1  20
Next
 Lee, Cheng F.
 Dordrecht : Springer, 2012.
 Description
 Book — 1 online resource (1237 pages)
 Summary

 Introduction and Descriptive Statistics. Probability and Important Distributions. Statistical Inferences Based on Samples. Regression and Correlation: Relating Two or More Variables. Selected Topics in Statistical Analysis for Business and Economics. Appendices. Index.
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 Ursone, Pierino, 1966 author.
 Chichester, West Sussex, United Kingdom : John Wiley & Sons, 2015.
 Description
 Book — 1 online resource (1 volume) : illustrations.
 Summary

 Preface ix
 Chapter 1 Introduction 1
 Chapter 2 The Normal Probability Distribution 7 Standard deviation in a financial market 8 The impact of volatility and time on the standard deviation 8
 Chapter 3 Volatility 11 The probability distribution of the value of a Future after one year of trading 11 Normal distribution versus lognormal distribution 11 Calculating the annualised volatility traditionally 15 Calculating the annualised volatility without 17 Calculating the annualised volatility applying the 16% rule 19 Variation in trading days 20 Approach towards intraday volatility 20 Historical versus implied volatility 23
 Chapter 4 Put Call Parity 25 Synthetically creating a Future long position, the reversal 29 Synthetically creating a Future short position, the conversion 30 Synthetic options 31 Covered call writing 34 Short note on interest rates 35
 Chapter 5 Delta 37 Change of option value through the delta 38 Dynamic delta 40 Delta at different maturities 41 Delta at different volatilities 44 20 80 Delta region 46 Delta per strike 46 Dynamic delta hedging 47 The at the money delta 50 Delta changes in time 53
 Chapter 6 Pricing 55 Calculating the at the money straddle using Black and Scholes formula 57 Determining the value of an at the money straddle 59
 Chapter 7 Delta II 61 Determining the boundaries of the delta 61 Valuation of the at the money delta 64 Delta distribution in relation to the at the money straddle 65 Application of the delta approach, determining the delta of a call spread 68
 Chapter 8 Gamma 71 The aggregate gamma for a portfolio of options 73 The delta change of an option 75 The gamma is not a constant 76 Long term gamma example 77 Short term gamma example 77 Very short term gamma example 78 Determining the boundaries of gamma 79 Determining the gamma value of an at the money straddle 80 Gamma in relation to time to maturity, volatility and the underlying level 82 Practical example 85 Hedging the gamma 87 Determining the gamma of out of the money options 89 Derivatives of the gamma 91
 Chapter 9 Vega 93 Different maturities will display different volatility regime changes 95 Determining the vega value of at the money options 96 Vega of at the money options compared to volatility 97 Vega of at the money options compared to time to maturity 99 Vega of at the money options compared to the underlying level 99 Vega on a 3dimensional scale, vega vs maturity and vega vs volatility 101 Determining the boundaries of vega 102 Comparing the boundaries of vega with the boundaries of gamma 104 Determining vega values of out of the money options 105 Derivatives of the vega 108 Vomma 108
 Chapter 10 Theta 111 A practical example 112 Theta in relation to volatility 114 Theta in relation to time to maturity 115 Theta of at the money options in relation to the underlying level 117 Determining the boundaries of theta 118 The gamma theta relationship 120 Theta on a 3dimensional scale, theta vs maturity and theta vs volatility 125 Determining the theta value of an at the money straddle 126 Determining theta values of out of the money options 127
 Chapter 11 Skew 129 Volatility smiles with different times to maturity 131 Sticky at the money volatility 133
 Chapter 12 Spreads 135 Call spread (horizontal) 135 Put spread (horizontal) 137 Boxes 138 Applying boxes in the real market 139 The Greeks for horizontal spreads 140 Time spread 146 Approximation of the value of at the money spreads 148 Ratio spread 149
 Chapter 13 Butterfly 155 Put call parity 158 Distribution of the butterfly 159 Boundaries of the butterfly 161 Method for estimating at the money butterfly values 163 Estimating out of the money butterfly values 164 Butterfly in relation to volatility 165 Butterfly in relation to time to maturity 166 Butterfly as a strategic play 166 The Greeks of a butterfly 167 Straddle strangle or the Iron fly 171
 Chapter 14 Strategies 173 Call 173 Put 174 Call spread 175 Ratio spread 176 Straddle 177 Strangle 178 Collar (risk reversal, fence) 178 Gamma portfolio 179 Gamma hedging strategies based on Monte Carlo scenarios 180 Setting up a gamma position on the back of prevailing kurtosis in the market 190 Excess kurtosis 191 Benefitting from a platykurtic environment 192 The mesokurtic market 193 The leptokurtic market 193 Transition from a platykurtic environment towards a leptokurtic environment 194 Wrong hedging strategy: Killergamma 195 Vega convexity/Vomma 196 Vega convexity in relation to time/Veta 202 Index 205.
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3. Statistics for finance [2015]
 Lindström, Erik, author.
 Boca Raton, FL : CRC Press, [2015]
 Description
 Book — 1 online resource (1 volume) : illustrations
 Summary

 Introduction Introduction to financial derivatives Financial derivativeswhat's the big deal? Stylized facts Overview
 Fundamentals Interest rates Cash flows Continuously compounded interest rates Interest rate options: caps and floors
 DiscreteTime Finance The binomial one period model The one period model The multi period model
 Linear Time Series Models Introduction Linear systems in the time domain Linear stochastic processes Linear processes with a rational transfer function Autocovariance functions Prediction in linear processes
 NonLinear Time Series Models Introduction The aim of model building Qualitative properties of the models Parameter estimation Parametric models Model identification Prediction in nonlinear models Applications of nonlinear models
 Kernel Estimators in Time Series Analysis Nonparametric estimation Kernel estimators for time series Kernel estimation for regression Applications of kernel estimators
 Stochastic Calculus Dynamical systems The Wiener process Stochastic Integrals Ito stochastic calculus Extensions to jump processes
 Stochastic Differential Equations Stochastic differential equations Analytical solution methods FeynmanKac representation Girsanov measure transformation
 ContinuousTime Security Markets From discrete to continuous time Classical arbitrage theory Modern approach using martingale measures Pricing Model extensions Computational methods
 Stochastic Interest Rate Models Gaussian onefactor models A general class of onefactor models Timedependent models Multifactor and stochastic volatility models
 The Term Structure of Interest Rates Basic concepts The classical approach The term structure for specific models HeathJarrowMorton framework Credit models Estimation of the term structurecurvefitting
 DiscreteTime Approximations Stochastic Taylor expansion Convergence Discretization schemes Multilevel Monte Carlo Simulation of SDEs
 Parameter Estimation in Discretely Observed SDEs Introduction High frequency methods Approximate methods for linear and nonlinear models State dependent diffusion term MLE for nonlinear diffusions Generalized method of moments (GMM) Model validation for discretely observed SDEs
 Inference in Partially Observed Processes Introduction The model Exact filtering Conditional moment estimators Kalman filter Approximate filters State filtering and prediction The unscented Kalman filter A maximum likelihood method Sequential Monte Carlo filters Application of nonlinear filters
 Appendix A: Projections in Hilbert Spaces Appendix B: Probability Theory
 Bibliography
 Problems appear at the end of each chapter.
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 First edition.  Amsterdam ; Boston : Elsevier, [2003]
 Description
 Book — 1 online resource (1 volume) : illustrations
 Summary

 Heavy tails in finance for independent or multifractal price increments / Benoit B. Mandelbrot
 Financial risk and heavy tails / Brendan O. Bradley and Murad S. Taqqu
 Modeling financial data with stable distributions / John P. Nolan
 Statistical issues in modeling multivariate stable portfolios / Tomasz J. Kozubowski, Anna K. Panorska and Svetlozar T. Rachev
 Jumpdiffusion models / Wolfgang J. Runggaldier
 Hyperbolic processes in finance / Bo Martin Bibby and Michael Sørensen
 Stable modeling of market and credit value at risk / Svetlozar T. Rachev, Eduardo S. Schwartz and Irina Khindanova
 Modelling dependence with copulas and applications to risk management / Paul Embrechts, Filip Lindskog and Alexander McNeil
 Prediction of financial downsiderisk with heavytailed conditional distributions / Stefan Mittnik and Marc S. Paolella
 Stable nonGaussian models for credit risk management / Bernhard Martin, Svetlozar T. Rachev and Eduardo S. Schwartz
 Multifactor stochastic variance models in risk management : maximum entropy approach and Lévy processes / Alexander Levin and Alexander Tchernitser
 Modelling the term structure of monetary rates / Luisa Izzi
 Asset liability management : a review and some new results in the presence of heavy tails / Yesim Tokat, Svetlozar T. Rachev and Eduardo S. Schwartz
 Portfolio choice theory with nonGaussian distributed returns / Sergio Ortobelli [and three others]
 Portfolio modeling with heavy tailed random vectors / Mark M. Meerschaert and HansPeter Scheffler
 Long range dependence in heavy tailed stochastic processes / Borjana RachevaIotova and Gennaday Samorodnitsky.
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5. Handbook of financial time series [2009]
 Berlin ; London : Springer, ©2009.
 Description
 Book — 1 online resource (xxix, 1050 pages) : illustrations
 Summary

 Recent Developments in GARCH Modeling. An Introduction to Univariate GARCH Models. Stationarity, Mixing, Distributional Properties and Moments of GARCH(p, q)Processes. ARCH( ) Models and Long Memory Properties. A Tour in the Asymptotic Theory of GARCH Estimation. Practical Issues in the Analysis of Univariate GARCH Models. Semiparametric and Nonparametric ARCH Modeling. Varying Coefficient GARCH Models. Extreme Value Theory for GARCH Processes. Multivariate GARCH Models. Recent Developments in Stochastic Volatility Modeling. Stochastic Volatility: Origins and Overview. Probabilistic Properties of Stochastic Volatility Models. MomentBased Estimation of Stochastic Volatility Models. Parameter Estimation and Practical Aspects of Modeling Stochastic Volatility. Stochastic Volatility Models with Long Memory. Extremes of Stochastic Volatility Models. Multivariate Stochastic Volatility. Topics in Continuous Time Processes. An Overview of AssetPrice Models. OrnsteinUhlenbeck Processes and Extensions. JumpType Levy Processes. LevyDriven ContinuousTime ARMA Processes. Continuous Time Approximations to GARCH and Stochastic Volatility Models. Maximum Likelihood and Gaussian Estimation of Continuous Time Models in Finance. Parametric Inference for Discretely Sampled Stochastic Differential Equations. Realized Volatility. Estimating Volatility in the Presence of Market Microstructure Noise: A Review of the Theory and Practical Considerations. Option Pricing. An Overview of Interest Rate Theory. Extremes of ContinuousTime Processes.. Topics in Cointegration and Unit Roots. Cointegration: Overview and Development. Time Series with Roots on or Near the Unit Circle. Fractional Cointegration. Special Topics  Risk. Different Kinds of Risk. ValueatRisk Models. CopulaBased Models for Financial Time Series. Credit Risk Modeling. Special Topics  Time Series Methods. Evaluating Volatility and Correlation Forecasts. Structural Breaks in Financial Time Series. An Introduction to Regime Switching Time Series Models. Model Selection. Nonparametric Modeling in Financial Time Series. Modelling Financial High Frequency Data Using Point Processes. Special Topics  Simulation Based Methods. Resampling and Subsampling for Financial Time Series. Markov Chain Monte Carlo. Particle Filtering.
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6. Nonparametric finance [2018]
 Klemelä, Jussi, 1965 author.
 Hoboken, NJ : John Wiley & Sons, Inc., [2018]
 Description
 Book — 1 online resource Digital: data file.
 Summary

 Preface xxiii
 1 Introduction 1 1.1 Statistical Finance 2 1.2 Risk Management 3 1.3 Portfolio Management 5 1.4 Pricing of Securities 6 Part I Statistical Finance 11
 2 Financial Instruments 13 2.1 Stocks 13 2.2 Fixed Income Instruments 19 2.3 Derivatives 23 2.4 Data Sets 27
 3 Univariate Data Analysis 33 3.1 Univariate Statistics 34 3.2 Univariate Graphical Tools 42 3.3 Univariate ParametricModels 55 3.4 Tail Modeling 61 3.5 Asymptotic Distributions 83 3.6 Univariate Stylized Facts 91
 4 Multivariate Data Analysis 95 4.1 Measures of Dependence 95 4.2 Multivariate Graphical Tools 103 4.3 Multivariate ParametricModels 107 4.4 Copulas 111
 5 Time Series Analysis 121 5.1 Stationarity and Autocorrelation 122 5.2 Model Free Estimation 128 5.3 Univariate Time Series Models 135 5.4 Multivariate Time Series Models 157 5.5 Time Series Stylized Facts 160
 6 Prediction 163 6.1 Methods of Prediction 164 6.2 Forecast Evaluation 170 6.3 Predictive Variables 175 6.4 Asset Return Prediction 182 Part II Risk Management 193
 7 Volatility Prediction 195 7.1 Applications of Volatility Prediction 197 7.2 Performance Measures for Volatility Predictors 199 7.3 Conditional Heteroskedasticity Models 200 7.4 Moving Average Methods 205 7.5 State Space Predictors 211
 8 Quantiles and ValueatRisk 219 8.1 Definitions of Quantiles 220 8.2 Applications of Quantiles 223 8.3 Performance Measures for Quantile Estimators 227 8.4 Nonparametric Estimators of Quantiles 233 8.5 Volatility Based Quantile Estimation 240 8.6 Excess Distributions in Quantile Estimation 258 8.7 Extreme ValueTheory in Quantile Estimation 288 8.8 Expected Shortfall 292 Part III Portfolio Management 297
 9 Some Basic Concepts of Portfolio Theory 299 9.1 Portfolios and Their Returns 300 9.2 Comparison of Return andWealth Distributions 312 9.3 Multiperiod Portfolio Selection 326
 10 Performance Measurement 337 10.1 The Sharpe Ratio 338 10.2 Certainty Equivalent 346 10.3 Drawdown 347 10.4 Alpha and Conditional Alpha 348 10.5 Graphical Tools of Performance Measurement 356
 11 Markowitz Portfolios 367 11.1 Variance Penalized Expected Return 369 11.2 Minimizing Variance under a Sufficient Expected Return 372 11.3 Markowitz Bullets 375 11.4 Further Topics in Markowitz Portfolio Selection 381 11.5 Examples of Markowitz Portfolio Selection 383
 12 Dynamic Portfolio Selection 385 12.1 Prediction in Dynamic Portfolio Selection 387 12.2 Backtesting Trading Strategies 393 12.3 One Risky Asset 394 12.4 Two Risky Assets 405 Part IV Pricing of Securities 419
 13 Principles of Asset Pricing 421 13.1 Introduction to Asset Pricing 422 13.2 Fundamental Theorems of Asset Pricing 430 13.3 Evaluation of Pricing and Hedging Methods 456
 14 Pricing by Arbitrage 459 14.1 Futures and the PutCall Parity 460 14.2 Pricing in Binary Models 466 14.3 BlackScholes Pricing 485 14.4 BlackScholes Hedging 505 14.5 BlackScholes Hedging and Volatility Estimation 515
 15 Pricing in IncompleteModels 521 15.1 Quadratic Hedging and Pricing 522 15.2 Utility Maximization 523 15.3 Absolutely Continuous Changes of Measures 530 15.4 GARCH Market Models 534 15.5 Nonparametric Pricing Using Historical Simulation 545 15.6 Estimation of the RiskNeutral Density 551 15.7 Quantile Hedging 555
 16 Quadratic and Local Quadratic Hedging 557 16.1 Quadratic Hedging 558 16.2 Local Quadratic Hedging 583 16.3 Implementations of Local Quadratic Hedging 595
 17 Option Strategies 615 17.1 Option Strategies 616 17.2 Profitability of Option Strategies 625
 18 Interest Rate Derivatives 649 18.1 Basic Concepts of Interest Rate Derivatives 650 18.2 Interest Rate Forwards 659 18.3 Interest Rate Options 666 18.4 Modeling Interest Rate Markets 669 References 673 Index 681.
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7. The mathematics of financial models : solving realworld problems with quantitative methods [2014]
 Ravindran, Kannoo, author.
 Hoboken, New Jersey : John Wiley & Sons, [2014]
 Description
 Book — 1 online resource (1 volume) : illustrations
 Summary

 Preface ix Acknowledgments xi CHAPTER 1 Setting the Stage 1 Why Is This Book Different? 2 Road Map of the Book 3 References 5 CHAPTER 2 Building Zero Curves 7 Market Instruments 8 Linear Interpolation 16 Cubic Splining 25 Appendix: Finding Swap Rates Using a Floating Coupon Bond Approach 41 References 43 CHAPTER 3 Valuing Vanilla Options 45 BlackScholes Formulae 47 Adaptations of the BlackScholes Formulae 53 Limitations of the BlackScholes Formulae 70 Application in Currency Risk Management 74 Appendix 78 References 80 CHAPTER 4 Simulations 81 Uniform Number Generation 82 NonUniform Number Generation 86 Applications of Simulations 93 Variance Reduction Techniques 100 References 104 CHAPTER 5 Valuing Exotic Options 107 Valuing PathIndependent, EuropeanStyle Options on a Single Variable 108 Valuing PathDependent, EuropeanStyle Options on a Single Variable 114 Valuing PathIndependent, EuropeanStyle Options on Two Variables 135 Valuing PathDependent, EuropeanStyle Options on Multiple Variables 152 References 157 CHAPTER 6 Estimating Model Parameters 159 Calibration of Parameters in the BlackScholes Model 161 Using Implied BlackScholes Volatility Surface and Zero Rate Term Structure to Value Options 169 Using Volatility Surface 178 Calibration of Interest Rate Option Model Parameters 190 Statistical Estimation 196 References 203 CHAPTER 7 The Effectiveness of Hedging Strategies 205 Delta Hedging 206 Assumptions Underlying Delta Hedging 216 Beyond Delta Hedging 223 Testing Hedging Strategies 230 Analysis Associated with the Hedging of a EuropeanStyle Vanilla Put Option 235 References 244 CHAPTER 8 Valuing Variable Annuity Guarantees 245 Basic GMDB 246 Death Benefit Riders 261 Other Details Associated with GMDB Products 269 Improving Modeling Assumptions 273 Living Benefit Riders 276 References 279 CHAPTER 9 Real Options 281
 Surrendering a GMAB Rider 282 Adding Servers in a Queue 300 References 314 CHAPTER 10 Parting Thoughts 315 About the Author 317 About the Website 319 Index 321.
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8. Statistics and finance : an introduction [2004]
 Ruppert, David, 1948 author.
 New York : Springer, [2004]
 Description
 Book — 1 online resource (xx, 473 pages) : illustrations
 Summary

 Introduction. Probability and Statistical Models. Returns. Time Series Models. Portfolio Theory. Regression. The Capital Asset Pricing Model. Options Pricing. Fixed Income Securities. Resampling. ValueatRisk. GARCH models. Nonparametric Regression and Splines. Behavioral Finance.
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9. A course on statistics for finance [2013]
 Sclove, Stanley L.
 Boca Raton, Fla. ; London : CRC Press, ©2013.
 Description
 Book — 1 online resource (xxvii, 245 pages) : illustrations
 Summary

 INTRODUCTORY CONCEPTS AND DEFINITIONS Review of Basic Statistics What Is Statistics? Characterizing Data Measures of Central Tendency Measures of Variability Higher Moments Summarizing Distributions Bivariate Data Three Variables TwoWay Tables
 Stock Price Series and Rates of Return Introduction Sharpe Ratio ValueatRisk Distributions for RORs
 Several Stocks and Their Rates of Return Introduction Review of Covariance and Correlation Two Stocks Three Stocks m Stocks
 REGRESSION Simple Linear Regression CAPM and Beta Introduction Simple Linear Regression Estimation Inference Concerning the Slope Testing Equality of Slopes of Two Lines through the Origin Linear Parametric Functions Variances Dependent upon X A Financial Application: CAPM and "Beta" Slope and Intercept
 Multiple Regression and Market Models Multiple Regression Models Market Models Models with Both Numerical and Dummy Explanatory Variables Model Building
 PORTFOLIO ANALYSIS MeanVariance Portfolio Analysis Introduction Two Stocks Three Stocks m Stocks m Stocks and a RiskFree Asset ValueatRisk Selling Short Market Models and Beta
 UtilityBased Portfolio Analysis Introduction SingleCriterion Analysis
 TIME SERIES ANALYSIS Introduction to Time Series Analysis Introduction Control Charts Moving Averages Need for Modeling Trend, Seasonality, and Randomness Models with Lagged Variables MovingAverage Models Identification of ARIMA Models Seasonal Data Dynamic Regression Models Simultaneous Equations Models
 Regime Switching Models Introduction Bull and Bear Markets
 Appendix A: Vectors and Matrices Appendix B: Normal Distributions Appendix C: Lagrange Multipliers Appendix D: Abbreviations and Symbols
 Index
 A Summary, Exercises, and Bibliography appear at the end of each chapter.
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10. Introduction to Quantitative Finance [2013]
 Blyth, Stephen.
 Oxford University Press, USA, 2013.
 Description
 Book — 1 online resource
 Summary

 Cover; Contents; PART I: PRELIMINARIES; 1 Preliminaries; 1.1 Interest rates and compounding; 1.2 Zero coupon bonds and discounting; 1.3 Annuities; 1.4 Daycount conventions; 1.5 An abridged guide to stocks, bonds and FX; 1.6 Exercises; PART II: FORWARDS, SWAPS AND OPTIONS; 2 Forward contracts and forward prices; 2.1 Derivative contracts; 2.2 Forward contracts; 2.3 Forward on asset paying no income; 2.4 Forward on asset paying known income; 2.5 Review of assumptions; 2.6 Value of forward contract; 2.7 Forward on stock paying dividends and on currency; 2.8 Physical versus cash settlement.
 2.9 Summary2.10 Exercises; 3 Forward rates and libor; 3.1 Forward zero coupon bond prices; 3.2 Forward interest rates; 3.3 Libor; 3.4 Forward rate agreements and forward libor; 3.5 Valuing floating and flxed cashflows; 3.6 Exercises; 4 Interest rate swaps; 4.1 Swap definition; 4.2 Forward swap rate and swap value; 4.3 Spotstarting swaps; 4.4 Swaps as difference between bonds; 4.5 Exercises; 5 Futures contracts; 5.1 Futures definition; 5.2 Futures versus forward prices; 5.3 Futures on libor rates; 5.4 Exercises; 6 Noarbitrage principle; 6.1 Assumption of noarbitrage.
 6.2 Monotonicity theorem6.3 Arbitrage violations; 6.4 Exercises; 7 Options; 7.1 Option definitions; 7.2 Putcall parity; 7.3 Bounds on call prices; 7.4 Call and put spreads; 7.5 Butterflies and convexity of option prices; 7.6 Digital options; 7.7 Options on forward contracts; 7.8 Exercises; PART III: REPLICATION, RISKNEUTRALITY AND THE FUNDAMENTAL THEOREM; 8 Replication and riskneutrality on the binomial tree; 8.1 Hedging and replication in the twostate world; 8.2 Riskneutral probabilities; 8.3 Multiple time steps; 8.4 General noarbitrage condition; 8.5 Exercises.
 9 Martingales, numeraires and the fundamental theorem9.1 Definition of martingales; 9.2 Numeraires and fundamental theorem; 9.3 Change of numeraire on binomial tree; 9.4 Fundamental theorem: a pragmatic example; 9.5 Fundamental theorem: summary; 9.6 Exercises; 10 Continuoustime limit and BlackScholes formula; 10.1 Lognormal limit; 10.2 Riskneutral limit; 10.3 BlackScholes formula; 10.4 Properties of BlackScholes formula; 10.5 Delta and vega; 10.6 Incorporating random interest rates; 10.7 Exercises; 11 Option price and probability duality.
 11.1 Digitals and cumulative distribution function11.2 Butterflies and riskneutral density; 11.3 Calls as spanning set; 11.4 Implied volatility; 11.5 Exercises; PART IV: INTEREST RATE OPTIONS; 12 Caps, floors and swaptions; 12.1 Caplets; 12.2 Caplet valuation and forward numeraire; 12.3 Swaptions and swap numeraire; 12.4 Summary; 12.5 Exercises; 13 Cancellable swaps and Bermudan swaptions; 13.1 European cancellable swaps; 13.2 Callable bonds; 13.3 Bermudan swaptions; 13.4 Bermudan swaption exercise criteria; 13.5 Bermudan cancellable swaps and callable bonds; 13.6 Exercises.
 Hilpisch, Yves J., author.
 Second edition.  Sebastopol, CA : O'Reilly Media, [2019]
 Description
 Book — 1 online resource (1 volume) : illustrations
 Summary

 Part I. Python and Finance; Part II. Mastering the Basics; Part III. Financial Data Science; Part IV. Algorithmic Trading; Part V. Derivatives Analytics; Appendix A. Dates and Times; Appendix B. BSM Option Class.
 Mantegna, Rosario N. (Rosario Nunzio), 1960
 Cambridge, UK ; New York : Cambridge University Press, 2000.
 Description
 Book — 1 online resource (ix, 148 pages) : illustrations Digital: data file.
 Summary

 Introduction
 Efficient market hypothesis
 Random walk
 Levy stochastic processes and limit theorems
 Scales in financial data
 Stationarity and time correlation
 Time correlation in financial time series
 Stochastic models of price dynamics
 Scaling and its breakdown
 ARCH and GARCH processes
 Financial markets and turbulence
 Correlation and anticorrelation between stocks
 Taxonomy of a stock portfolio
 Options in idealized markets
 Options in real markets.
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 Mantegna, Rosario N. (Rosario Nunzio), 1960
 Cambridge, UK ; New York : Cambridge University Press, 2000.
 Description
 Book — 1 online resource (ix, 148 pages) : illustrations Digital: data file.
 Summary

 Introduction
 Efficient market hypothesis
 Random walk
 Levy stochastic processes and limit theorems
 Scales in financial data
 Stationarity and time correlation
 Time correlation in financial time series
 Stochastic models of price dynamics
 Scaling and its breakdown
 ARCH and GARCH processes
 Financial markets and turbulence
 Correlation and anticorrelation between stocks
 Taxonomy of a stock portfolio
 Options in idealized markets
 Options in real markets.
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 Probability and finance
 Shafer, Glenn, 1946 author.
 Hoboken, NJ : John Wiley & Sons, Inc., 2019.
 Description
 Book — 1 online resource Digital: data file.
 Summary

Gametheoretic probability and finance come of age Glenn Shafer and Vladimir Vovk Probability and Finance, published in 2001, showed that perfectinformation games can be used to define mathematical probability. Based on fifteen years of further research, GameTheoretic Foundations for Probability and Finance presents a mature view of the foundational role game theory can play. Its account of probability theory opens the way to new methods of prediction and testing and makes many statistical methods more transparent and widely usable. Its contributions to finance theory include purely gametheoretic accounts of Ito stochastic calculus, the capital asset pricing model, the equity premium, and portfolio theory. GameTheoretic Foundations for Probability and Finance is a book of research. It is also a teaching resource. Each chapter is supplemented with carefully designed exercises and notes relating the new theory to its historical context. Praise from early readers ver since Kolmogorov's Grundbegriffe, the standard mathematical treatment of probability theory has been measuretheoretic. In this groundbreaking work, Shafer and Vovk give a gametheoretic foundation instead. While being just as rigorous, the gametheoretic approach allows for vast and useful generalizations of classical measuretheoretic results, while also giving rise to new, radical ideas for prediction, statistics and mathematical finance without stochastic assumptions. The authors set out their theory in great detail, resulting in what is definitely one of the most important books on the foundations of probability to have appeared in the last few decades. Peter Gr130 0Wiley series in probability and statisticsnwald, CWI and University of Leiden hafer and Vovk have thoroughly rewritten their 2001 book on the gametheoretic foundations for probability and for finance. They have included an account of the tremendous growth that has occurred since, in the gametheoretic and pathwise approaches to stochastic analysis and in their applications to continuoustime finance. This new book will undoubtedly spur a better understanding of the foundations of these very important fields, and we should all be grateful to its authors. Ioannis Karatzas, Columbia University.
 Jansen, Stefan.
 [S.l.] : Packt Publishing, 2020.
 Description
 Book — 1 online resource
 Summary

 Table of Contents Machine Learning for Trading  From Idea to Execution Market and Fundamental Data  Sources and Techniques Alternative Data for Finance  Categories and Use Cases Financial Feature Engineering  How to Research Alpha Factors Portfolio Optimization and Performance Evaluation The Machine Learning Process Linear Models  From Risk Factors to Return Forecasts The ML4T Workflow  From Model to Strategy Backtesting (N.B. Please use the Look Inside option to see further chapters).
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 Jansen, Stefan, author.
 Birmingham, UK : Packt Publishing, 2018.
 Description
 Book — 1 online resource (1 volume) : illustrations
 Summary

 Table of Contents Machine Learning for Trading Market and Fundamental Data Alternative Data for Finance Alpha Factor Research Strategy Evaluation The Machine Learning Process Linear Models Time Series Models Bayesian Machine Learning Decision Trees and Random Forests Gradient Boosting Machines Unsupervised Learning Working with Text Data Topic Modeling Word Embeddings Next Steps.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Jansen, Stefan, author.
 Birmingham, UK : Packt Publishing, 2018.
 Description
 Book — 1 online resource (1 volume) : illustrations
 Summary

 Table of Contents Machine Learning for Trading Market and Fundamental Data Alternative Data for Finance Alpha Factor Research Strategy Evaluation The Machine Learning Process Linear Models Time Series Models Bayesian Machine Learning Decision Trees and Random Forests Gradient Boosting Machines Unsupervised Learning Working with Text Data Topic Modeling Word Embeddings Next Steps.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Voit, Johannes, 1957
 3rd ed.  Berlin ; New York : Springer, c2005.
 Description
 Book — xv, 378 p. : ill. ; 25 cm.
 Summary

This highly praised introductory treatment describes the parallels between statistical physics and finance  both those established in the 100year long interaction between these disciplines, as well as new research results on financial markets. The randomwalk technique, well known in physics, is also the basic model in finance, upon which are built, for example, the BlackScholes theory of option pricing and hedging, plus methods of portfolio optimization. Here the underlying assumptions are assessed critically. Using empirical financial data and analogies to physical models such as fluid flows, turbulence, or superdiffusion, the book develops a more accurate description of financial markets based on random walks. With this approach, novel methods for derivative pricing and risk management can be formulated. Computer simulations of interactingagent models provide insight into the mechanisms underlying unconventional price dynamics. It is shown that stock exchange crashes can be modelled in ways analogous to phase transitions and earthquakes, and sometimes have even been predicted successfully. This third edition of "The Statistical Mechanics of Financial Markets" especially stands apart from other treatments because it offers new chapters containing a practitioner's treatment of two important current topics in banking: the basic notions and tools of risk management and capital requirements for financial institutions, including an overview of the new Basel II capital framework which may well set the risk management standards in scores of countries for years to come.
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 Online
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
HG176.5 .V64 2005  Unknown 
 Neapolitan, Richard E.
 San Fransisco, CA : Morgan Kaufmann Publishers, ©2007.
 Description
 Book — 1 online resource (viii, 413 pages) : illustrations Digital: text file.
 Summary

 I: Informatics and Baysesian Networks
 Introduction to Informatics
 Basics of Probability and Statistics
 Algorithms for Bayesian Networks
 Decision Trees and Influence Diagrams. II: Business Informatics: Collaborative Filtering
 Targeted Advertising
 Market Basket Analysis
 Venture Capital Decision Making
 Measuring Operational Risk
 Credit Scoring
 Applications to Investment Science. Appendices.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Neapolitan, Richard E.
 San Fransisco, CA : Morgan Kaufmann Publishers, ©2007.
 Description
 Book — 1 online resource (viii, 413 pages) : illustrations Digital: text file.
 Summary

 I: Informatics and Baysesian Networks
 Introduction to Informatics
 Basics of Probability and Statistics
 Algorithms for Bayesian Networks
 Decision Trees and Influence Diagrams. II: Business Informatics: Collaborative Filtering
 Targeted Advertising
 Market Basket Analysis
 Venture Capital Decision Making
 Measuring Operational Risk
 Credit Scoring
 Applications to Investment Science. Appendices.
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