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Discrete-time approximations and limit theorems : in applications to financial markets / / Yuliya Mishura, Kostiantyn Ralchenko
| Discrete-time approximations and limit theorems : in applications to financial markets / / Yuliya Mishura, Kostiantyn Ralchenko |
| Autore | Mishura I︠U︡lii︠a︡ S. |
| Pubbl/distr/stampa | Berlin, Germany : , : Walter de Gruyter GmbH, , [2022] |
| Descrizione fisica | 1 online resource (XVI, 374 p.) |
| Disciplina | 003 |
| Collana | De Gruyter series in probability and stochastics |
| Soggetto topico |
Discrete-time systems
Finance - Mathematical models |
| ISBN |
3-11-065299-4
3-11-065424-5 |
| Classificazione | SK 980 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Frontmatter -- Introduction -- Contents -- Abbreviations and notations -- 1 Financial markets. From discrete to continuous time -- 2 Rate of convergence of asset and option prices -- 3 Limit theorems for markets with non-random time-varying coefficients -- 4 Convergence of stochastic integrals in application to financial markets -- A Essentials of calculus, probability, and stochastic processes -- Bibliography -- Index |
| Record Nr. | UNINA-9910554262703321 |
Mishura I︠U︡lii︠a︡ S.
|
||
| Berlin, Germany : , : Walter de Gruyter GmbH, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Dynamic copula methods in finance [[electronic resource] /] / Umberto Cherubini ... [et al.]
| Dynamic copula methods in finance [[electronic resource] /] / Umberto Cherubini ... [et al.] |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Hoboken, NJ, : Wiley, 2011 |
| Descrizione fisica | 1 online resource (286 p.) |
| Disciplina | 332.01/519233 |
| Altri autori (Persone) | CherubiniUmberto |
| Collana | The wiley finance series |
| Soggetto topico |
Finance - Mathematical models
Mathematics Finances Models matemàtics |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
1-118-46740-X
1-283-29530-X 9786613295309 1-119-95451-7 |
| Classificazione | BUS027000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Dynamic Copula Methods in Finance; Contents; Preface; 1 Correlation Risk in Finance; 1.1 Correlation Risk in Pricing and Risk Management; 1.2 Implied vs Realized Correlation; 1.3 Bottom-up vs Top-down Models; 1.4 Copula Functions; 1.5 Spatial and Temporal Dependence; 1.6 Long-range Dependence; 1.7 Multivariate GARCH Models; 1.8 Copulas and Convolution; 2 Copula Functions: The State of the Art; 2.1 Copula Functions: The Basic Recipe; 2.2 Market Co-movements; 2.3 Delta Hedging Multivariate Digital Products; 2.4 Linear Correlation; 2.5 Rank Correlation; 2.6 Multivariate Spearman's Rho
2.7 Survival Copulas and Radial Symmetry2.8 Copula Volume and Survival Copulas; 2.9 Tail Dependence; 2.10 Long/Short Correlation; 2.11 Families of Copulas; 2.11.1 Elliptical Copulas; 2.11.2 Archimedean Copulas; 2.12 Kendall Function; 2.13 Exchangeability; 2.14 Hierarchical Copulas; 2.15 Conditional Probability and Factor Copulas; 2.16 Copula Density and Vine Copulas; 2.17 Dynamic Copulas; 2.17.1 Conditional Copulas; 2.17.2 Pseudo-copulas; 3 Copula Functions and Asset Price Dynamics; 3.1 The Dynamics of Speculative Prices; 3.2 Copulas and Markov Processes: The DNO approach 3.2.1 The * and Product Operators3.2.2 Product Operators and Markov Processes; 3.2.3 Self-similar Copulas; 3.2.4 Simulating Markov Chains with Copulas; 3.3 Time-changed Brownian Copulas; 3.3.1 CEV Clock Brownian Copulas; 3.3.2 VG Clock Brownian Copulas; 3.4 Copulas and Martingale Processes; 3.4.1 C-Convolution; 3.4.2 Markov Processes with Independent Increments; 3.4.3 Markov Processes with Dependent Increments; 3.4.4 Extracting Dependent Increments in Markov Processes; 3.4.5 Martingale Processes; 3.5 Multivariate Processes; 3.5.1 Multivariate Markov Processes 3.5.2 Granger Causality and the Martingale Condition4 Copula-based Econometrics of Dynamic Processes; 4.1 Dynamic Copula Quantile Regressions; 4.2 Copula-based Markov Processes: Non-linear Quantile Autoregression; 4.3 Copula-based Markov Processes: Semi-parametric Estimation; 4.4 Copula-based Markov Processes: Non-parametric Estimation; 4.5 Copula-based Markov Processes: Mixing Properties; 4.6 Persistence and Long Memory; 4.7 C-convolution-based Markov Processes: The Likelihood Function; 5 Multivariate Equity Products; 5.1 Multivariate Equity Products 5.1.1 European Multivariate Equity Derivatives5.1.2 Path-dependent Equity Derivatives; 5.2 Recursions of Running Maxima and Minima; 5.3 The Memory Feature; 5.4 Risk-neutral Pricing Restrictions; 5.5 Time-changed Brownian Copulas; 5.6 Variance Swaps; 5.7 Semi-parametric Pricing of Path-dependent Derivatives; 5.8 The Multivariate Pricing Setting; 5.9 H-Condition and Granger Causality; 5.10 Multivariate Pricing Recursion; 5.11 Hedging Multivariate Equity Derivatives; 5.12 Correlation Swaps; 5.13 The Term Structure of Multivariate Equity Derivatives; 5.13.1 Altiplanos; 5.13.2 Everest 5.13.3 Spread Options |
| Record Nr. | UNINA-9910139577603321 |
| Hoboken, NJ, : Wiley, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Dynamic copula methods in finance / / Umberto Cherubini ... [et al.]
| Dynamic copula methods in finance / / Umberto Cherubini ... [et al.] |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Hoboken, NJ, : Wiley, 2011 |
| Descrizione fisica | 1 online resource (286 p.) |
| Disciplina | 332.01/519233 |
| Altri autori (Persone) | CherubiniUmberto |
| Collana | The wiley finance series |
| Soggetto topico |
Finance - Mathematical models
Mathematics Finances Models matemàtics |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
1-118-46740-X
1-283-29530-X 9786613295309 1-119-95451-7 |
| Classificazione | BUS027000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Dynamic Copula Methods in Finance; Contents; Preface; 1 Correlation Risk in Finance; 1.1 Correlation Risk in Pricing and Risk Management; 1.2 Implied vs Realized Correlation; 1.3 Bottom-up vs Top-down Models; 1.4 Copula Functions; 1.5 Spatial and Temporal Dependence; 1.6 Long-range Dependence; 1.7 Multivariate GARCH Models; 1.8 Copulas and Convolution; 2 Copula Functions: The State of the Art; 2.1 Copula Functions: The Basic Recipe; 2.2 Market Co-movements; 2.3 Delta Hedging Multivariate Digital Products; 2.4 Linear Correlation; 2.5 Rank Correlation; 2.6 Multivariate Spearman's Rho
2.7 Survival Copulas and Radial Symmetry2.8 Copula Volume and Survival Copulas; 2.9 Tail Dependence; 2.10 Long/Short Correlation; 2.11 Families of Copulas; 2.11.1 Elliptical Copulas; 2.11.2 Archimedean Copulas; 2.12 Kendall Function; 2.13 Exchangeability; 2.14 Hierarchical Copulas; 2.15 Conditional Probability and Factor Copulas; 2.16 Copula Density and Vine Copulas; 2.17 Dynamic Copulas; 2.17.1 Conditional Copulas; 2.17.2 Pseudo-copulas; 3 Copula Functions and Asset Price Dynamics; 3.1 The Dynamics of Speculative Prices; 3.2 Copulas and Markov Processes: The DNO approach 3.2.1 The * and Product Operators3.2.2 Product Operators and Markov Processes; 3.2.3 Self-similar Copulas; 3.2.4 Simulating Markov Chains with Copulas; 3.3 Time-changed Brownian Copulas; 3.3.1 CEV Clock Brownian Copulas; 3.3.2 VG Clock Brownian Copulas; 3.4 Copulas and Martingale Processes; 3.4.1 C-Convolution; 3.4.2 Markov Processes with Independent Increments; 3.4.3 Markov Processes with Dependent Increments; 3.4.4 Extracting Dependent Increments in Markov Processes; 3.4.5 Martingale Processes; 3.5 Multivariate Processes; 3.5.1 Multivariate Markov Processes 3.5.2 Granger Causality and the Martingale Condition4 Copula-based Econometrics of Dynamic Processes; 4.1 Dynamic Copula Quantile Regressions; 4.2 Copula-based Markov Processes: Non-linear Quantile Autoregression; 4.3 Copula-based Markov Processes: Semi-parametric Estimation; 4.4 Copula-based Markov Processes: Non-parametric Estimation; 4.5 Copula-based Markov Processes: Mixing Properties; 4.6 Persistence and Long Memory; 4.7 C-convolution-based Markov Processes: The Likelihood Function; 5 Multivariate Equity Products; 5.1 Multivariate Equity Products 5.1.1 European Multivariate Equity Derivatives5.1.2 Path-dependent Equity Derivatives; 5.2 Recursions of Running Maxima and Minima; 5.3 The Memory Feature; 5.4 Risk-neutral Pricing Restrictions; 5.5 Time-changed Brownian Copulas; 5.6 Variance Swaps; 5.7 Semi-parametric Pricing of Path-dependent Derivatives; 5.8 The Multivariate Pricing Setting; 5.9 H-Condition and Granger Causality; 5.10 Multivariate Pricing Recursion; 5.11 Hedging Multivariate Equity Derivatives; 5.12 Correlation Swaps; 5.13 The Term Structure of Multivariate Equity Derivatives; 5.13.1 Altiplanos; 5.13.2 Everest 5.13.3 Spread Options |
| Record Nr. | UNINA-9910811657703321 |
| Hoboken, NJ, : Wiley, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Dynamic models for volatility and heavy tails : with applications to financial and economic time series / / Andrew C. Harvey [[electronic resource]]
| Dynamic models for volatility and heavy tails : with applications to financial and economic time series / / Andrew C. Harvey [[electronic resource]] |
| Autore | Harvey A. C (Andrew C.) |
| Pubbl/distr/stampa | Cambridge : , : Cambridge University Press, , 2013 |
| Descrizione fisica | 1 online resource (xviii, 261 pages) : digital, PDF file(s) |
| Disciplina | 330.01/5195 |
| Collana | Econometric Society monographs |
| Soggetto topico |
Econometrics
Finance - Mathematical models Time-series analysis |
| ISBN |
1-107-32712-1
1-107-33688-0 1-107-33356-3 1-107-33522-1 1-139-54093-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Contents; Preface; Acronyms and Abbreviations; 1 Introduction; 1.1 Unobserved Components and Filters; 1.2 Independence, White Noise and Martingale Differences; 1.2.1 The Law of Iterated Expectations and Optimal Predictions; 1.2.2 Definitions and Properties; 1.3 Volatility; 1.3.1 Stochastic Volatility; 1.3.2 Generalized Autoregressive Conditional Heteroscedasticity; 1.3.3 Exponential GARCH; 1.3.4 Variance, Scale and Outliers; 1.3.5 Location/Scale Models; 1.4 Dynamic Conditional Score Models; 1.5 Distributions and Quantiles; 1.6 Plan of Book; 2 Statistical Distributions and Asymptotic Theory
2.1 Distributions2.1.1 Student's t Distribution; 2.1.2 General Error Distribution; 2.1.3 Beta Distribution; 2.1.4 Gamma Distribution; 2.2 Maximum Likelihood; 2.2.1 Student's t Distribution; 2.2.2 General Error Distribution; 2.2.3 Gamma Distribution; 2.2.4 Consistency and Asymptotic Normality*; 2.3 Maximum Likelihood Estimation; 2.3.1 An Information Matrix Lemma; 2.3.2 Information Matrix for the First-Order Model; 2.3.3 Information Matrix with the 0=x""010E Parameterization*; 2.3.4 Asymptotic Distribution; 2.3.5 Consistency and Asymptotic Normality*; 2.3.6 Nonstationarity 2.3.7 Several Parameters2.4 Higher Order Models; 2.5 Tests; 2.5.1 Serial Correlation; 2.5.2 Goodness of Fit of Distributions; 2.5.3 Residuals; 2.5.4 Model Fit; 2.6 Explanatory Variables; 3 Location; 3.1 Dynamic Student's t Location Model; 3.2 Basic Properties; 3.2.1 Generalization and Reduced Form; 3.2.2 Moments of the Observations; 3.2.3 Autocorrelation Function; 3.3 Maximum Likelihood Estimation; 3.3.1 Asymptotic Distribution of the Maximum Likelihood Estimator; 3.3.2 Monte Carlo Experiments; 3.3.3 Application to U.S. GDP; 3.4 Parameter Restrictions* 3.5 Higher Order Models and the State Space Form*3.5.1 Linear Gaussian Models and the Kalman Filter; 3.5.2 The DCS Model; 3.5.3 QARMA Models; 3.6 Trend and Seasonality; 3.6.1 Local Level Model; 3.6.2 Application to Weekly Hours of Employees in U.S. Manufacturing; 3.6.3 Local Linear Trend; 3.6.4 Stochastic Seasonal; 3.6.5 Application to Rail Travel; 3.6.6 QARIMA and Seasonal QARIMA Models*; 3.7 Smoothing; 3.7.1 Weights; 3.7.2 Smoothing Recursions for Linear State Space Models; 3.7.3 Smoothing Recursions for DCS Models; 3.7.4 Conditional Mode Estimation and the Score; 3.8 Forecasting 3.8.1 QARMA Models3.8.2 State Space Form*; 3.9 Components and Long Memory; 3.10 General Error Distribution; 3.11 Skew Distributions; 3.11.1 How to Skew a Distribution; 3.11.2 Dynamic Skew-t Location Model; 4 Scale; 4.1 Beta-tttt-EGARCH; 4.2 Properties of Stationary Beta-tttt-EGARCH Models; 4.2.1 Exponential GARCH; 4.2.2 Moments; 4.2.3 Autocorrelation Functions of Squares and Powersof Absolute Values; 4.2.4 Autocorrelations and Kurtosis; 4.3 Leverage Effects; 4.4 Gamma-GED-EGARCH; 4.5 Forecasting; 4.5.1 Beta-t-EGARCH; 4.5.2 Gamma-GED-EGARCH; 4.5.3 Integrated Exponential Models 4.5.4 Predictive Distribution |
| Altri titoli varianti | Dynamic Models for Volatility & Heavy Tails |
| Record Nr. | UNINA-9910462581603321 |
|
Harvey A. C (Andrew C.)
|
||
| Cambridge : , : Cambridge University Press, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Dynamic models for volatility and heavy tails : with applications to financial and economic time series / / Andrew C. Harvey [[electronic resource]]
| Dynamic models for volatility and heavy tails : with applications to financial and economic time series / / Andrew C. Harvey [[electronic resource]] |
| Autore | Harvey A. C (Andrew C.) |
| Pubbl/distr/stampa | Cambridge : , : Cambridge University Press, , 2013 |
| Descrizione fisica | 1 online resource (xviii, 261 pages) : digital, PDF file(s) |
| Disciplina | 330.01/5195 |
| Collana | Econometric Society monographs |
| Soggetto topico |
Econometrics
Finance - Mathematical models Time-series analysis |
| ISBN |
1-107-32712-1
1-107-33688-0 1-107-33356-3 1-107-33522-1 1-139-54093-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Contents; Preface; Acronyms and Abbreviations; 1 Introduction; 1.1 Unobserved Components and Filters; 1.2 Independence, White Noise and Martingale Differences; 1.2.1 The Law of Iterated Expectations and Optimal Predictions; 1.2.2 Definitions and Properties; 1.3 Volatility; 1.3.1 Stochastic Volatility; 1.3.2 Generalized Autoregressive Conditional Heteroscedasticity; 1.3.3 Exponential GARCH; 1.3.4 Variance, Scale and Outliers; 1.3.5 Location/Scale Models; 1.4 Dynamic Conditional Score Models; 1.5 Distributions and Quantiles; 1.6 Plan of Book; 2 Statistical Distributions and Asymptotic Theory
2.1 Distributions2.1.1 Student's t Distribution; 2.1.2 General Error Distribution; 2.1.3 Beta Distribution; 2.1.4 Gamma Distribution; 2.2 Maximum Likelihood; 2.2.1 Student's t Distribution; 2.2.2 General Error Distribution; 2.2.3 Gamma Distribution; 2.2.4 Consistency and Asymptotic Normality*; 2.3 Maximum Likelihood Estimation; 2.3.1 An Information Matrix Lemma; 2.3.2 Information Matrix for the First-Order Model; 2.3.3 Information Matrix with the 0=x""010E Parameterization*; 2.3.4 Asymptotic Distribution; 2.3.5 Consistency and Asymptotic Normality*; 2.3.6 Nonstationarity 2.3.7 Several Parameters2.4 Higher Order Models; 2.5 Tests; 2.5.1 Serial Correlation; 2.5.2 Goodness of Fit of Distributions; 2.5.3 Residuals; 2.5.4 Model Fit; 2.6 Explanatory Variables; 3 Location; 3.1 Dynamic Student's t Location Model; 3.2 Basic Properties; 3.2.1 Generalization and Reduced Form; 3.2.2 Moments of the Observations; 3.2.3 Autocorrelation Function; 3.3 Maximum Likelihood Estimation; 3.3.1 Asymptotic Distribution of the Maximum Likelihood Estimator; 3.3.2 Monte Carlo Experiments; 3.3.3 Application to U.S. GDP; 3.4 Parameter Restrictions* 3.5 Higher Order Models and the State Space Form*3.5.1 Linear Gaussian Models and the Kalman Filter; 3.5.2 The DCS Model; 3.5.3 QARMA Models; 3.6 Trend and Seasonality; 3.6.1 Local Level Model; 3.6.2 Application to Weekly Hours of Employees in U.S. Manufacturing; 3.6.3 Local Linear Trend; 3.6.4 Stochastic Seasonal; 3.6.5 Application to Rail Travel; 3.6.6 QARIMA and Seasonal QARIMA Models*; 3.7 Smoothing; 3.7.1 Weights; 3.7.2 Smoothing Recursions for Linear State Space Models; 3.7.3 Smoothing Recursions for DCS Models; 3.7.4 Conditional Mode Estimation and the Score; 3.8 Forecasting 3.8.1 QARMA Models3.8.2 State Space Form*; 3.9 Components and Long Memory; 3.10 General Error Distribution; 3.11 Skew Distributions; 3.11.1 How to Skew a Distribution; 3.11.2 Dynamic Skew-t Location Model; 4 Scale; 4.1 Beta-tttt-EGARCH; 4.2 Properties of Stationary Beta-tttt-EGARCH Models; 4.2.1 Exponential GARCH; 4.2.2 Moments; 4.2.3 Autocorrelation Functions of Squares and Powersof Absolute Values; 4.2.4 Autocorrelations and Kurtosis; 4.3 Leverage Effects; 4.4 Gamma-GED-EGARCH; 4.5 Forecasting; 4.5.1 Beta-t-EGARCH; 4.5.2 Gamma-GED-EGARCH; 4.5.3 Integrated Exponential Models 4.5.4 Predictive Distribution |
| Altri titoli varianti | Dynamic Models for Volatility & Heavy Tails |
| Record Nr. | UNINA-9910786998703321 |
|
Harvey A. C (Andrew C.)
|
||
| Cambridge : , : Cambridge University Press, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Dynamic models for volatility and heavy tails : with applications to financial and economic time series / / Andrew C. Harvey [[electronic resource]]
| Dynamic models for volatility and heavy tails : with applications to financial and economic time series / / Andrew C. Harvey [[electronic resource]] |
| Autore | Harvey A. C (Andrew C.) |
| Pubbl/distr/stampa | Cambridge : , : Cambridge University Press, , 2013 |
| Descrizione fisica | 1 online resource (xviii, 261 pages) : digital, PDF file(s) |
| Disciplina | 330.01/5195 |
| Collana | Econometric Society monographs |
| Soggetto topico |
Econometrics
Finance - Mathematical models Time-series analysis |
| ISBN |
1-107-32712-1
1-107-33688-0 1-107-33356-3 1-107-33522-1 1-139-54093-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Contents; Preface; Acronyms and Abbreviations; 1 Introduction; 1.1 Unobserved Components and Filters; 1.2 Independence, White Noise and Martingale Differences; 1.2.1 The Law of Iterated Expectations and Optimal Predictions; 1.2.2 Definitions and Properties; 1.3 Volatility; 1.3.1 Stochastic Volatility; 1.3.2 Generalized Autoregressive Conditional Heteroscedasticity; 1.3.3 Exponential GARCH; 1.3.4 Variance, Scale and Outliers; 1.3.5 Location/Scale Models; 1.4 Dynamic Conditional Score Models; 1.5 Distributions and Quantiles; 1.6 Plan of Book; 2 Statistical Distributions and Asymptotic Theory
2.1 Distributions2.1.1 Student's t Distribution; 2.1.2 General Error Distribution; 2.1.3 Beta Distribution; 2.1.4 Gamma Distribution; 2.2 Maximum Likelihood; 2.2.1 Student's t Distribution; 2.2.2 General Error Distribution; 2.2.3 Gamma Distribution; 2.2.4 Consistency and Asymptotic Normality*; 2.3 Maximum Likelihood Estimation; 2.3.1 An Information Matrix Lemma; 2.3.2 Information Matrix for the First-Order Model; 2.3.3 Information Matrix with the 0=x""010E Parameterization*; 2.3.4 Asymptotic Distribution; 2.3.5 Consistency and Asymptotic Normality*; 2.3.6 Nonstationarity 2.3.7 Several Parameters2.4 Higher Order Models; 2.5 Tests; 2.5.1 Serial Correlation; 2.5.2 Goodness of Fit of Distributions; 2.5.3 Residuals; 2.5.4 Model Fit; 2.6 Explanatory Variables; 3 Location; 3.1 Dynamic Student's t Location Model; 3.2 Basic Properties; 3.2.1 Generalization and Reduced Form; 3.2.2 Moments of the Observations; 3.2.3 Autocorrelation Function; 3.3 Maximum Likelihood Estimation; 3.3.1 Asymptotic Distribution of the Maximum Likelihood Estimator; 3.3.2 Monte Carlo Experiments; 3.3.3 Application to U.S. GDP; 3.4 Parameter Restrictions* 3.5 Higher Order Models and the State Space Form*3.5.1 Linear Gaussian Models and the Kalman Filter; 3.5.2 The DCS Model; 3.5.3 QARMA Models; 3.6 Trend and Seasonality; 3.6.1 Local Level Model; 3.6.2 Application to Weekly Hours of Employees in U.S. Manufacturing; 3.6.3 Local Linear Trend; 3.6.4 Stochastic Seasonal; 3.6.5 Application to Rail Travel; 3.6.6 QARIMA and Seasonal QARIMA Models*; 3.7 Smoothing; 3.7.1 Weights; 3.7.2 Smoothing Recursions for Linear State Space Models; 3.7.3 Smoothing Recursions for DCS Models; 3.7.4 Conditional Mode Estimation and the Score; 3.8 Forecasting 3.8.1 QARMA Models3.8.2 State Space Form*; 3.9 Components and Long Memory; 3.10 General Error Distribution; 3.11 Skew Distributions; 3.11.1 How to Skew a Distribution; 3.11.2 Dynamic Skew-t Location Model; 4 Scale; 4.1 Beta-tttt-EGARCH; 4.2 Properties of Stationary Beta-tttt-EGARCH Models; 4.2.1 Exponential GARCH; 4.2.2 Moments; 4.2.3 Autocorrelation Functions of Squares and Powersof Absolute Values; 4.2.4 Autocorrelations and Kurtosis; 4.3 Leverage Effects; 4.4 Gamma-GED-EGARCH; 4.5 Forecasting; 4.5.1 Beta-t-EGARCH; 4.5.2 Gamma-GED-EGARCH; 4.5.3 Integrated Exponential Models 4.5.4 Predictive Distribution |
| Altri titoli varianti | Dynamic Models for Volatility & Heavy Tails |
| Record Nr. | UNINA-9910811412303321 |
|
Harvey A. C (Andrew C.)
|
||
| Cambridge : , : Cambridge University Press, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Econophysics and financial economics : an emerging dialogue / / Franck Jovanovic, Christophe Schinckus
| Econophysics and financial economics : an emerging dialogue / / Franck Jovanovic, Christophe Schinckus |
| Autore | Jovanovic Franck |
| Pubbl/distr/stampa | New York, NY : , : Oxford University Press, , 2016 |
| Descrizione fisica | 1 online resource |
| Disciplina | 330.015195 |
| Soggetto topico |
Finance - Mathematical models
Economics - Mathematical models Statistical physics |
| ISBN |
0-19-020505-9
0-19-020506-7 0-19-020504-0 |
| Classificazione | BUS069030SCI055000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910154719703321 |
|
Jovanovic Franck
|
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| New York, NY : , : Oxford University Press, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Extreme events in finance : a handbook of extreme value theory and its applications / / edited by Francois Longin
| Extreme events in finance : a handbook of extreme value theory and its applications / / edited by Francois Longin |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2017 |
| Descrizione fisica | 1 online resource (638 p.) |
| Disciplina | 332.015195 |
| Collana |
Wiley Handbooks in Financial Engineering and Econometrics
THEi Wiley ebooks |
| Soggetto topico |
Finance - Mathematical models
Extreme value theory - Mathematical models |
| ISBN |
1-118-65020-4
1-118-65033-6 1-118-65031-X |
| Classificazione | BUS033070 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright; Contents; About the Editor; About the Contributors; Chapter 1 Introduction; 1.1 Extremes; 1.2 History; 1.3 Extreme value theory; 1.4 Statistical estimation of extremes; 1.5 Applications in finance; 1.6 Practitioners' points of view; 1.7 A broader view on modeling extremes; 1.8 Final words; 1.9 Thank you note; References; Chapter 2 Extremes Under Dependence-Historical Development and Parallels with Central Limit Theory; 2.1 Introduction; 2.2 Classical (I.I.D.) central limit and extreme value theories; 2.3 Exceedances of levels, kth largest values
2.4 CLT and EVT for stationary sequences, bernstein's blocks and strong mixing2.5 Weak distributional mixing for EVT, D(un), extremal index; 2.6 Point process of level exceedances; 2.7 Continuous parameter extremes; References; Chapter 3 The Extreme Value Problem in Finance: Comparing the Pragmatic Program with the Mandelbrot Program; 3.1 The extreme value puzzle in financial modeling; 3.2 The sato classification and the two programs; 3.3 Mandelbrot's program: A fractal approach; 3.4 The Pragmatic Program: A data-driven approach; 3.5 Conclusion; Acknowledgments; References Chapter 4 Extreme Value Theory: An Introductory Overview4.1 Introduction; 4.2 Univariate case; 4.3 Multivariate case: Some highlights; Further reading; Acknowledgments; References; Chapter 5 Estimation of the Extreme Value Index; 5.1 Introduction; 5.2 The main limit theorem behind extreme value theory; 5.3 Characterizations of the max-domains of attraction and extreme value index estimators; 5.4 Consistency and asymptotic normality of the estimators; 5.5 Second-order reduced-bias estimation; 5.6 Case study; 5.7 Other topics and comments; References Chapter 6 Bootstrap Methods in Statistics of Extremes6.1 Introduction; 6.2 A few details on EVT; 6.3 The bootstrap methodology in statistics of univariate extremes; 6.4 Applications to simulated data; 6.5 Concluding remarks; Acknowledgments; References; Chapter 7 Extreme Values Statistics for Markov Chains with Applications to Finance and Insurance; 7.1 Introduction; 7.2 On the (pseudo) regenerative approach for markovian data; 7.3 Preliminary results; 7.4 Regeneration-based statistical methods for extremal events; 7.5 The extremal index; 7.6 The regeneration-based hill estimator 7.7 Applications to ruin theory and financial time series7.8 An application to the CAC40; 7.9 Conclusion; References; Chapter 8 Lévy Processes and Extreme Value Theory; 8.1 Introduction; 8.2 Extreme value theory; 8.3 Infinite divisibility and Lévy processes; 8.4 Heavy-tailed Lévy processes; 8.5 Semi-heavy-tailed Lévy processes; 8.6 Lévy processes and extreme values; 8.7 Conclusion; References; Chapter 9 Statistics of Extremes: Challenges and Opportunities; 9.1 Introduction; 9.2 Statistics of bivariate extremes; 9.3 Models based on families of tilted measures; 9.4 Miscellanea; References Chapter 10 Measures of Financial Risk |
| Record Nr. | UNINA-9910166634503321 |
| Hoboken, New Jersey : , : Wiley, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Extreme value methods with applications to finance / / Serguei Y. Novak
| Extreme value methods with applications to finance / / Serguei Y. Novak |
| Autore | Novak Serguei Y. |
| Pubbl/distr/stampa | Boca Raton, Fla. : , : CRC Press, , 2012 |
| Descrizione fisica | 1 online resource (397 p.) |
| Disciplina | 332.01/5195 |
| Collana | Monographs on statistics and applied probability |
| Soggetto topico |
Finance - Mathematical models
Financial risk - Mathematical models Extreme value theory - Mathematical models |
| Soggetto genere / forma | Electronic books. |
| ISBN |
0-429-09383-7
1-280-12191-2 9786613525772 1-4398-3575-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Front Cover; Detication; Contents; Preface; Introduction; List of Conventions; List of Abbreviations; Author; Part I: Distribution of Extremes; 1. Methods of Extreme Value Theory; 2. Maximum of Partial Sums; 3. Extremes in Samples of Random Size; 4. Poisson Approximation; 5. Compound Poisson Approximation; 6. Exceedances of Several Levels; 7. Processes of Exceedances; 8. Beyond Compound Poisson; Part II: Statistics of Extremes; 9. Inference on Heavy Tails; 10. Value-at-Risk; 11. Extremal Index; 12. Normal Approximation; 13. Lower Bounds; 14. Appendix; References |
| Record Nr. | UNINA-9910457269803321 |
|
Novak Serguei Y.
|
||
| Boca Raton, Fla. : , : CRC Press, , 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Extreme value methods with applications to finance / / Serguei Y. Novak
| Extreme value methods with applications to finance / / Serguei Y. Novak |
| Autore | Novak Serguei Y. |
| Pubbl/distr/stampa | Boca Raton, Fla. : , : CRC Press, , 2012 |
| Descrizione fisica | 1 online resource (397 p.) |
| Disciplina | 332.01/5195 |
| Collana | Monographs on statistics and applied probability |
| Soggetto topico |
Finance - Mathematical models
Financial risk - Mathematical models Extreme value theory - Mathematical models |
| ISBN |
0-429-09383-7
1-280-12191-2 9786613525772 1-4398-3575-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Front Cover; Detication; Contents; Preface; Introduction; List of Conventions; List of Abbreviations; Author; Part I: Distribution of Extremes; 1. Methods of Extreme Value Theory; 2. Maximum of Partial Sums; 3. Extremes in Samples of Random Size; 4. Poisson Approximation; 5. Compound Poisson Approximation; 6. Exceedances of Several Levels; 7. Processes of Exceedances; 8. Beyond Compound Poisson; Part II: Statistics of Extremes; 9. Inference on Heavy Tails; 10. Value-at-Risk; 11. Extremal Index; 12. Normal Approximation; 13. Lower Bounds; 14. Appendix; References |
| Record Nr. | UNINA-9910778817703321 |
|
Novak Serguei Y.
|
||
| Boca Raton, Fla. : , : CRC Press, , 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
