie, Ostap Okhrin ami Yarema Oklirin 1.1 Introduction ............................ 3 1.2 Divariate Copulae ......................... 4 1.2.1 Copula Families ...................... 6 1.2.2 Dependence Measures .................. 9 1.3 Multivariate Copulae ....................... 11 1.3.1 Copula Families ...................... 13 1.3.2 Dependence Measures .................. 15 1.4 Estimation Methods ....................... 17 1.5 Goodness-oi-Fit Tests for Copulae ................ 19 1.6 Simulation Methods ........................ 21 1.6.1 Conditional Inverse Method ............... 22 1.6.2 Marshal-Olkin Method .................. 22 1.7 Applications to Finance ..................... 23 1.7.1 Asset Allocation ..................... 24 1.7.2 Value-at-Risk ....................... 25 1.7.3 Time Series Modeling ................... 26 1.8 Simulation Study and Empirical Results ............ 28 1.8.1 Simulation Study ..................... 28 1.8.2 Empirical Example .................... 30 1.9 Summary ............................. 33 xii Contents 2 Quantification of Spread Risk by Means of Historical Simulation 37 Christoph Frisch and Germax Knöchlcin 2.1 Introduction ............................ 37 2.2 Risk Categories - a Defìnition of Terms ............. 37 2.3 Yield Spread Time Series ..................... 39 2.3.1 Data Analysis ....................... 40 2.3.2 Discussion of Results ................... 44 2.4 Historical Simulation and Value at Risk ............. 49 2.4.1 Risk Factor: Full Yield .................. 49 2.4.2 Risk Factor: Benchmark ................. 52 2.4.3 Risk Factor: Spread over Benchmark Yield ....... 53 2.4.4 Conservative Approach .................. 54 2.4.5 Simultaneous Simulation ................. 54 2.5 Mark-to-Model Backtesting ................... 54 2.6 Vali Estimation and Backtesting ................ 55 2.7 P-P Plots ............................. 59 2.8 Q-Q Plots ............................. 60 2.9 Discussion of Simulation Results ................. 60 2.9.1 Risk Factor: Full Yield .................. 60 2.9.2 Risk Factor: Benchmark ................. 61 2.9.3 Risk Factor: Spread over Benchmark Yield ....... 61 2.9.4 Conservative Approach .................. 62 2.9.5 Simultaneous Simulation ................. 62 2.10 Internal Risk Models ....................... 63 3 A Copula-Based Model of the Term Structure of CDO Tranches 69 Umberto Cherubini, Sabrina Mulinacci and Silvia Romagnoh 3.1 Introduction ............................ 69 3.2 A Copula-Based Model of Basket Credit Losses Dynamics . . 71 3.3 Stochastic Processes with Dependent Increments ........ 72 3.4 An Algorithm for the Propagation of Losses .......... 75 3.5 Empirical Analysis ........................ 76 3.6 Concluding Remarks ....................... 80 4 Va R in High Dimensional Systems - a Conditional Correlation Approach 83 Helmut Herwaxtz and Bruno Pedrinha 4.1 Introduction ............................ 83 4.2 Half-Vec Multivaxiate GARCH Modeb ............. 85 4.3 Correlation Models ........................ 86 4.3.1 Motivation ......................... 86 Contents xiii 4.3.2 Log-Likelihood Decomposition .............. 87 4.3.3 Constant Conditional Correlation Model ........ 88 4.3.4 Dynamic Conditional Correlation Model ........ 89 4.3.5 Inference in the Correlation Models .......... 90 4.3.6 Generalizations of the DCC Model ........... 92 4.4 Valuc-at-Risk ........................... 92 4.5 An Empirical Illustration ..................... 93 4.5.1 Equal and Value Weighted Portfolios .......... 93 4.5.2 Estimation Results .................... 96 II Credit Risk 103 5 Rating Migrations 105 Steffi Hose, Stefan Huscheus and Robert Warna 5.1 Rating Transition Probabilities .................106 5.1.1 From Credit Events to Migration Counts ........ 106 5.1.2 Estimating Rating Transition Probabilities ....... 107 5.1.3 Dependent Migrations .................. 108 5.1.4 Computational Aspects .................. Ill 5.2 Analyzing the Time-Stability of Transition Probabilities . . . . Ill 5.2.1 Aggregation over Periods ................. Ill 5.2.2 Testing the Time-Stability of Transition Probabilities . 112 5.2.3 Example ..........................114 ¿5.2.4 Computational Aspects ..................115 5.3 Multi-Period Transitions .....................115 5.3.1 Homogeneous Markov Chain ...............116 5.3.2 Bootstrapping Markov Chains ..............117 5.3.3 Rating Transitions of German Bank Borrowers .....118 5.3.4 Portfolio Migration ....................119 5.3.5 Computational Aspects ..................121 6 Cross- and Autocorrelation in Multi-Period Credit Portfolio Models 125 Christoph K.J. Wagner 6.1 Introduction ............................125 6.2 Tie Modeis............................127 6.2.1 A Markov-Chain Credit Migration Model ........ 127 6.2.2 The Correlated-Default-Time Model .......... 130 6.2.3 A Discrete Banner Model ................. 132 6.2.4 The Time-Changed Barrier MoíM ............ 133 xiv Contents 6.3 Inter-Temporal Dependency and Autocorrelation .......135 6.4 Conclusion .............................137 7 Risk Measurement with Spectral Capital Allocation 139 Ludgcr Overbeck and Maria Sokolova 7.1 Introduction ............................139 7.2 Review of Coherent Risk Measures and Allocation .......140 7.2.1 Coherent Risk Measures ................. 140 7.2.2 Spectral Risk Measures .................. 143 7.2.3 Coherent Allocation Measures .............. 144 7.2.4 Spectral Allocation Measures .............. 145 7.3 Weight Function and Mixing Measure .............. 146 7.4 Risk Aversion ........................... 146 7.5 Implementation .......................... 147 7.5.1 Mixing Representation .................. 148 7.5.2 Density Representation .................. 149 7.6 Credit Portfolio Model ...................... 149 7.7 Examples ............................. 150 7.7.1 Weighting Scheme ....................150 7.7.2 Concrete Example ....................151 7.8 Summary .............................158 8 Valuation and Va R Computation for CDOs Using Stein s Method 161 Nicole El Karoui, Ying Jiao, David Kurtz 8.1 Introduction ............................161 8.1.1 A Primer on CDO ....................161 8.1.2 Factor Models .......................163 8.1.3 Numerical Algoritlmis ..................164 8.2 First Order Gauss-Poisson Approximations ...........165 8.2.1 Stem s Method - the Normal Case ............165 8.2.2 First-Order Gaussian Approximation ..........167 8.2.3 Stem s Method - the Poisson Case ............171 8.2.4 First-Order Poisson Approximation ...........172 8.3 Numerical Tests ..........................175 8.3.1 Validity Domain of the Approximations .........175 8.3.2 Stochastic Recovery Rate - Gaussian Case .......177 8.3.3 Sensitivity Analysis ....................179 8.4 Real Life Applications ......................180 8.4.1 Gaussian Approximation .................180 8.4.2 Poisson Approximation ..................181 Coutente 8.4.3 CDO Valuation ......................182 8.4.4 Robustness of VaR Computation ............184 III Implied Volatility 191 9 Least Squares Kernel Smoothing of the Implied Volatility Smile 193 Matthias R. Fengler and Qihna Wang 9.1 Introduction ............................193 9.2 Least Squares Kernel Smoothing of the Smile .........194 9.3 Application ............................197 9.3.1 Weighting Functions, Kernels, and Minimization Scheme ..........................197 .9.3.2 Data Description and Empirical Demonstration .... 198 9.4 Proońi ...............................203 10 Numerics of Implied Binomial Trees 209 Wolfgang Hardie and AJena Myšičková 10.1 Construction of the IBT .....................210 10.1.1 The Derman and Kani Algorithm ............212 10.1.2 Compensation .......................218 10.1.3 Baric and Calcici Algorithm ...............219 10.2 A Simulation and a Comparison of the SPDs ..........220 10.2.1 Simulation Using the DK Algorithm .......... 221 10.2.2 Shnulation Using the BG Algorithm ........... 223 10.2.3 Comparison with the Monte-Carlo Simulation ..... 224 10.3 Example- Analysis of EUREXData .............. 227 11 Application of Extended Kalman Filter to SPD Estimation 233 Zdeněk Hlávka and Marek Svojik 11.1 Linear Model ...........................234 11.1.1 Linear Model for Call Option Prices ...........235 11.1.2 Estimation of State Price Density ............236 11.1.3 State-Space Model for Call Option Prices ........237 11.2 Extended Kalman Filter and Call Options ...........238 11.3 Empirical Results .........................239 11.3.1 Extended Kalman Filtering in Practice .........240 11.3.2 SPD Estimation in 1995.................241 11.3.3 SPD Estimation in 2003.................243 11.4 Conclusions ............................245 XVI Contents 12 Stochastic Volatility Estimation Using Markov Chain Simulation 249 Nikolaus Ilautsch and Yangguoyi Ou 12.1 The Standard Stochastic Volatility Model ...........250 12.2 Extended SV Models .......................252 12.2.1 Fat Tails and Jumps ...................252 12.2.2 The Relationship Between Volatility and Returns . . . 254 12.2.3 The Long Memory SV Model ..............256 12.3 MCMC-Based Bayesian inference ................257 12.3.1 Bayes Theorem and the MCMC Algorithm ......257 12.3.2 MGMG-Bascd Estimation of the Standard SV Model . 261 12.4 Empirical Illustrations ......................264 12.4.1 The Data .........................264 12.4.2 Estimation of SV Models .................265 12.5 Appendix .............................270 12.5.1 Derivation of the Conditional Posterior Distributions . 270 13 Measuring and Modeling Risk Using High-Frequency Data 275 Wolfgang Härdle, Nikolaus Hantsch and Uta Pigorsch 13.1 Introduction ............................275 13.2 Market Microstructure Effects ..................277 13.3 Stylized Facts of Realized Volatility ...............280 13.4 Realized Volatility Models ....................284 13.5 Time-Varying Betas ........................285 13.5.1 The Conditional CAPM .................286 23.5.2 Realized Betas ......................287 13.6 Summary .............................289 14 Valuation of Multidimensional Bermudán Options 295 Shih-Feng Huang and Meihui Quo 14.1 Introduction ............................295 14.2 Model Assumptions ........................296 14.3 Methodology ...........................298 14.4 Examples .............................302 14.5 Conclusion .............................308 Contents xvii IV Econometrics 311 15 Multivariate Volatility Models 313 Matthias R. Fengler and Helmut //erwarte 15.1 Introduction ............................313 15.1.1 Model Speciucations ...................314 15.1.2 Estimation of the BEKK-Model .............316 15.2 Ah Empirica] Illustration .....................317 15.2.1 Data Description ..................... 317 15.2.2 Estimating Divariate GARCE.............. 318 15.2.3 Estimating the (Go) Variance Processes ......... 320 15.3 Forecasting Exchange Rate Densities .............. 323 16 The Accuracy of Long-term Real Estate Valuations 327 Rainer Schub, Markus Staiber, Martin Wersing and Axel Werwatz 16.1 Introduction ............................327 16.2 Implementation..........................328 16.2.1 Computation of the Valuations ............. 329 16.2.2 Data ............................331 16.3 Empirical Results .........................333 16.3.1 Characterization of the Test Market ...........333 16.3.2 Horse Race ........................337 16.4 Conclusion .............................343 17 Locally Time Homogeneous Time Series Modelling 345 Mstislav Elagin and Vladimir Spokoiny 17.1 Introduction ............................345 17.2 Model and Setup .........................346 17.2.1 Conditional Heteroskedastic Model ...........346 17.2.2 Parametric and Local Parametric Estimation and Inference ..........................347 17.2.3 Nearly Parametric Case .................348 17.3 Methods for the Estimation of Parameters ...........349 17.3.1 Sequence of Intervals ................... 349 17.3.2 Local Change Point Selection .............. 349 17.3.3 Local Model Selection .................. 350 17.3.4 Stagewise Aggregation .................. 351 17.4 Critical Values and Other Parameters .............. 352 17.5 AppMcations ............................ 354 17.5.1 Forecasting Performance for One and Multiple Steps . 355 Contents 17.5.2 Value-at-Risk .......................357 17.5.3 A Multiple Time Series Example ............359 18 Simulation Based Option Pricing 363 Denis Belomestny and Grígorí N. Mustéin 18.1 Introduction ............................363 18.2 The Consumption Based Processes ...............365 18.2.1 The Snell Envelope ....................365 18.2.2 The Continuation Value, the Continuation and Exercise Regions .....................366 18.2.3 Equivalence of American Options to European Ones with Consumption Processes ...............367 18.2.4 Upper and Lower Bounds Using Consumption Processes ......................... 367 18.2.5 Bermudán Options .................... 368 18.3 The Main Procedure ....................... 369 28.3.2 Local Lower Bounds ................... 369 28.3.2 The Main Procedure for Constructing Upper Bounds for the Initial Position (Global Upper Bounds) .....370 28.3.3 Tie Main Procedure for Constructing Lower Bounds for the Initiai Position (Global Lower Bounds) ..... 372 28.3.4 Kernéi ínterpoiation................... 373 28.4 Simulations ............................ 374 18.4.2 Bermudán Max Calis on d Assets ............ 374 28.4.2 Bermudán Basket-Put .................. 375 18.5 Conclusions ............................377 19 High-Frequency Volatility and Liquidity 379 Nikolaus Hautsch and V&hidin Jeleskovic 19.1 Introduction ............................379 29.2 The XMvariate MEM .......................380 19.3 The Vector MEM ................. ........383 19.4 Statistical inference ........................385 29.5 ffigh-Erequency Volatility and Liquidity Dynamics .......387 20 Statistical Process Control in Asset Management 399 Vasyl Golosaoy and Wolfgang Schmid 20.1 Introduction ............................399 20.2 Review of Statistical Process Control Concepts .........400 20.3 Applications of SPC in Asset Management ...........403 Contents xix 20.3.1 Monitoring Active Portfolio Managers .........404 20.3.2 Surveillance of the Optimal Portfolio Proportions . . . 408 20.4 Summary .............................414 21 Canonical Dynamics Mechanism of Monetary Policy and Interest Rate 417 Jcnher Jeng, Wei-Fang Niu, Nan-Jyc Wang, and Shih-Shan Lin 21.1 Introduction ............................417 21.2 Statistical Technology ......................419 21.3 Principles of the Fed Funds Rate Decision-Making .......424 21.3.1 Fairness of Inflation Gauge ................ 424 21.3.2 Neutral Interest Rate Based on Fair Gauge of inflation 425 21.3.3 Monetary Policy-Making as Tight-Accommodative Cy¬ cles Along Neutral Level as Dynamic Principal ..... 427 21.4 Response Curve Structure and FOM С Behavioral Analysis . . 428 21.4.1 Data Analysis and Regressive Results ..........428 21.4.2 The Structure of the FOMC s Response Curve - Model Characteristics, Interpretations .............429 21.4.3 The Dynamics of the FFR - Model Implications .... 432 21.4.4 General Dynamic Mechanism for Long-Run Dependence of Interest Rate and Inflation ..............437 27.5 Discussions and Conclusions ...................439 Index 443
adam_txt	Contents Preface to the 2nd Edition v Preface to the 1st Edition vii Contributors xxi Frequently Used Notation xxv I Value at Risk 1 1 Modeling Dependencies with Copulae 3 Wolfgang Hardie, Ostap Okhrin ami Yarema Oklirin 1.1 Introduction . 3 1.2 Divariate Copulae . 4 1.2.1 Copula Families . 6 1.2.2 Dependence Measures . 9 1.3 Multivariate Copulae . 11 1.3.1 Copula Families . 13 1.3.2 Dependence Measures . 15 1.4 Estimation Methods . 17 1.5 Goodness-oi-Fit Tests for Copulae . 19 1.6 Simulation Methods . 21 1.6.1 Conditional Inverse Method . 22 1.6.2 Marshal-Olkin Method . 22 1.7 Applications to Finance . 23 1.7.1 Asset Allocation . 24 1.7.2 Value-at-Risk . 25 1.7.3 Time Series Modeling . 26 1.8 Simulation Study and Empirical Results . 28 1.8.1 Simulation Study . 28 1.8.2 Empirical Example . 30 1.9 Summary . 33 xii Contents 2 Quantification of Spread Risk by Means of Historical Simulation 37 Christoph Frisch and Germax Knöchlcin 2.1 Introduction . 37 2.2 Risk Categories - a Defìnition of Terms . 37 2.3 Yield Spread Time Series . 39 2.3.1 Data Analysis . 40 2.3.2 Discussion of Results . 44 2.4 Historical Simulation and Value at Risk . 49 2.4.1 Risk Factor: Full Yield . 49 2.4.2 Risk Factor: Benchmark . 52 2.4.3 Risk Factor: Spread over Benchmark Yield . 53 2.4.4 Conservative Approach . 54 2.4.5 Simultaneous Simulation . 54 2.5 Mark-to-Model Backtesting . 54 2.6 Vali Estimation and Backtesting . 55 2.7 P-P Plots . 59 2.8 Q-Q Plots . 60 2.9 Discussion of Simulation Results . 60 2.9.1 Risk Factor: Full Yield . 60 2.9.2 Risk Factor: Benchmark . 61 2.9.3 Risk Factor: Spread over Benchmark Yield . 61 2.9.4 Conservative Approach . 62 2.9.5 Simultaneous Simulation . 62 2.10 Internal Risk Models . 63 3 A Copula-Based Model of the Term Structure of CDO Tranches 69 Umberto Cherubini, Sabrina Mulinacci and Silvia Romagnoh 3.1 Introduction . 69 3.2 A Copula-Based Model of Basket Credit Losses Dynamics . . 71 3.3 Stochastic Processes with Dependent Increments . 72 3.4 An Algorithm for the Propagation of Losses . 75 3.5 Empirical Analysis . 76 3.6 Concluding Remarks . 80 4 Va R in High Dimensional Systems - a Conditional Correlation Approach 83 Helmut Herwaxtz and Bruno Pedrinha 4.1 Introduction . 83 4.2 Half-Vec Multivaxiate GARCH Modeb . 85 4.3 Correlation Models . 86 4.3.1 Motivation . 86 Contents xiii 4.3.2 Log-Likelihood Decomposition . 87 4.3.3 Constant Conditional Correlation Model . 88 4.3.4 Dynamic Conditional Correlation Model . 89 4.3.5 Inference in the Correlation Models . 90 4.3.6 Generalizations of the DCC Model . 92 4.4 Valuc-at-Risk . 92 4.5 An Empirical Illustration . 93 4.5.1 Equal and Value Weighted Portfolios . 93 4.5.2 Estimation Results . 96 II Credit Risk 103 5 Rating Migrations 105 Steffi Hose, Stefan Huscheus and Robert Warna 5.1 Rating Transition Probabilities .106 5.1.1 From Credit Events to Migration Counts . 106 5.1.2 Estimating Rating Transition Probabilities . 107 5.1.3 Dependent Migrations . 108 5.1.4 Computational Aspects . Ill 5.2 Analyzing the Time-Stability of Transition Probabilities . . . . Ill 5.2.1 Aggregation over Periods . Ill 5.2.2 Testing the Time-Stability of Transition Probabilities . 112 5.2.3 Example .114 ¿5.2.4 Computational Aspects .115 5.3 Multi-Period Transitions .115 5.3.1 Homogeneous Markov Chain .116 5.3.2 Bootstrapping Markov Chains .117 5.3.3 Rating Transitions of German Bank Borrowers .118 5.3.4 Portfolio Migration .119 5.3.5 Computational Aspects .121 6 Cross- and Autocorrelation in Multi-Period Credit Portfolio Models 125 Christoph K.J. Wagner 6.1 Introduction .125 6.2 Tie Modeis.127 6.2.1 A Markov-Chain Credit Migration Model . 127 6.2.2 The Correlated-Default-Time Model . 130 6.2.3 A Discrete Banner Model . 132 6.2.4 The Time-Changed Barrier MoíM . 133 xiv Contents 6.3 Inter-Temporal Dependency and Autocorrelation .135 6.4 Conclusion .137 7 Risk Measurement with Spectral Capital Allocation 139 Ludgcr Overbeck and Maria Sokolova 7.1 Introduction .139 7.2 Review of Coherent Risk Measures and Allocation .140 7.2.1 Coherent Risk Measures . 140 7.2.2 Spectral Risk Measures . 143 7.2.3 Coherent Allocation Measures . 144 7.2.4 Spectral Allocation Measures . 145 7.3 Weight Function and Mixing Measure . 146 7.4 Risk Aversion . 146 7.5 Implementation . 147 7.5.1 Mixing Representation . 148 7.5.2 Density Representation . 149 7.6 Credit Portfolio Model . 149 7.7 Examples . 150 7.7.1 Weighting Scheme .150 7.7.2 Concrete Example .151 7.8 Summary .158 8 Valuation and Va R Computation for CDOs Using Stein's Method 161 Nicole El Karoui, Ying Jiao, David Kurtz 8.1 Introduction .161 8.1.1 A Primer on CDO .161 8.1.2 Factor Models .163 8.1.3 Numerical Algoritlmis .164 8.2 First Order Gauss-Poisson Approximations .165 8.2.1 Stem's Method - the Normal Case .165 8.2.2 First-Order Gaussian Approximation .167 8.2.3 Stem's Method - the Poisson Case .171 8.2.4 First-Order Poisson Approximation .172 8.3 Numerical Tests .175 8.3.1 Validity Domain of the Approximations .175 8.3.2 Stochastic Recovery Rate - Gaussian Case .177 8.3.3 Sensitivity Analysis .179 8.4 Real Life Applications '.180 8.4.1 Gaussian Approximation .180 8.4.2 Poisson Approximation .181 Coutente 8.4.3 CDO Valuation .182 8.4.4 Robustness of VaR Computation .184 III Implied Volatility 191 9 Least Squares Kernel Smoothing of the Implied Volatility Smile 193 Matthias R. Fengler and Qihna Wang 9.1 Introduction .193 9.2 Least Squares Kernel Smoothing of the Smile .194 9.3 Application .197 9.3.1 Weighting Functions, Kernels, and Minimization Scheme .197 .9.3.2 Data Description and Empirical Demonstration . 198 9.4 Proońi .203 10 Numerics of Implied Binomial Trees 209 Wolfgang Hardie and AJena Myšičková 10.1 Construction of the IBT .210 10.1.1 The Derman and Kani Algorithm .212 10.1.2 Compensation .218 10.1.3 Baric and Calcici Algorithm .219 10.2 A Simulation and a Comparison of the SPDs .220 10.2.1 Simulation Using the DK Algorithm . 221 10.2.2 Shnulation Using the BG Algorithm . 223 10.2.3 Comparison with the Monte-Carlo Simulation . 224 10.3 Example- Analysis of EUREXData . 227 11 Application of Extended Kalman Filter to SPD Estimation 233 Zdeněk Hlávka and Marek Svojik 11.1 Linear Model .234 11.1.1 Linear Model for Call Option Prices .235 11.1.2 Estimation of State Price Density .236 11.1.3 State-Space Model for Call Option Prices .237 11.2 Extended Kalman Filter and Call Options .238 11.3 Empirical Results .239 11.3.1 Extended Kalman Filtering in Practice .240 11.3.2 SPD Estimation in 1995.241 11.3.3 SPD Estimation in 2003.243 11.4 Conclusions .245 XVI Contents 12 Stochastic Volatility Estimation Using Markov Chain Simulation 249 Nikolaus Ilautsch and Yangguoyi Ou 12.1 The Standard Stochastic Volatility Model .250 12.2 Extended SV Models .252 12.2.1 Fat Tails and Jumps .252 12.2.2 The Relationship Between Volatility and Returns . . . 254 12.2.3 The Long Memory SV Model .256 12.3 MCMC-Based Bayesian 'inference .257 12.3.1 Bayes' Theorem and the MCMC Algorithm .257 12.3.2 MGMG-Bascd Estimation of the Standard SV Model . 261 12.4 Empirical Illustrations .264 12.4.1 The Data .264 12.4.2 Estimation of SV Models .265 12.5 Appendix .270 12.5.1 Derivation of the Conditional Posterior Distributions . 270 13 Measuring and Modeling Risk Using High-Frequency Data 275 Wolfgang Härdle, Nikolaus Hantsch and Uta Pigorsch 13.1 Introduction .275 13.2 Market Microstructure Effects .277 13.3 Stylized Facts of Realized Volatility .280 13.4 Realized Volatility Models .284 13.5 Time-Varying Betas .285 13.5.1 The Conditional CAPM .286 23.5.2 Realized Betas .287 13.6 Summary .289 14 Valuation of Multidimensional Bermudán Options 295 Shih-Feng Huang and Meihui Quo 14.1 Introduction .295 14.2 Model Assumptions .296 14.3 Methodology .298 14.4 Examples .302 14.5 Conclusion .308 Contents xvii IV Econometrics 311 15 Multivariate Volatility Models 313 Matthias R. Fengler and Helmut //erwarte 15.1 Introduction .313 15.1.1 Model Speciucations .314 15.1.2 Estimation of the BEKK-Model .316 15.2 Ah Empirica] Illustration .317 15.2.1 Data Description . 317 15.2.2 Estimating Divariate GARCE. 318 15.2.3 Estimating the (Go) Variance Processes . 320 15.3 Forecasting Exchange Rate Densities . 323 16 The Accuracy of Long-term Real Estate Valuations 327 Rainer Schub, Markus Staiber, Martin Wersing and Axel Werwatz 16.1 Introduction .327 16.2 Implementation.328 16.2.1 Computation of the Valuations . 329 16.2.2 Data .331 16.3 Empirical Results .333 16.3.1 Characterization of the Test Market .333 16.3.2 Horse Race .337 16.4 Conclusion .343 17 Locally Time Homogeneous Time Series Modelling 345 Mstislav Elagin and Vladimir Spokoiny 17.1 Introduction .345 17.2 Model and Setup .346 17.2.1 Conditional Heteroskedastic Model .346 17.2.2 Parametric and Local Parametric Estimation and Inference .347 17.2.3 Nearly Parametric Case .348 17.3 Methods for the Estimation of Parameters .349 17.3.1 Sequence of Intervals . 349 17.3.2 Local Change Point Selection . 349 17.3.3 Local Model Selection . 350 17.3.4 Stagewise Aggregation . 351 17.4 Critical Values and Other Parameters . 352 17.5 AppMcations . 354 17.5.1 Forecasting Performance for One and Multiple Steps . 355 Contents 17.5.2 Value-at-Risk .357 17.5.3 A Multiple Time Series Example .359 18 Simulation Based Option Pricing 363 Denis Belomestny and Grígorí N. Mustéin 18.1 Introduction .363 18.2 The Consumption Based Processes .365 18.2.1 The Snell Envelope .365 18.2.2 The Continuation Value, the Continuation and Exercise Regions .366 18.2.3 Equivalence of American Options to European Ones with Consumption Processes .367 18.2.4 Upper and Lower Bounds Using Consumption Processes . 367 18.2.5 Bermudán Options . 368 18.3 The Main Procedure . 369 28.3.2 Local Lower Bounds . 369 28.3.2 The Main Procedure for Constructing Upper Bounds for the Initial Position (Global Upper Bounds) .370 28.3.3 Tie Main Procedure for Constructing Lower Bounds for the Initiai Position (Global Lower Bounds) . 372 28.3.4 Kernéi ínterpoiation. 373 28.4 Simulations . 374 18.4.2 Bermudán Max Calis on d Assets . 374 28.4.2 Bermudán Basket-Put . 375 18.5 Conclusions .377 19 High-Frequency Volatility and Liquidity 379 Nikolaus Hautsch and V&hidin Jeleskovic 19.1 Introduction .379 29.2 The XMvariate MEM .380 19.3 The Vector MEM .'.383 19.4 Statistical inference .385 29.5 ffigh-Erequency Volatility and Liquidity Dynamics .387 20 Statistical Process Control in Asset Management 399 Vasyl Golosaoy and Wolfgang Schmid 20.1 Introduction .399 20.2 Review of Statistical Process Control Concepts .400 20.3 Applications of SPC in Asset Management .403 Contents xix 20.3.1 Monitoring Active Portfolio Managers .404 20.3.2 Surveillance of the Optimal Portfolio Proportions . . . 408 20.4 Summary .414 21 Canonical Dynamics Mechanism of Monetary Policy and Interest Rate 417 Jcnher Jeng, Wei-Fang Niu, Nan-Jyc Wang, and Shih-Shan Lin 21.1 Introduction .417 21.2 Statistical Technology .419 21.3 Principles of the Fed Funds Rate Decision-Making .424 21.3.1 Fairness of Inflation Gauge . 424 21.3.2 Neutral Interest Rate Based on Fair Gauge of inflation 425 21.3.3 Monetary Policy-Making as Tight-Accommodative Cy¬ cles Along Neutral Level as Dynamic Principal . 427 21.4 Response Curve Structure and FOM С Behavioral Analysis . . 428 21.4.1 Data Analysis and Regressive Results .428 21.4.2 The Structure of the FOMC's Response Curve - Model Characteristics, Interpretations .429 21.4.3 The Dynamics of the FFR - Model Implications . 432 21.4.4 General Dynamic Mechanism for Long-Run Dependence of Interest Rate and Inflation .437 27.5 Discussions and Conclusions .439 Index 443
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spellingShingle	Applied quantitative finance Mathematisches Modell Finance Mathematical models Risk Mathematical models Financial Engineering (DE-588)4208404-0 gnd Finanzmathematik (DE-588)4017195-4 gnd
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title	Applied quantitative finance
title_auth	Applied quantitative finance
title_exact_search	Applied quantitative finance
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title_full	Applied quantitative finance Wolfgang K. Härdle ... (eds.)
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title_full_unstemmed	Applied quantitative finance Wolfgang K. Härdle ... (eds.)
title_short	Applied quantitative finance
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topic	Mathematisches Modell Finance Mathematical models Risk Mathematical models Financial Engineering (DE-588)4208404-0 gnd Finanzmathematik (DE-588)4017195-4 gnd
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