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42por Moro, Alessio
Publicado 2010Biblioteca Universidad de Deusto (Otras Fuentes: Biblioteca de la Universidad Pontificia de Salamanca)Libro -
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46por Pino Alea, Jorge A.Materias: “…Compuestos volátiles…”
Publicado 2011
Biblioteca Universitat Ramon Llull (Otras Fuentes: Universidad Loyola - Universidad Loyola Granada, Biblioteca de la Universidad Pontificia de Salamanca)Libro electrónico -
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48Publicado 2007Universidad Loyola - Universidad Loyola Granada (Otras Fuentes: Biblioteca de la Universidad Pontificia de Salamanca)Enlace del recurso
Libro electrónico -
49Publicado 2013Tabla de Contenidos: “…Introduction -- A novel model-free term structure for stock prediction -- An adaptive correlation heston model for stock prediction -- The algorithm to control risk using option -- Option strategies: evaluation criterion and optimization -- A novel mean reversion-based local volatility model -- Regression-based correlation modeling for heston model -- Index option strategies comparison and self-risk management -- Call-put term structure spread-based HSI analysis…”
Universidad Loyola - Universidad Loyola Granada (Otras Fuentes: Biblioteca de la Universidad Pontificia de Salamanca)Enlace del recurso
Libro electrónico -
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56Publicado 1996Tabla de Contenidos: “…Estimating Diffusion Models of Stochastic Volatility…”
Libro electrónico -
57por Huchet-Bourdon, Marilyne“…This report examines the question of whether commodity price volatility has materially changed with the rapid run up in world prices in 2006-09, followed by an equally sharp decline in many commodity prices. …”
Publicado 2011
Capítulo de libro electrónico -
58por Thompson, Wyatt“…This study complements OECD analyses on commodity price volatility by providing quantitative assessments of the impact of two structural changes that a number of market observers have identified as contributing to world wheat market price volatility. …”
Publicado 2012
Capítulo de libro electrónico -
59Publicado 2017Tabla de Contenidos: “…Machine generated contents note: Preface 1 I Introduction to Volatility and Variance 3 1 Derivatives, Volatility and Variance 5 1.1 Option Pricing and Hedging 5 1.2 Notions of Volatility and Variance 7 1.3 Listed Volatility and Variance Derivatives 8 1.3.1 The US History 8 1.3.2 The European History 10 1.3.3 Volatility of Volatility Indexes 11 1.3.4 Products Covered in this Book 12 1.4 Volatility and Variance Trading 12 1.4.1 Volatility Trading 13 1.4.2 Variance Trading 14 1.5 Python as Our Tool of Choice 15 1.6 Quick Guide Through Rest of the Book 15 2 Introduction to Python 19 2.1 Python Basics 19 2.1.1 Data Types 19 2.1.2 Data Structures 21 2.1.3 Control Structures 23 2.1.4 Special Python Idioms 24 2.2 NumPy 27 2.3 matplotlib 32 2.4 pandas 36 2.4.1 pandas Data Frame class 36 2.4.2 Input-Output Operations 40 2.4.3 Financial Analytics Examples 43 2.5 Conclusions 48 3 Model-Free Replication of Variance 49 3.1 Introduction 49 3.2 Spanning with Options 49 3.3 Log Contracts 50 3.4 Static Replication of Realized Variance and Variance Swaps 51 3.5 Constant Dollar Gamma Derivatives and Portfolios 51 3.6 Practical Replication of Realized Variance 52 3.7 VSTOXX as Volatility Index 57 3.8 Conclusions 59 II Listed Volatility Derivatives 61 4 Data Analysis and Strategies 63 4.1 Introduction 63 4.2 Retrieving Base Data 63 4.2.1 EURO STOXX 50 Data 63 4.2.2 VSTOXX Data 65 4.2.3 Combining the Data Sets 67 4.2.4 Saving the Data 68 4.3 Basic Data Analysis 69 4.4 Correlation Analysis 72 4.5 Constant Proportion Investment Strategies 77 4.6 Conclusions 82 5 VSTOXX Index 83 5.1 Introduction 83 5.2 Collecting Option Data 84 5.3 Calculating the Sub-Indexes 91 5.3.1 The Algorithm 91 5.4 Calculating the VSTOXX Index 98 5.5 Conclusions 101 5.6 Python Scripts 103 5.6.1 index_collect_option_data.py 103 5.6.2 index_subindex_calculation.py 107 5.6.3 index_vstoxx_calculation.py 110 6 Valuing Volatility Derivatives 113 6.1 Introduction 113 6.2 The Valuation Framework 113 6.3 The Futures Pricing Formula 114 6.4 The Option Pricing Formula 115 6.5 Monte Carlo Simulation 118 6.6 Automated Monte Carlo Tests 123 6.6.1 The Automated Testing 123 6.6.2 The Storage Functions 126 6.6.3 The Results 128 6.7 Model Calibration 133 6.7.1 The Option Quotes 133 6.7.2 The Calibration Procedure 134 6.7.3 The Calibration Results 138 6.8 Conclusions 141 6.9 Python Scripts 141 6.9.1 srd_functions.py 141 6.9.2 srd_simulation_analysis.py 145 6.9.3 srd_simulation_results.py 148 6.9.4 srd_model_calibration.py 151 7 Advanced Modeling of the VSTOXX Index 155 7.1 Introduction 155 7.2 Market Quotes for Call Options 155 7.3 The SRJD Model 158 7.4 Term Structure Calibration 159 7.4.1 Futures Term Structure 159 7.4.2 Shifted Volatility Process 163 7.5 Option Valuation by Monte Carlo Simulation 164 7.5.1 Monte Carlo Valuation 165 7.5.2 Technical Implementation 165 7.6 Model Calibration 169 7.6.1 The Python Code 169 7.6.2 Short Maturity 171 7.6.3 Two Maturities 173 7.6.4 Four Maturities 175 7.6.5 All Maturities 176 7.7 Conclusions 181 7.8 Python Scripts 181 7.8.1 srjd_fwd_calibration.py 181 7.8.2 srjd_simulation.py 183 7.8.3 srjd_model_calibration.py 185 8 Terms of the VSTOXX and its Derivatives 191 8.1 The EURO STOXX 50 Index 191 8.2 The VSTOXX Index 192 8.3 VSTOXX Futures Contracts 192 8.4 VSTOXX Options Contracts 193 8.5 Conclusions 195 III Listed Variance Derivatives 197 9 Realized Variance and Variance Swaps 199 9.1 Introdution 199 9.2 Realized Variance 199 9.3 Variance Swaps 204 9.3.1 Definition of a Variance Swap 204 9.3.2 Numerical Example 205 9.3.3 Mark-to-Market 208 9.3.4 Vega Sensitivity 209 9.3.5 Variance Swap on the EURO STOXX 50 211 9.4 Variance vs. …”
Libro electrónico -
60por Augen, Jeffrey“….” — A RTHUR T ISI , Founder and CEO, EXA Infosystems; private investor and options trader In The Volatility Edge in Options Trading , leading options trader Jeff Augen introduces breakthrough strategies for identifying subtle price distortions that arise from changes in market volatility. …”
Publicado 2008
Libro electrónico