{"product_id":"inside-volatility-filtering-isbn-9781118943977","title":"Inside Volatility Filtering","description":"\u003cb\u003eA new, more accurate take on the classical approach to volatility evaluation\u003c\/b\u003e \u003cp\u003e\u003ci\u003eInside Volatility Filtering\u003c\/i\u003e presents a new approach to volatility estimation, using financial econometrics based on a more accurate estimation of the hidden state. Based on the idea of \"filtering\", this book lays out a two-step framework involving a Chapman-Kolmogorov prior distribution followed by Bayesian posterior distribution to develop a robust estimation based on all available information. This new second edition includes guidance toward basing estimations on historic option prices instead of stocks, as well as Wiener Chaos Expansions and other spectral approaches. The author's statistical trading strategy has been expanded with more in-depth discussion, and the companion website offers new topical insight, additional models, and extra charts that delve into the profitability of applied model calibration. You'll find a more precise approach to the classical time series and financial econometrics evaluation, with expert advice on turning data into profit. \u003c\/p\u003e\u003cp\u003eFinancial markets do not always behave according to a normal bell curve. Skewness creates uncertainty and surprises, and tarnishes trading performance, but it's not going away. This book shows traders how to work with skewness: how to predict it, estimate its impact, and determine whether the data is presenting a warning to stay away or an opportunity for profit. \u003c\/p\u003e\u003cul\u003e \u003cli\u003eBase volatility estimations on more accurate data\u003c\/li\u003e \u003cli\u003eIntegrate past observation with Bayesian probability\u003c\/li\u003e \u003cli\u003eExploit posterior distribution of the hidden state for optimal estimation\u003c\/li\u003e \u003cli\u003eBoost trade profitability by utilizing \"skewness\" opportunities\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eWall Street is constantly searching for volatility assessment methods that will make their models more accurate, but precise handling of skewness is the key to true accuracy. \u003ci\u003eInside Volatility Filtering\u003c\/i\u003e shows you a better way to approach non-normal distributions for more accurate volatility estimation. Foreword ix \u003c\/p\u003e\u003cp\u003eAcknowledgments (Second Edition) xi\u003c\/p\u003e \u003cp\u003eAcknowledgments (First Edition) xiii\u003c\/p\u003e \u003cp\u003eIntroduction (Second Edition) xv\u003c\/p\u003e \u003cp\u003eIntroduction (First Edition) xvii\u003c\/p\u003e \u003cp\u003eSummary xvii\u003c\/p\u003e \u003cp\u003eContributions and Further Research xxiii\u003c\/p\u003e \u003cp\u003eData and Programs xxiv\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 1 The Volatility Problem 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 1\u003c\/p\u003e \u003cp\u003eThe Stock Market 2\u003c\/p\u003e \u003cp\u003eThe Stock Price Process 2\u003c\/p\u003e \u003cp\u003eHistoric Volatility 3\u003c\/p\u003e \u003cp\u003eThe Derivatives Market 5\u003c\/p\u003e \u003cp\u003eThe Black-Scholes Approach 5\u003c\/p\u003e \u003cp\u003eThe Cox Ross Rubinstein Approach 7\u003c\/p\u003e \u003cp\u003eJump Diffusion and Level-Dependent Volatility 8\u003c\/p\u003e \u003cp\u003eJump Diffusion 8\u003c\/p\u003e \u003cp\u003eLevel-Dependent Volatility 11\u003c\/p\u003e \u003cp\u003eLocal Volatility 14\u003c\/p\u003e \u003cp\u003eThe Dupire Approach 14\u003c\/p\u003e \u003cp\u003eThe Derman Kani Approach 17\u003c\/p\u003e \u003cp\u003eStability Issues 18\u003c\/p\u003e \u003cp\u003eCalibration Frequency 19\u003c\/p\u003e \u003cp\u003eStochastic Volatility 21\u003c\/p\u003e \u003cp\u003eStochastic Volatility Processes 21\u003c\/p\u003e \u003cp\u003eGARCH and Diffusion Limits 22\u003c\/p\u003e \u003cp\u003eThe Pricing PDE under Stochastic Volatility 26\u003c\/p\u003e \u003cp\u003eThe Market Price of Volatility Risk 26\u003c\/p\u003e \u003cp\u003eThe Two-Factor PDE 27\u003c\/p\u003e \u003cp\u003eThe Generalized Fourier Transform 28\u003c\/p\u003e \u003cp\u003eThe Transform Technique 28\u003c\/p\u003e \u003cp\u003eSpecial Cases 30\u003c\/p\u003e \u003cp\u003eThe Mixing Solution 32\u003c\/p\u003e \u003cp\u003eThe Romano Touzi Approach 32\u003c\/p\u003e \u003cp\u003eA One-Factor Monte-Carlo Technique 34\u003c\/p\u003e \u003cp\u003eThe Long-Term Asymptotic Case 35\u003c\/p\u003e \u003cp\u003eThe Deterministic Case 35\u003c\/p\u003e \u003cp\u003eThe Stochastic Case 37\u003c\/p\u003e \u003cp\u003eA Series Expansion on Volatility-of-Volatility 39\u003c\/p\u003e \u003cp\u003eLocal Volatility Stochastic Volatility Models 42\u003c\/p\u003e \u003cp\u003eStochastic Implied Volatility 43\u003c\/p\u003e \u003cp\u003eJoint SPX and VIX Dynamics 45\u003c\/p\u003e \u003cp\u003ePure-Jump Models 47\u003c\/p\u003e \u003cp\u003eVariance Gamma 47\u003c\/p\u003e \u003cp\u003eVariance Gamma with Stochastic Arrival 51\u003c\/p\u003e \u003cp\u003eVariance Gamma with Gamma Arrival Rate 53\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 2 The Inference Problem 55\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 55\u003c\/p\u003e \u003cp\u003eUsing Option Prices 58\u003c\/p\u003e \u003cp\u003eConjugate Gradient (Fletcher-Reeves-Polak-Ribiere) Method 59\u003c\/p\u003e \u003cp\u003eLevenberg-Marquardt (LM) Method 59\u003c\/p\u003e \u003cp\u003eDirection Set (Powell) Method 61\u003c\/p\u003e \u003cp\u003eNumeric Tests 62\u003c\/p\u003e \u003cp\u003eThe Distribution of the Errors 65\u003c\/p\u003e \u003cp\u003eUsing Stock Prices 65\u003c\/p\u003e \u003cp\u003eThe Likelihood Function 65\u003c\/p\u003e \u003cp\u003eFiltering 69\u003c\/p\u003e \u003cp\u003eThe Simple and Extended Kalman Filters 72\u003c\/p\u003e \u003cp\u003eThe Unscented Kalman Filter 74\u003c\/p\u003e \u003cp\u003eKushner’s Nonlinear Filter 77\u003c\/p\u003e \u003cp\u003eParameter Learning 80\u003c\/p\u003e \u003cp\u003eParameter Estimation via MLE 95\u003c\/p\u003e \u003cp\u003eDiagnostics 108\u003c\/p\u003e \u003cp\u003eParticle Filtering 111\u003c\/p\u003e \u003cp\u003eComparing Heston with Other Models 133\u003c\/p\u003e \u003cp\u003eThe Performance of the Inference Tools 141\u003c\/p\u003e \u003cp\u003eThe Bayesian Approach 158\u003c\/p\u003e \u003cp\u003eUsing the Characteristic Function 172\u003c\/p\u003e \u003cp\u003eIntroducing Jumps 174\u003c\/p\u003e \u003cp\u003ePure-Jump Models 184\u003c\/p\u003e \u003cp\u003eRecapitulation 201\u003c\/p\u003e \u003cp\u003eModel Identification 201\u003c\/p\u003e \u003cp\u003eConvergence Issues and Solutions 202\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 3 The Consistency Problem 203\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 203\u003c\/p\u003e \u003cp\u003eThe Consistency Test 206\u003c\/p\u003e \u003cp\u003eThe Setting 206\u003c\/p\u003e \u003cp\u003eThe Cross-Sectional Results 206\u003c\/p\u003e \u003cp\u003eTime-Series Results 209\u003c\/p\u003e \u003cp\u003eFinancial Interpretation 210\u003c\/p\u003e \u003cp\u003eThe “Peso” Theory 214\u003c\/p\u003e \u003cp\u003eBackground 214\u003c\/p\u003e \u003cp\u003eNumeric Results 215\u003c\/p\u003e \u003cp\u003eTrading Strategies 216\u003c\/p\u003e \u003cp\u003eSkewness Trades 216\u003c\/p\u003e \u003cp\u003eKurtosis Trades 217\u003c\/p\u003e \u003cp\u003eDirectional Risks 217\u003c\/p\u003e \u003cp\u003eAn Exact Replication 219\u003c\/p\u003e \u003cp\u003eThe Mirror Trades 220\u003c\/p\u003e \u003cp\u003eAn Example of the Skewness Trade 220\u003c\/p\u003e \u003cp\u003eMultiple Trades 225\u003c\/p\u003e \u003cp\u003eHigh Volatility-of-Volatility and High Correlation 225\u003c\/p\u003e \u003cp\u003eNon-Gaussian Case 230\u003c\/p\u003e \u003cp\u003eVGSA 232\u003c\/p\u003e \u003cp\u003eA Word of Caution 236\u003c\/p\u003e \u003cp\u003eForeign Exchange, Fixed Income, and Other Markets 237\u003c\/p\u003e \u003cp\u003eForeign Exchange 237\u003c\/p\u003e \u003cp\u003eFixed Income 238\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 4 The Quality Problem 241\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 241\u003c\/p\u003e \u003cp\u003eAn Exact Solution? 241\u003c\/p\u003e \u003cp\u003eNonlinear Filtering 242\u003c\/p\u003e \u003cp\u003eStochastic PDE 243\u003c\/p\u003e \u003cp\u003eWiener Chaos Expansion 244\u003c\/p\u003e \u003cp\u003eFirst-Order WCE 247\u003c\/p\u003e \u003cp\u003eSimulations 248\u003c\/p\u003e \u003cp\u003eSecond-Order WCE 251\u003c\/p\u003e \u003cp\u003eQuality of Observations 251\u003c\/p\u003e \u003cp\u003eHistoric Spot Prices 252\u003c\/p\u003e \u003cp\u003eHistoric Option Prices 252\u003c\/p\u003e \u003cp\u003eConclusion 262\u003c\/p\u003e \u003cp\u003eBibliography 263\u003c\/p\u003e \u003cp\u003eIndex 279\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eALIREZA JAVAHERI\u003c\/b\u003e is the head of Equities Quantitative Research Americas at JP Morgan and an adjunct professor of Mathematical Finance at the Courant Institute of New York University, as well as Baruch College. He has worked in the field of derivatives quantitative research since 1994 in a variety of investment banks, including Goldman Sachs and Citigroup.   \u003c\/p\u003e\u003cp\u003eThis fully updated and revised \u003ci\u003eSecond Edition\u003c\/i\u003e of the Wilmott Award-winning book \u003ci\u003eInside Volatility Arbitrage\u003c\/i\u003e demonstrates how to filter data using time series and financial econometrics to discover the best possible estimation of hidden opportunities given all the available information up to that point. All-new content includes estimation from historic option prices, instead of stocks, to gain better observation quality; spectral approaches and Wiener Chaos Expansions; and expanded in-depth examples of the statistical trading strategy. \u003c\/p\u003e\u003cp\u003eIn even greater detail, Javaheri shares in-depth information on the relationship between volatility and the stock and derivatives markets, detailed insights on Brownian motion for stock price returns, and option-pricing techniques such as inversion of the Fourier transform and mixing Monte Carlo. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eInside\u003c\/i\u003e \u003ci\u003eVolatility Filtering\u003c\/i\u003e also illuminates how to: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e Effectively use a variety of models, from local volatility and stochastic volatility models to pure-jump models\u003c\/li\u003e \u003cli\u003e Accurately estimate model parameters using two possible sets of data—options prices and historic stock prices\u003c\/li\u003e \u003cli\u003e Best apply parametric inference methodologies to assets, and why you should question the consistency of information contained in the options and stock markets\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eAdditional models and extra illustrative charts show you how to profit in these scenarios using the nuts and bolts of applied model calibration. \u003ci\u003eInside Volatility Filtering, Second Edition\u003c\/i\u003e shows you a better way to approach abnormal distributions for more accurate volatility estimation.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989432746213,"sku":"NP9781118943977","price":110.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118943977.jpg?v=1761784081","url":"https:\/\/k12savings.com\/products\/inside-volatility-filtering-isbn-9781118943977","provider":"K12savings","version":"1.0","type":"link"}