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The Heston model and its extensions in Matlab and C#
Tap into the power of the most popular stochastic volatility model for pricing equity derivatives
Since its introduction in 1993, the Heston model has become a popular model for pricing equity derivatives, and the most popular stochastic volatility model in financial engineering. This vital resource provides a thorough derivation of the original model, and includes the most important extensions and refinements that have allowed the model to produce option prices that are more accurate and volatility surfaces that better reflect market conditions. The book's material is drawn from research papers and many of the models covered and the computer codes are unavailable from other sources.
The book is light on theory and instead highlights the implementation of the models. All of the models found here have been coded in Matlab and C#. This reliable resource offers an understanding of how the original model was derived from Ricatti equations, and shows how to implement implied and local volatility, Fourier methods applied to the model, numerical integration schemes, parameter estimation, simulation schemes, American options, the Heston model with time-dependent parameters, finite difference methods for the Heston PDE, the Greeks, and the double Heston model.
* A groundbreaking book dedicated to the exploration of the Heston model—a popular model for pricing equity derivatives
* Includes a companion website, which explores the Heston model and its extensions all coded in Matlab and C#
* Written by Fabrice Douglas Rouah a quantitative analyst who specializes in financial modeling for derivatives for pricing and risk management
Engaging and informative, this is the first book to deal exclusively with the Heston Model and includes code in Matlab and C# for pricing under the model, as well as code for parameter estimation, simulation, finite difference methods, American options, and more.
eBook
The Heston Model and Its Extensions in VBA
Practical options pricing for better-informed investment decisions.
The Heston Model and Its Extensions in VBA is the definitive guide to options pricing using two of the derivatives industry's most powerful modeling tools—the Heston model, and VBA. Light on theory, this extremely useful reference focuses on implementation, and can help investors more efficiently—and accurately—exploit market information to better inform investment decisions. Coverage includes a description of the Heston model, with specific emphasis on equity options pricing and variance modeling, The book focuses not only on the original Heston model, but also on the many enhancements and refinements that have been applied to the model, including methods that use the Fourier transform, numerical integration schemes, simulation, methods for pricing American options, and much more. The companion website offers pricing code in VBA that resides in an extensive set of Excel spreadsheets.
The Heston model is the derivatives industry's most popular stochastic volatility model for pricing equity derivatives. This book provides complete guidance toward the successful implementation of this valuable model using the industry's ubiquitous financial modeling software, giving users the understanding—and VBA code—they need to produce option prices that are more accurate, and volatility surfaces that more closely reflect market conditions.
Derivatives pricing is often the hinge on which profit is made or lost in financial institutions, making accuracy of utmost importance. This book will help risk managers, traders, portfolio managers, quants, academics and other professionals better understand the Heston model and its extensions, in a writing style that is clear, concise, transparent and easy to understand. For better pricing accuracy, The Heston Model and Its Extensions in VBA is a crucial resource for producing more accurate model outputs such as prices, hedge ratios, volatilities, and graphs.
eBook
The Heston Model and its Extensions in Matlab and C#
Tap into the power of the most popular stochastic volatility model for pricing equity derivatives Since its introduction in 1993, the Heston model has become a popular model for pricing equity derivatives, and the most popular stochastic volatility model in financial engineering. This vital resource provides a thorough derivation of the original model, and includes the most important extensions and refinements that have allowed the model to produce option prices that are more accurate and volatility surfaces that better reflect market conditions. The book's material is drawn from rese
Book
The Heston model and its extensions in VBA + website
\"Practical options pricing for better-informed investment decisions.The Heston Model and Its Extensions in VBA is the definitive guide to options pricing using two of the derivatives industry's most powerful modeling tools--the Heston model, and VBA. Light on theory, this extremely useful reference focuses on implementation, and can help investors more efficiently--and accurately--exploit market information to better inform investment decisions. Coverage includes a description of the Heston model, with specific emphasis on equity options pricing and variance modeling, The book focuses not only on the original Heston model, but also on the many enhancements and refinements that have been applied to the model, including methods that use the Fourier transform, numerical integration schemes, simulation, methods for pricing American options, and much more. The companion website offers pricing code in VBA that resides in an extensive set of Excel spreadsheets.The Heston model is the derivatives industry's most popular stochastic volatility model for pricing equity derivatives. This book provides complete guidance toward the successful implementation of this valuable model using the industry's ubiquitous financial modeling software, giving users the understanding--and VBA code--they need to produce option prices that are more accurate, and volatility surfaces that more closely reflect market conditions.Derivatives pricing is often the hinge on which profit is made or lost in financial institutions, making accuracy of utmost importance. This book will help risk managers, traders, portfolio managers, quants, academics and other professionals better understand the Heston model and its extensions, in a writing style that is clear, concise, transparent and easy to understand. For better pricing accuracy, The Heston Model and Its Extensions in VBA is a crucial resource for producing more accurate model outputs such as prices, hedge ratios, volatilities, and graphs\"--
Book
The Heston Model and its Extensions in Matlab and C#, + Website
Tap into the power of the most popular stochastic volatility model for pricing equity derivativesSince its introduction in 1993, the Heston model has become a popular model for pricing equity derivatives, and the most popular stochastic volatility model in financial engineering. This vital resource provides a thorough derivation of the original model, and includes the most important extensions and refinements that have allowed the model to produce option prices that are more accurate and volatility surfaces that better reflect market conditions. The book's material is drawn from research papers and many of the models covered and the computer codes are unavailable from other sources.The book is light on theory and instead highlights the implementation of the models. All of the models found here have been coded in Matlab and C#. This reliable resource offers an understanding of how the original model was derived from Ricatti equations, and shows how to implement implied and local volatility, Fourier methods applied to the model, numerical integration schemes, parameter estimation, simulation schemes, American options, the Heston model with time-dependent parameters, finite difference methods for the Heston PDE, the Greeks, and the double Heston model.A groundbreaking book dedicated to the exploration of the Heston model—a popular model for pricing equity derivativesIncludes a companion website, which explores the Heston model and its extensions all coded in Matlab and C#Written by Fabrice Douglas Rouah a quantitative analyst who specializes in financial modeling for derivatives for pricing and risk managementEngaging and informative, this is the first book to deal exclusively with the Heston Model and includes code in Matlab and C# for pricing under the model, as well as code for parameter estimation, simulation, finite difference methods, American options, and more.Note: The ebook version does not provide access to the companion files.
eBook
Self-Sampling Is Associated with Increased Detection of Human Papillomavirus DNA in the Genital Tract of HIV-Seropositive Women
Background. Analysis of self-collected swab samples from the genital tract could improve accrual and retention of women in studies of human papillomavirus (HPV) infection and precancerous cervical lesions. Self-collected vaginal swab specimens and physician-collected cervical swab specimens were compared for detection and typing of HPV DNA in 158 HIV-seropositive women. Methods. Paired samples were collected for 157 participants. β-Globin was not detected in 6 (3.3%) physician-collected specimens and 8 (4.3%) self-obtained specimens collected from 11 women, leaving 146 paired samples suitable for PCR analysis. HPV DNA was amplified with the HPV primers PGMY09 and PGMY11 and typed using the line blot assay. Results. HPV DNA was detected more frequently in self-collected samples (95 [65.1%] of 146), compared with physician-collected samples (78 [53.4%] of 146) (P = .04). Self-collected samples contained a greater number of types (mean ± SD, 1.60 ± 1.80 types; 95% confidence interval [CI], 1.31–1.90), compared with physician-collected samples (mean ± SD, 1.25 ± 1.66 types; 95% CI, 0.98–1.52) (P = .04). A good agreement between sampling methods was achieved for detection of any HPV DNA (κ = 0.73; 95% CI, 0.58–0.89), high-risk types (κ = 0.84; 95% CI, 0.68–0.99), and low-risk types (κ = 0.71; 95% CI, 0.67–0.75). Agreement between sampling methods for detection of HPV DNA was found for 24 (88.8%) of 27 follow-up samples collected from a total of 20 women. A comparison of samples collected at consecutive visits revealed agreements for detection of any HPV DNA, detection of high-risk HPV, and HPV typing results between visits of 88.9% (24 of 27 samples), 81.5% (22 of 27), and 55.5% (15 of 27), respectively, for physician-collected samples, and 96.3% (26 of 27 samples), 92.6% (25 of 27), and 55.5% (15 of 27), respectively, for self-collected samples. Conclusion. Analysis of self-collected vaginal swab samples improved the detection rate of HPV, suggesting that such samples might be of greater value than physician-obtained samples in studies of HPV transmission.
Journal Article
Molecular Analysis of Human Papillomavirus Type 52 Isolates Detected in the Genital Tract of Human Immunodeficiency Virus–Seropositive and –Seronegative Women
Human papillomavirus (HPV) type 52 DNA was detected in cervicovaginal lavage samples from 91 (12.4%) of 732 human immunodeficiency virus (HIV)–seropositive women and 23 (7.1%) of 323 HIV-seronegative women (P=.0004). HIV infection was an independent predictor for HPV-52 infection when controlling for age and sexual activity (odds ratio, 2.21; 95% confidence interval, 1.30–3.75: P=.003). We describe the genomic polymorphism of 114 HPV-52 isolates. Long control region (LCR) mutations defined 27 HPV-52 variants. Nearly 32% of HPV-52 isolates carried deletions in the LCR. E6 and E7 mutations defined 17 and 9 variants, respectively. Five nonsynonymous E6 mutations were clustered from amino acids 92 to 94, near the putative p53 binding area. White women were more frequently infected by the prototype strain than were women of African descent (P=.0001). The genetic diversity of HPV-52 should facilitate the investigation of the role of genomic variations in cervical disease
Journal Article
Commodity Trading Advisors
Authoritative, up-to-date research and analysis that provides adramatic new understanding of the rewards-and risks-of investing inCTAsCommodity Trading Advisors (CTAs) are an increasingly popular andpotentially profitable investment alternative for institutionalinvestors and high-net-worth individuals. Commodity TradingAdvisors is one of the first books to study their performance indetail and analyze the \"survivorship bias\" present in CTAperformance data. This book investigates the many benefits andrisks associated with CTAs, examining the risk/returncharacteristics of a number of different strategies deployed byCTAs from a sophisticated investor's perspective. A contributedwork, its editors and contributing authors are among today'sleading voices on the topic of commodity trading advisors and averitable \"Who's Who\" in hedge fund and CTA research.Greg N. Gregoriou (Plattsburgh, NY) is a Visiting AssistantProfessor of Finance and Research Coordinator in the School ofBusiness and Economics at the State University of New York.Vassilios N. Karavas (Amherst, MA) is Director of Research atSchneeweis Partners. Francois-Serge Lhabitant (Coppet, Switzerland)is a FAME Research Fellow, and a Professor of Finance at EDHEC(France) and at HEC University of Lausanne (Switzerland). FabriceRouah (Montreal, Quebec) is Institut de Finance Mathématiquede Montréal Scholar in the finance program at McGillUniversity.
eBook