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Search results “Volatility for options pricing models”

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Views: 185000 Option Alpha

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Views: 68329 projectoption

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[here is my xls https://trtl.bz/2Ri5R7r] Instead of arbitrarily selecting the up (u) and down (d) jumps in the binomial, we can "match them to a volatility input assumption, σ. The correct values are given by u = exp[σ*sqrt(Δt)] and d = 1/u; notice that the exponent is just apply the Square Root Rule (SRR) of scaling the per annum volatility to the correct period volatility; in this example, a 30.0% per annum volatility translated into 30.0% * sqrt(0.25) = 15.0% three-month volatility. When we use these assumptions, we implicitly assume that the geometric (aka, continuous) returns are normally distributed which is tantamount to assuming the prices are lognormally distributed, and this version of the binomial (aka, Cox Ross Rubinstein) converges on the classic Black-Scholes-Merton (BSM) as the number of steps increases. Discuss this video here in our FRM forum: https://trtl.bz/2Vx7uLw.
Views: 630 Bionic Turtle

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Speaker: Jason Strimpel (@JasonStrimpel) Python has become an increasingly important tool in the domain of quantitative and algorithmic trading and research. This extends from senior quantitative analysts pricing complex derivatives using numerical techniques all the way to the retail trader using closed form valuation methods and analysis techniques. This talk will focus on the uses of Python in discovering unobserved features of listed equity options. The Black-Scholes option pricing formula was first published in 1973 in a paper called "The Pricing of Options and Corporate Liabilities". In that paper Fischer Black and Myron Scholes derived an equation which estimates the price of an option over time. This formula and its associated "greeks" have become ubiquitous in options trading. In this talk, we'll demonstrate how to gather options data using the Pandas module and apply various transformations to obtain the theoretical value of the option and the associated greeks. We'll then extend the talk to discuss implied volatility and show how to use Numpy methods to compute implied volatility. We'll use the results to visualize the so-called volatility skew and term structure to help inform potential trading decisions. Event Page: http://www.meetup.com/PyData-SG/events/226837711/ Produced by Engineers.SG Help us caption & translate this video! http://amara.org/v/WCeb/
Views: 3508 Engineers.SG

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This video demonstrates my Matlab implementation of Monte-Carlo simulation used to price options on equities while accounting for non-constant volatility, specifically stochastic mean reverting volatility as per the Heston model. I am happy to connect with other financial professionals and recruiters on LinkedIn. You can find my profile here: https://www.linkedin.com/in/alex-ockenden-81756aa1
Views: 3014 Alexander Ockenden

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MIT 18.S096 Topics in Mathematics with Applications in Finance, Fall 2013 View the complete course: http://ocw.mit.edu/18-S096F13 Instructor: Stephen Blythe This guest lecture focuses on option price and probability duality. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 43961 MIT OpenCourseWare

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Just what is implied volatility? Find out how this important data is derived from the Black-Scholes options pricing model, and how implied volatility can impact the prices of call and put options.
Views: 4686 Zecco

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Using the market price for an option on Google's stock, I use Excel's GOAL SEEK function to estimate implied volatility. Implied volatility is a reverse-engineering exercise: we find the volatility that produces a MODEL VALUE = MARKET PRICE. For more financial risk videos, visit our website! http://www.bionicturtle.com
Views: 77707 Bionic Turtle

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Views: 3245 Finideas Sol

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The volatility smile is a real-life pattern that is observed when different strikes of option, with the same underlying and same expiration date are plotted on a graph. Buy The Book Here: https://amzn.to/2CLG5y2 Follow Patrick on Twitter Here: https://twitter.com/PatrickEBoyle Volatility smiles are implied volatility patterns that arise in pricing financial options. It corresponds to finding one single parameter (implied volatility) that is needed to be modified for the Black-Scholes formula to fit market prices. In particular for a given expiration, options whose strike price differs substantially from the underlying asset's price command higher prices (and thus implied volatilities) than what is suggested by standard option pricing models. These options are said to be either deep in-the-money or out-of-the-money. Graphing implied volatilities against strike prices for a given expiry yields a skewed "smile" instead of the expected flat surface. The pattern differs across various markets. Equity options traded in American markets did not show a volatility smile before the Crash of 1987 but began showing one afterwards. It is believed that investor reassessments of the probabilities of fat-tail have led to higher prices for out-of-the-money options. This anomaly implies deficiencies in the standard Black-Scholes option pricing model which assumes constant volatility and log-normal distributions of underlying asset returns. Empirical asset returns distributions, however, tend to exhibit fat-tails (kurtosis) and skew. Modelling the volatility smile is an active area of research in quantitative finance, and better pricing models such as the stochastic volatility model partially address this issue. A related concept is that of term structure of volatility, which describes how (implied) volatility differs for related options with different maturities. We will be learning about that in tomorrows video. An implied volatility surface is a 3-D plot that plots volatility smile and term structure of volatility in a consolidated three-dimensional surface for all options on a given underlying asset.
Views: 269 Patrick Boyle

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This is Black-Scholes for a European-style call option. You can download the XLS @ this forum thread on our website at http://www.bionicturtle.com.
Views: 155012 Bionic Turtle

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Lecture 23: Carter introduces the Black-Scholes options pricing formula through conceptual discussion and trading examples. Historical and implied volatility are defined.

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How do you find out the implied volatility for your option pricing model? Here's the answer.

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Tom Sosnoff, Tony Battista, and Jacob Perlman discuss a model that can be used in option pricing formulas to try to account for the volatility skew, which pushes the prices of OTM options higher. Catch Jacob, our in-studio Math Wizard, every Thursday live at 9am CT: only at https://tastytrade.com/tt/live

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A plot of implied volatility (i.e., the volatility that forces the BSM model option price to equal the observed market price) against strike price. The smile is proof the model is imprecise (incorrect in some assumption); e.g., returns are not lognormally distributed. For more financial risk videos, visit our website! http://www.bionicturtle.com
Views: 41104 Bionic Turtle

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This video is a part of our course on Certification in Applied Derivatives (https://finshiksha.com/courses/certification-in-applied-derivatives/), and talks about the Binomial Model of Option Pricing.
Views: 491 FinShiksha

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We price an American put option using 3 period binomial tree model. We cover the methdology of working backwards through the tree to price the option in multi-period binomial framework. Empahsis is also placed on early exercise feature of American option and it's significance in pricing. Although not a prerequisite, viewers can look at the tutorial on risk neutral valuation in binomial model for understanding how to calculate risk neutral probability of stock price going up.
Views: 77406 finCampus Lecture Hall

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Pricing Options using Black-Scholes Model, part 1 contain calculation on excel using data from NSE and part 2 explains how to use goal seek function to get implied volatility.

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Introduces the Black-Scholes Option Pricing Model and walks through an example of using the BS OPM to find the value of a call. Supplemental files (Standard Normal Distribution Table, BS OPM Formulas, and BS OPM Spreadsheet) are provided with links to the files in Google Documents. tinyurl.com/Bracker-StNormTable tinyurl.com/Bracker-BSOPM tinyurl.com/Bracker-BSOPMspread
Views: 243104 Kevin Bracker

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Views: 4136 Bullish Bears

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FinTree website link: http://www.fintreeindia.com This series of videos discusses the following key points: 1) Lognormal property of stock prices, the distribution of rates of return, and the calculation of expected return. 2) Realized return and historical volatility of a stock. 3) Assumptions underlying the Black- Scholes -Merton option pricing model. 4) Value of a European option using the Black- Scholes -Merton model on a non-dividend-paying stock. 5) Complications involving the valuation of warrants. 6) Implied volatilities and describe how to compute implied volatilities from market prices of options using the Black- Scholes -Merton model. 7) How dividends affect the early decision for American call and put options. 8) Value of a European option using the Black- Scholes -Merton model on a dividend-paying stock. 9) Use of Black's Approximation in calculating the value of an American call option on a dividend-paying stock. FB Page link :http://www.facebook.com/Fin... We love what we do, and we make awesome video lectures for CFA and FRM exams. Our Video Lectures are comprehensive, easy to understand and most importantly, fun to study with! This Video lecture was recorded by our popular trainer for CFA, Mr. Utkarsh Jain, during one of his live CFA Level I Classes in Pune (India). #CFA #FinTree

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@ Members :: This Video would let you know about parameters of Black Scholes Options Pricing Model (BSOPM) like Stock Price , Strike Price , Time to Maturity , Volatility ( Implied Volatility ) and Risk Free Interest Rates. You are most welcome to connect with us at 91-9899242978 (Handheld) , Skype ~Rahul5327 , Twitter @ Rahulmagan8 , [email protected] , [email protected] or visit our website - www.treasuryconsulting.in

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Sheldon Natenberg, legendary author and head educator at Chicago Trading Company, discusses volatility, arbitrage and options trading with Tom Sosnoff and Tony Battista. The guys discuss volatility and its impact on Option Prices. Watch a REAL Financial Network, live everyday from 7am-3pm CT at https://tastytrade.com/tt/live

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An introduction into option pricing. Understanding how option pricing works and the components that determine an option price. For more information visit www.tradesmartu.com

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Views: 37421 Kevin Bracker

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How Volatillity Impacts Options Pricing by The Options Industry Council (OIC) For The Full Managing Volatillity Series click here https://goo.gl/0D5Bgv Implied volatility is a key part of every option position, and one that all investors should understand. In this 60 minute webinar, we analyze how implied volatility affects your position when the underlying stock soars, falls or goes sideways – and offer ideas for how you can use it to your advantage in the future. About the series: Learn how volatillity can impact your options positions

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Views: 5116 Brian Glueck

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https://h5bedi.github.io/DataAndCode/Code/Heston-Model
Views: 8727 Quant Education

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www.skyviewtrading.com Options are priced based on three elements of the underlying stock. 1. Time 2. Price 3. Volatility Watch this video to fully understand each of these three elements that make up option prices. Adam Thomas www.skyviewtrading.com what are options option pricing how to trade options option trading basics options explanation stock options

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The binomial solves for the price of an option by creating a riskless portfolio. For more financial risk videos, visit our website! http://www.bionicturtle.com
Views: 150487 Bionic Turtle

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Views: 12373 Finideas Sol

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ZACH DE GREGORIO, CPA www.WolvesAndFinance.com This video discusses the Black-Scholes Option Pricing Model. This math formula was first published in 1973 by Fischer Black and Myron Scholes. They received the Nobel Prize in 1997 for their work. This equation calculates out the value of the right to enter into a transaction. The math is complicated, but the concept is simple. It is based on the idea that the higher the risk, the higher the return. So the value of an option is based on the riskiness of the payout. If a payout is uncertain, you would be willing to pay less money. The way the Black-Scholes equation works is with five main variables: volatility, time, current price, exercise price, and risk free rate. Each variable has some level of risk associated with it which drives the value of the option. By entering in your assumptions, it calculates a value. Calculators are available online for this equation. This video shows an example with actual numbers. You can understand the variable sensitivity by creating a table. You can change the value of the current price while keeping the other variables the same. Neither Zach De Gregorio or Wolves and Finance Inc. shall be liable for any damages related to information in this video. It is recommended you contact a CPA in your area for business advice.
Views: 2250 WolvesAndFinance

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Full workshop available at www.quantshub.com Presenter: Roger Lord: Head of Quantitative Analytics, Cardano Within this workshop we will explore two topics that are important to the modern day pricing of derivatives - the Monte Carlo simulation of stochastic volatility models, as well as how to price options by using Fourier inversion techniques. The first part of the workshop will focus on techniques to efficiently simulate stochastic volatility models such as Heston, Schöbel-Zhu and SABR. Pitfalls of using too simple methods are shown, and lessons are learned from more sophisticated methods that are applicable in a wide variety of stochastic volatility models. The second part will be focussed on the usage of Fourier inversion techniques to price options. Since the characteristic function of many, typically affine, models can be expressed in closed-form, one can price vanilla options by means of Fourier inversion. We will show how to derive the characteristic function of such models, and focus on how to compute these efficiently by means of choosing an optimal contour, or via control variates. An overview of stochastic volatility models (e.g. Heston, Schöbel-Zhu, SABR) Pitfalls using Euler or higher-order schemes Leaking correlation Moment-matching schemes Derivation of characteristic function in affine models Option pricing using Fourier inversion Caveats using complex logarithms Choosing the optimal dampening coefficient Usage of control variates
Views: 2324 Quants Hub

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Views: 12608 Finideas Sol

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Views: 18603 projectoption

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Join us in the discussion on InformedTrades: http://www.informedtrades.com/1087607-black-scholes-n-d2-explained.html In this video, I give a general overview of the Black Scholes formula, and then break down N(d2) in detail. I cover most of the entire formula in this video. My goal is to describe Black Scholes in a simple, easy to understand way that has never been done before. Because this parts of the formula are somewhat complicated, I repeat parts several times during this video. See our other videos on Black Scholes: http://www.informedtrades.com/tags/black%20scholes/ Practice trading options with a free options trading demo account: http://bit.ly/apextrader

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This video shows how to calculate call and put option prices on excel, based on Black-Scholes Model.
Views: 10574 Mehmet Akgun

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A continuation of the Black-Scholes Option Pricing Model with the focus on the put option. Templates available at: tinyurl.com/Bracker-StNormTable tinyurl.com/Bracker-BSOPM tinyurl.com/Bracker-BSOPMSpread
Views: 33124 Kevin Bracker

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[my xls is here https://trtl.bz/2AruFiH] The binomial option pricing model needs: 1. A set of assumptions similar but not identical to those found in Black-Scholes; 2. A framework; i.e., risk-neutral valuation which allows us to infer the probability of an up-jump; 3. An assumption about asset dynamics, in this case that arithmetic returns are normally distributed; and 4. A valuation process which is two steps: FORWARD simulation produces terminal asset prices, then BACKWARD induction which returns the option price based on a series of discounted expected values. Discuss this video here in our FRM forum: https://trtl.bz/30qCfFL.
Views: 1448 Bionic Turtle

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Financial Markets (2011) (ECON 252) After introducing the core terms and main ideas of options in the beginning of the lecture, Professor Shiller emphasizes two purposes of options, a theoretical and a behavioral purpose. Subsequently, he provides a graphical representation for the value of a call and a put option, and, in this context, addresses the put-call parity for European options. Within the framework of the Binomial Asset Pricing model, he derives the value of a call-option from the no-arbitrage-principle, and, as a continuous-time analogue to this formula, he presents the Black-Scholes Option Pricing formula. He contrasts implied volatility, as represented by the VIX index of the Chicago Board Options Exchange, which uses a different formula in the spirit of Black-Scholes, with the actual S&P Composite volatility from 1986 until 2010. Professor Shiller concludes the lecture with some thoughts about options on single-family homes that he launched with his colleagues of the Chicago Mercantile Exchange in 2006. 00:00 - Chapter 1. Examples of Options Markets and Core Terms 07:11 - Chapter 2. Purposes of Option Contracts 17:11 - Chapter 3. Quoted Prices of Options and the Role of Derivatives Markets 24:54 - Chapter 4. Call and Put Options and the Put-Call Parity 34:56 - Chapter 5. Boundaries on the Price of a Call Option 39:07 - Chapter 6. Pricing Options with the Binomial Asset Pricing Model 51:02 - Chapter 7. The Black-Scholes Option Pricing Formula 55:49 - Chapter 8. Implied Volatility - The VIX Index in Comparison to Actual Market Volatility 01:09:33 - Chapter 9. The Potential for Options in the Housing Market Complete course materials are available at the Yale Online website: online.yale.edu This course was recorded in Spring 2011.
Views: 123917 YaleCourses

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