FEAR &GREED VOLATILITY MARKETS. Emanuel Derman. Quantitative Strategies Group Goldman Sachs & Co. Quantitative Strategies. Page 1 of 24. Fear&Greed.

Size: px
Start display at page:

Download "FEAR &GREED VOLATILITY MARKETS. Emanuel Derman. Quantitative Strategies Group Goldman Sachs & Co. Quantitative Strategies. Page 1 of 24. Fear&Greed."

Transcription

1 FEAR &GREED IN VOLATILITY MARKETS ~ Emanuel Derman Group Goldman Sachs & Co. Page 1 of 24 Fear&Greed.fm

2 Are There Patterns to Volatility Changes? Since 1987, global index options markets are persistently skewed. How do/should volatilities and the skew change as markets move? Every description of data involves an articulated or unarticulated model. There are at least three models for volatility change: An apocryphal Sticky-Strike Rule, that reflects Greed; An apocryphal Sticky-Delta Rule, that reflects Moderation; A theoretical Implied Tree Model, that reflects Fear. Each rule leads to different predictions for valuing & hedging options. Which works best? And why? Traders daily reports are sometimes unreliable. They focus on liquid at-the-money volatility, a moving target, but they own definite strikes. Therefore, ignore everyone and look at the data through the prism of models. Page 2 of 24 There appear to be several distinct periods ( regimes ) in which different rules seem to hold. Often, S&P 500 implied volatilities seems to oscillate between the Fear Rule and the Greed Rule... Producing Moderation in the long run, but not the short.

3 Contents 1. INTRODUCTION: GLOBAL IMPLIED VOLATILITIES 2. GREED (STICKY STRIKE) 3. MODERATION (STICKY DELTA) 4. FEAR (STICKY IMPLIED TREE) 5. WHAT REALLY HAPPENS: MODEL REGIMES Page 3 of 24

4 PART I PART I INTRODUCTION: GLOBAL INDEX IMPLIED VOLATILITIES INTRODUCTION: GLOBAL INDEX IMPLIED VOLATILITIES Page 4 of 24

5 A Persistent Negative Global Skew A persistent large skew, almost linear, and inconsistent with Black- Scholes. PART I INTRODUCTION: GLOBAL INDEX IMPLIED VOLATILITIES Global Three-Month Volatility Skews Mar D Put Atm 25D Call Nikkei 225 S&P 500 Hang Sendg g FTSE 100 DAX CAC 40 MIB 30 SMI AEX Page 5 of 24 Σ( K ) = Σ atm bk ( S 0 )

6 PART I INTRODUCTION: GLOBAL INDEX IMPLIED VOLATILITIES Page 6 of 24 A Negative Correlation with the Index The S&P 500 index and its at-the-money three-month implied volatility, Sep through Nov Three-Month Implied Volatilities of SPX Options INDEX ATM Note - you don t own at-the-money volatility, you own a fixed strike.

7 PART 50 I INTRODUCTION: 40 GLOBAL 35 INDEX IMPLIED 30 VOLATILITIES 25 Page 7 of 24 Volatility Behavior By Strike Is Complex Three Month Implied Volatilities of SPX Options What s going on here? INDEX ATM 750" 800" 850" 900" 950" 1000" 1050" 1100" 1150" 1200"

8 What s The Future Skew? We know the current skew Σ(K) = Σ atm b(k - S 0 ). Hypothetical Implied Volatility of Three-Month SPX Options Index Strike PART I INTRODUCTION: GLOBAL INDEX IMPLIED VOLATILITIES ? 18? ? 20? ? 22? Page 8 of 24 What will happen when the index moves? What s the S-dependence in Σ(S,K)? Distinguish carefully between Σ(S,K) and Σ atm (S) = Σ(S,S).

9 PART II PART II GREED (STICKY STRIKE) GREED (STICKY STRIKE) Page 9 of 24

10 Complacency or Greed: Sticky Strike Model The simplest & most convenient model for changing the implied volatility of an option as the index moves is not to change it at all. This is the or complacency model, or sticky strike, the closest thing to Black-Scholes. It s also the lazy-trader model. STICKY STRIKE Σ( S, K) Σ( K ) = Σ atm bk ( S 0 ) PART II GREED (STICKY STRIKE) Characteristics Fixed-strike volatility is independent of S. Therefore, because of the negative skew, at-the-money volatility falls with rising S. = BS. In a rising market, you can think of this model as representing Irrational Exuberance or Greed: At-the-money options are the most liquid. When the market rises, at-the-money volatility falls, and you are selling the most liquid options more and more cheaply, as though you need never worry about future index declines. Page 10 of 24

11 How Options Trees Evolve In The Sticky Strike Model Index Strike Known 90 PART II GREED (STICKY STRIKE) Fixed-strike volatility is independent of S. Therefore, because of the negative skew, at-the-money volatility falls with rising S. = BS. Page 11 of 24

12 PART III PART III MODERATION (STICKY DELTA) MODERATION (STICKY DELTA) Page 12 of 24

13 Rational Moderation At-the-money volatility is the rational estimate for the future cost of replicating liquid options issued now. On average, over the long run, at-the-money volatility should be independent of index level. If you have no special expectations about the future, you should keep at-the-money volatility unchanged. Given the negative skew, as the index rises, you need to raise every strike s volatility to keep at-the-money volatility unchanged. PART III Traders refer to this as the Sticky Moneyness or Sticky Delta Model. MODERATION (STICKY DELTA) STICKY DELTA : Σ = Σ( K S) = Σ atm bk ( S) Characteristics Atm vol is independent of S. Fixed-strike vol increases with S. > BS. Page 13 of 24

14 How Options Trees Evolve In The Sticky Delta Model. Index Strike Known PART III MODERATION (STICKY DELTA) Atm vol is independent of S. Fixed-strike vol increases with S. Page 14 of 24 > BS.

15 PART IV PART IV FEAR (STICKY IMPLIED TREE) FEAR (STICKY IMPLIED TREE) Page 15 of 24

16 Why The Skew? Fear of Index Declines! PART IV FEAR (STICKY IMPLIED TREE) The skew represent the premium for the fear of a downward market move and an increase in realized and implied volatility. Relation between the current skew and the expected future volatility. Strike Implied Volatility (%) % 99 21% 98 22% 97 23% You can deduce the local volatility at different market levels by treating the implied volatility as an average over local (future at-themoney) volatilities. Index Level Local volatility (%) % 99 22% 98 24% 97 26% Page 16 of 24 These local volatilities are the future at-the-money volatilities feared to occur in a decline. Note that local volatilities increase twice as fast with index changes as implieds increase with strike.

17 Sticky Implied Tree Extracts Local Volatilities There is one market-consistent tree - the implied tree - whose expectations of future volatilities match all current options prices and the skew. In this view, the skew is attributable to an expectation of higher volatility as the market moves (jumps?) down. You can use this tree to price all options consistently off future implied local volatilities. This is similar to pricing all off-the-run bonds off current forwards. PART IV FEAR (STICKY IMPLIED TREE) stock price variable local volatility σ(s,t) in the future time several different constant volatility trees are equivalent to one implied tree When the index moves, to find the new skew, you roll along the local vols. This is similar to rolling along the forward curve to get future yields as time passes. STICKY IMPLIED TREE: Σ( K, S) = Σ atm bk ( + S) Page 17 of 24

18 How Options Trees Evolve In The Sticky Implied Tree Model Strike Index Current Tree PART IV FEAR (STICKY IMPLIED TREE) Page 18 of 24 Fixed-strike volatility decreases as K or S increases. Atm vol falls twice as rapidly as skew. < BS.

19 PART V PART V MODEL SUMMARY MODEL SUMMARY Page 19 of 24

20 The Properties of the Models Stickiness Model Strike PART V MODEL SUMMARY Delta Implied tree Equation for Σ( S, K) Σ atm () t bt ()K ( S 0 ) Σ atm () t bt ()K ( S) Σ atm () t bt ()K ( + S) Fixed-strike Option Volatility independent of index level increases as index level increases decreases as index level increases Behavior of At-the-money Option Volatility decreases as index level increases independent of index level decreases twice as rapidly as index level increases Delta = BS > BS < BS Page 20 of 24

21 PART VI PART VI WHAT REALLY HAPPENS:MODEL REGIMES WHAT REALLY HAPPENS: MODEL REGIMES Page 21 of 24

22 Which Model Reigns in Which Regime? PART VI 60 WHAT REALLY 55 HAPPENS:MODEL REGIMES 50 Three-Month S&P 500 Implied Volatilities 45 40? /1/97 9/11/97 9/23/97 10/6/97 10/17/97 10/29/97 11/10/97 11/20/97 12/3/97 12/15/97 12/25/97 1/7/98 1/20/98 2/2/98 2/12/98 2/24/98 3/6/98 3/18/98 3/30/98 4/9/98 4/21/98 5/1/98 5/13/98 5/25/98 6/4/98 6/16/98 6/26/98 7/8/98 7/20/98 7/30/98 8/11/98 8/21/98 9/2/98 9/14/98 9/25/98 10/7/98 10/19/98 10/29/98 11/10/98 11/20/98 12/2/98 12/14/98 12/24/98 1/6/99 1/18/99 1/28/99 2/9/99 2/19/99 3/4/99 3/16/99 3/26/99 4/7/99 4/19/99 4/29/99 Page 22 of ATM S&P 500 Level sticky strike or sticky implied tree Fear Greed Correction Greed Fear Greed Correction jumpy index: sticky implied tree index trends; should be sticky delta, seems to be sticky strike CORRECTION vols rise to sticky delta level stable index trends; should be sticky delta, seems to be sticky strike jumpy index: sticky implied tree index trends; should be sticky delta, seems to be sticky strike CORRECTION vols rise to sitcky delta level sticky implied tree INDEX Volatility

23 Conclusions Sticky strike (complacency) Sticky delta (moderation) Sticky implied tree (fear) PART VI WHAT REALLY HAPPENS:MODEL REGIMES are intuitively useful ways of thinking about variations in implied volatility that sometimes correspond to modes of market behavior. When times are good, and the index keeps rising, the options market keeps every strike s volatility roughly fixed, and so the pendulum of at-the-money volatility drops. When times get bad, and the index jumps down a few percentage points, the market has to compensate for having let at-the-money volatility drop too far. The pendulum reverses, and moves at-themoney volatility up at twice the rate as the index collapses. On average, over the long haul, the pendulum oscillations between sticky-strike Greed and sticky-implied-tree Fear average out to sticky-delta Moderation. Will these conjectured regimes extend through time and across markets? Is there a model of stochastic volatility that encompasses this? Page 23 of 24

24 Recent Update: July-August 99 PART VI WHAT REALLY HAPPENS:MODEL REGIMES Page 24 of 24

25 Volatility /1/99 6/2/99 skew is about 4 vol pts per 100 S&P pts points 6/3/99 6/4/99 6/7/99 6/8/99 6/9/99 6/10/99 6/11/99 6/14/99 6/15/99 6/16/ Three-Month S&P Implied Volatilities Rising Index, Atm vol falls to 19% Falling index, Atm vol rises to 25% Rising index, Atm vol falls again pt rise in S&P S&P declines 140 pts S&P rises 80 pts vols by strike 25.4 rise about pts vols by strike remain vols by strike 33.26remain roughly unchanged again unchanged Atm vol rises twice 21.3as much, about 6 pts ATM vol again drops ATM vol 33.6drops about pts about 3 pts as index as index 33.73rises rises Date ATM INDEX /17/99 6/18/99 6/21/99 6/22/99 6/23/99 6/24/99 6/25/99 6/28/99 6/29/99 6/30/99 7/1/99 7/2/99 7/5/99 7/6/99 7/7/99 7/8/99 7/9/99 7/12/99 7/13/99 7/14/99 7/15/99 7/16/99 7/19/99 7/20/99 7/21/99 7/22/99 7/23/99 7/26/99 7/27/99 7/28/99 7/29/99 7/30/99 8/2/99 8/3/99 8/4/99 8/5/99 8/6/99 8/9/99 8/10/99 8/11/99 8/12/99 8/13/99 8/16/99 8/17/99 8/18/99 8/19/99 8/20/99 8/23/99 8/24/99 8/25/99 Index

Principal Component Analysis of the Volatility Smiles and Skews. Motivation

Principal Component Analysis of the Volatility Smiles and Skews. Motivation Principal Component Analysis of the Volatility Smiles and Skews Professor Carol Alexander Chair of Risk Management ISMA Centre University of Reading www.ismacentre.rdg.ac.uk 1 Motivation Implied volatilities

More information

1. What is Implied Volatility?

1. What is Implied Volatility? Numerical Methods FEQA MSc Lectures, Spring Term 2 Data Modelling Module Lecture 2 Implied Volatility Professor Carol Alexander Spring Term 2 1 1. What is Implied Volatility? Implied volatility is: the

More information

Quantitative Strategies Research Notes

Quantitative Strategies Research Notes Quantitative Strategies Research Notes December 1995 The Local Volatility Surface Unlocking the Information in Index Option Prices Emanuel Derman Iraj Kani Joseph Z. Zou Copyright 1995 Goldman, & Co. All

More information

Smile in the low moments

Smile in the low moments Smile in the low moments L. De Leo, T.-L. Dao, V. Vargas, S. Ciliberti, J.-P. Bouchaud 10 jan 2014 Outline 1 The Option Smile: statics A trading style The cumulant expansion A low-moment formula: the moneyness

More information

Developments in Volatility-Related Indicators & Benchmarks

Developments in Volatility-Related Indicators & Benchmarks Developments in Volatility-Related Indicators & Benchmarks William Speth, Global Head of Research Cboe Multi-Asset Solutions Team September 12, 18 Volatility-related indicators unlock valuable information

More information

FX Smile Modelling. 9 September September 9, 2008

FX Smile Modelling. 9 September September 9, 2008 FX Smile Modelling 9 September 008 September 9, 008 Contents 1 FX Implied Volatility 1 Interpolation.1 Parametrisation............................. Pure Interpolation.......................... Abstract

More information

VIX Hedging September 30, 2015 Pravit Chintawongvanich, Head of Risk Strategy

VIX Hedging September 30, 2015 Pravit Chintawongvanich, Head of Risk Strategy P R O V E N E X P E R T I S E. U N B I A S E D A D V I C E. F L E X I B L E S O L U T I O N S. VIX Hedging September 3, 215 Pravit Chintawongvanich, Head of Risk Strategy Hedging objectives What is the

More information

Hedging the Smirk. David S. Bates. University of Iowa and the National Bureau of Economic Research. October 31, 2005

Hedging the Smirk. David S. Bates. University of Iowa and the National Bureau of Economic Research. October 31, 2005 Hedging the Smirk David S. Bates University of Iowa and the National Bureau of Economic Research October 31, 2005 Associate Professor of Finance Department of Finance Henry B. Tippie College of Business

More information

Chapter 18 Volatility Smiles

Chapter 18 Volatility Smiles Chapter 18 Volatility Smiles Problem 18.1 When both tails of the stock price distribution are less heavy than those of the lognormal distribution, Black-Scholes will tend to produce relatively high prices

More information

Interpreting Volatility-Related Indicators & Benchmarks

Interpreting Volatility-Related Indicators & Benchmarks Interpreting Volatility-Related Indicators & Benchmarks William Speth, Head of Research Cboe Multi-Asset Solutions Team March 7, 18 Volatility-related indicators & benchmarks unlock valuable information

More information

Z. Wahab ENMG 625 Financial Eng g II 04/26/12. Volatility Smiles

Z. Wahab ENMG 625 Financial Eng g II 04/26/12. Volatility Smiles Z. Wahab ENMG 625 Financial Eng g II 04/26/12 Volatility Smiles The Problem with Volatility We cannot see volatility the same way we can see stock prices or interest rates. Since it is a meta-measure (a

More information

HEDGING AND ARBITRAGE WARRANTS UNDER SMILE EFFECTS: ANALYSIS AND EVIDENCE

HEDGING AND ARBITRAGE WARRANTS UNDER SMILE EFFECTS: ANALYSIS AND EVIDENCE HEDGING AND ARBITRAGE WARRANTS UNDER SMILE EFFECTS: ANALYSIS AND EVIDENCE SON-NAN CHEN Department of Banking, National Cheng Chi University, Taiwan, ROC AN-PIN CHEN and CAMUS CHANG Institute of Information

More information

Practical Hedging: From Theory to Practice. OSU Financial Mathematics Seminar May 5, 2008

Practical Hedging: From Theory to Practice. OSU Financial Mathematics Seminar May 5, 2008 Practical Hedging: From Theory to Practice OSU Financial Mathematics Seminar May 5, 008 Background Dynamic replication is a risk management technique used to mitigate market risk We hope to spend a certain

More information

Advanced Corporate Finance. 5. Options (a refresher)

Advanced Corporate Finance. 5. Options (a refresher) Advanced Corporate Finance 5. Options (a refresher) Objectives of the session 1. Define options (calls and puts) 2. Analyze terminal payoff 3. Define basic strategies 4. Binomial option pricing model 5.

More information

Factors in Implied Volatility Skew in Corn Futures Options

Factors in Implied Volatility Skew in Corn Futures Options 1 Factors in Implied Volatility Skew in Corn Futures Options Weiyu Guo* University of Nebraska Omaha 6001 Dodge Street, Omaha, NE 68182 Phone 402-554-2655 Email: wguo@unomaha.edu and Tie Su University

More information

OPTION VALUATION Fall 2000

OPTION VALUATION Fall 2000 OPTION VALUATION Fall 2000 2 Essentially there are two models for pricing options a. Black Scholes Model b. Binomial option Pricing Model For equities, usual model is Black Scholes. For most bond options

More information

DELTA HEDGING VEGA RISK?

DELTA HEDGING VEGA RISK? DELTA HEDGING VEGA RISK? Stéphane CRÉPEY, Évry University stephane.crepey@univ-evry.fr QMF Conference, Sydney, December 17 2004 Figure 1: The Volatility Smile (E. Derman) Contents 1 Basics of the smile

More information

Modeling Capital Market with Financial Signal Processing

Modeling Capital Market with Financial Signal Processing Modeling Capital Market with Financial Signal Processing Jenher Jeng Ph.D., Statistics, U.C. Berkeley Founder & CTO of Harmonic Financial Engineering, www.harmonicfinance.com Outline Theory and Techniques

More information

Quantitative Strategies Research Notes

Quantitative Strategies Research Notes Quantitative Strategies Research Notes January 1994 The Volatility Smile and Its Implied Tree Emanuel Derman Iraj Kani Copyright 1994 Goldman, & Co. All rights reserved. This material is for your private

More information

The Black-Scholes Model

The Black-Scholes Model IEOR E4706: Foundations of Financial Engineering c 2016 by Martin Haugh The Black-Scholes Model In these notes we will use Itô s Lemma and a replicating argument to derive the famous Black-Scholes formula

More information

Copyright Emanuel Derman 2008

Copyright Emanuel Derman 2008 E4718 Spring 2008: Derman: Lecture 6: Extending Black-Scholes; Local Volatility Models Page 1 of 34 Lecture 6: Extending Black-Scholes; Local Volatility Models Summary of the course so far: Black-Scholes

More information

Chapter 15: Jump Processes and Incomplete Markets. 1 Jumps as One Explanation of Incomplete Markets

Chapter 15: Jump Processes and Incomplete Markets. 1 Jumps as One Explanation of Incomplete Markets Chapter 5: Jump Processes and Incomplete Markets Jumps as One Explanation of Incomplete Markets It is easy to argue that Brownian motion paths cannot model actual stock price movements properly in reality,

More information

The Black-Scholes Model

The Black-Scholes Model The Black-Scholes Model Liuren Wu Options Markets (Hull chapter: 12, 13, 14) Liuren Wu ( c ) The Black-Scholes Model colorhmoptions Markets 1 / 17 The Black-Scholes-Merton (BSM) model Black and Scholes

More information

The Black-Scholes Model

The Black-Scholes Model The Black-Scholes Model Liuren Wu Options Markets Liuren Wu ( c ) The Black-Merton-Scholes Model colorhmoptions Markets 1 / 18 The Black-Merton-Scholes-Merton (BMS) model Black and Scholes (1973) and Merton

More information

No-Arbitrage Conditions for the Dynamics of Smiles

No-Arbitrage Conditions for the Dynamics of Smiles No-Arbitrage Conditions for the Dynamics of Smiles Presentation at King s College Riccardo Rebonato QUARC Royal Bank of Scotland Group Research in collaboration with Mark Joshi Thanks to David Samuel The

More information

Any asset that derives its value from another underlying asset is called a derivative asset. The underlying asset could be any asset - for example, a

Any asset that derives its value from another underlying asset is called a derivative asset. The underlying asset could be any asset - for example, a Options Week 7 What is a derivative asset? Any asset that derives its value from another underlying asset is called a derivative asset. The underlying asset could be any asset - for example, a stock, bond,

More information

P&L Attribution and Risk Management

P&L Attribution and Risk Management P&L Attribution and Risk Management Liuren Wu Options Markets (Hull chapter: 15, Greek letters) Liuren Wu ( c ) P& Attribution and Risk Management Options Markets 1 / 19 Outline 1 P&L attribution via the

More information

Hedging Default Risks of CDOs in Markovian Contagion Models

Hedging Default Risks of CDOs in Markovian Contagion Models Hedging Default Risks of CDOs in Markovian Contagion Models Second Princeton Credit Risk Conference 24 May 28 Jean-Paul LAURENT ISFA Actuarial School, University of Lyon, http://laurent.jeanpaul.free.fr

More information

Real-World Quantitative Finance

Real-World Quantitative Finance Sachs Real-World Quantitative Finance (A Poor Man s Guide To What Physicists Do On Wall St.) Emanuel Derman Goldman, Sachs & Co. March 21, 2002 Page 1 of 16 Sachs Introduction Models in Physics Models

More information

Bachelier Finance Society, Fifth World Congress London 19 July 2008

Bachelier Finance Society, Fifth World Congress London 19 July 2008 Hedging CDOs in in Markovian contagion models Bachelier Finance Society, Fifth World Congress London 19 July 2008 Jean-Paul LAURENT Professor, ISFA Actuarial School, University of Lyon & scientific consultant

More information

Portfolio Management Using Option Data

Portfolio Management Using Option Data Portfolio Management Using Option Data Peter Christoffersen Rotman School of Management, University of Toronto, Copenhagen Business School, and CREATES, University of Aarhus 2 nd Lecture on Friday 1 Overview

More information

MATH4143: Scientific Computations for Finance Applications Final exam Time: 9:00 am - 12:00 noon, April 18, Student Name (print):

MATH4143: Scientific Computations for Finance Applications Final exam Time: 9:00 am - 12:00 noon, April 18, Student Name (print): MATH4143 Page 1 of 17 Winter 2007 MATH4143: Scientific Computations for Finance Applications Final exam Time: 9:00 am - 12:00 noon, April 18, 2007 Student Name (print): Student Signature: Student ID: Question

More information

INSTITUTE OF ACTUARIES OF INDIA

INSTITUTE OF ACTUARIES OF INDIA INSTITUTE OF ACTUARIES OF INDIA EXAMINATIONS 24 th March 2017 Subject ST6 Finance and Investment B Time allowed: Three Hours (10.15* 13.30 Hours) Total Marks: 100 INSTRUCTIONS TO THE CANDIDATES 1. Please

More information

Pricing Options on Dividend paying stocks, FOREX, Futures, Consumption Commodities

Pricing Options on Dividend paying stocks, FOREX, Futures, Consumption Commodities Pricing Options on Dividend paying stocks, FOREX, Futures, Consumption Commodities The Black-Scoles Model The Binomial Model and Pricing American Options Pricing European Options on dividend paying stocks

More information

Lecture 9: Practicalities in Using Black-Scholes. Sunday, September 23, 12

Lecture 9: Practicalities in Using Black-Scholes. Sunday, September 23, 12 Lecture 9: Practicalities in Using Black-Scholes Major Complaints Most stocks and FX products don t have log-normal distribution Typically fat-tailed distributions are observed Constant volatility assumed,

More information

Employee Reload Options: Pricing, Hedging, and Optimal Exercise

Employee Reload Options: Pricing, Hedging, and Optimal Exercise Employee Reload Options: Pricing, Hedging, and Optimal Exercise Philip H. Dybvig Washington University in Saint Louis Mark Loewenstein Boston University for a presentation at Cambridge, March, 2003 Abstract

More information

non linear Payoffs Markus K. Brunnermeier

non linear Payoffs Markus K. Brunnermeier Institutional Finance Lecture 10: Dynamic Arbitrage to Replicate non linear Payoffs Markus K. Brunnermeier Preceptor: Dong Beom Choi Princeton University 1 BINOMIAL OPTION PRICING Consider a European call

More information

Empirical Option Pricing

Empirical Option Pricing Empirical Option Pricing Holes in Black& Scholes Overpricing Price pressures in derivatives and underlying Estimating volatility and VAR Put-Call Parity Arguments Put-call parity p +S 0 e -dt = c +EX e

More information

Implied Volatility Surface

Implied Volatility Surface White Paper Implied Volatility Surface By Amir Akhundzadeh, James Porter, Eric Schneider Originally published 19-Aug-2015. Updated 24-Jan-2017. White Paper Implied Volatility Surface Contents Introduction...

More information

Chapter 14. Exotic Options: I. Question Question Question Question The geometric averages for stocks will always be lower.

Chapter 14. Exotic Options: I. Question Question Question Question The geometric averages for stocks will always be lower. Chapter 14 Exotic Options: I Question 14.1 The geometric averages for stocks will always be lower. Question 14.2 The arithmetic average is 5 (three 5s, one 4, and one 6) and the geometric average is (5

More information

The objective of Part One is to provide a knowledge base for learning about the key

The objective of Part One is to provide a knowledge base for learning about the key PART ONE Key Option Elements The objective of Part One is to provide a knowledge base for learning about the key elements of forex options. This includes a description of plain vanilla options and how

More information

Volatility Surface. Course Name: Analytical Finance I. Report date: Oct.18,2012. Supervisor:Jan R.M Röman. Authors: Wenqing Huang.

Volatility Surface. Course Name: Analytical Finance I. Report date: Oct.18,2012. Supervisor:Jan R.M Röman. Authors: Wenqing Huang. Course Name: Analytical Finance I Report date: Oct.18,2012 Supervisor:Jan R.M Röman Volatility Surface Authors: Wenqing Huang Zhiwen Zhang Yiqing Wang 1 Content 1. Implied Volatility...3 2.Volatility Smile...

More information

Learn To Trade Stock Options

Learn To Trade Stock Options Learn To Trade Stock Options Written by: Jason Ramus www.daytradingfearless.com Copyright: 2017 Table of contents: WHAT TO EXPECT FROM THIS MANUAL WHAT IS AN OPTION BASICS OF HOW AN OPTION WORKS RECOMMENDED

More information

MATH6911: Numerical Methods in Finance. Final exam Time: 2:00pm - 5:00pm, April 11, Student Name (print): Student Signature: Student ID:

MATH6911: Numerical Methods in Finance. Final exam Time: 2:00pm - 5:00pm, April 11, Student Name (print): Student Signature: Student ID: MATH6911 Page 1 of 16 Winter 2007 MATH6911: Numerical Methods in Finance Final exam Time: 2:00pm - 5:00pm, April 11, 2007 Student Name (print): Student Signature: Student ID: Question Full Mark Mark 1

More information

The Information Content of Implied Volatility Skew: Evidence on Taiwan Stock Index Options

The Information Content of Implied Volatility Skew: Evidence on Taiwan Stock Index Options Data Science and Pattern Recognition c 2017 ISSN 2520-4165 Ubiquitous International Volume 1, Number 1, February 2017 The Information Content of Implied Volatility Skew: Evidence on Taiwan Stock Index

More information

Volatility as a Tradable Asset: Using the VIX as a market signal, diversifier and for return enhancement

Volatility as a Tradable Asset: Using the VIX as a market signal, diversifier and for return enhancement Volatility as a Tradable Asset: Using the VIX as a market signal, diversifier and for return enhancement Joanne Hill Sandy Rattray Equity Product Strategy Goldman, Sachs & Co. March 25, 2004 VIX as a timing

More information

IEOR E4718 Topics in Derivatives Pricing: An Introduction to the Volatility Smile

IEOR E4718 Topics in Derivatives Pricing: An Introduction to the Volatility Smile Aim of the Course IEOR E4718 Topics in Derivatives Pricing: An Introduction to the Volatility Smile Emanuel Derman January 2009 This isn t a course about mathematics, calculus, differential equations or

More information

Hedging Credit Derivatives in Intensity Based Models

Hedging Credit Derivatives in Intensity Based Models Hedging Credit Derivatives in Intensity Based Models PETER CARR Head of Quantitative Financial Research, Bloomberg LP, New York Director of the Masters Program in Math Finance, Courant Institute, NYU Stanford

More information

Trading Volatility: Theory and Practice. FPA of Illinois. Conference for Advanced Planning October 7, Presented by: Eric Metz, CFA

Trading Volatility: Theory and Practice. FPA of Illinois. Conference for Advanced Planning October 7, Presented by: Eric Metz, CFA Trading Volatility: Theory and Practice Presented by: Eric Metz, CFA FPA of Illinois Conference for Advanced Planning October 7, 2014 Trading Volatility: Theory and Practice Institutional Use Only 1 Table

More information

ECON FINANCIAL ECONOMICS

ECON FINANCIAL ECONOMICS ECON 337901 FINANCIAL ECONOMICS Peter Ireland Boston College Fall 2017 These lecture notes by Peter Ireland are licensed under a Creative Commons Attribution-NonCommerical-ShareAlike 4.0 International

More information

Validation of Nasdaq Clearing Models

Validation of Nasdaq Clearing Models Model Validation Validation of Nasdaq Clearing Models Summary of findings swissquant Group Kuttelgasse 7 CH-8001 Zürich Classification: Public Distribution: swissquant Group, Nasdaq Clearing October 20,

More information

SOCIETY OF ACTUARIES Quantitative Finance and Investment Advanced Exam Exam QFIADV AFTERNOON SESSION

SOCIETY OF ACTUARIES Quantitative Finance and Investment Advanced Exam Exam QFIADV AFTERNOON SESSION SOCIETY OF ACTUARIES Exam QFIADV AFTERNOON SESSION Date: Friday, May 2, 2014 Time: 1:30 p.m. 3:45 p.m. INSTRUCTIONS TO CANDIDATES General Instructions 1. This afternoon session consists of 6 questions

More information

A SUMMARY OF OUR APPROACHES TO THE SABR MODEL

A SUMMARY OF OUR APPROACHES TO THE SABR MODEL Contents 1 The need for a stochastic volatility model 1 2 Building the model 2 3 Calibrating the model 2 4 SABR in the risk process 5 A SUMMARY OF OUR APPROACHES TO THE SABR MODEL Financial Modelling Agency

More information

Chapter 14 Exotic Options: I

Chapter 14 Exotic Options: I Chapter 14 Exotic Options: I Question 14.1. The geometric averages for stocks will always be lower. Question 14.2. The arithmetic average is 5 (three 5 s, one 4, and one 6) and the geometric average is

More information

e.g. + 1 vol move in the 30delta Puts would be example of just a changing put skew

e.g. + 1 vol move in the 30delta Puts would be example of just a changing put skew Calculating vol skew change risk (skew-vega) Ravi Jain 2012 Introduction An interesting and important risk in an options portfolio is the impact of a changing implied volatility skew. It is not uncommon

More information

F1 Results. News vs. no-news

F1 Results. News vs. no-news F1 Results News vs. no-news With news visible, the median trading profits were about $130,000 (485 player-sessions) With the news screen turned off, median trading profits were about $165,000 (283 player-sessions)

More information

* Professor of Finance Stern School of Business New York University.

* Professor of Finance Stern School of Business New York University. * Professor of Finance Stern School of Business New York University email: sfiglews@stern.nyu.edu An American Call on a Non-Dividend Paying Stock Should never be exercised early Is therefore worth the

More information

MANAGING OPTIONS POSITIONS MARCH 2013

MANAGING OPTIONS POSITIONS MARCH 2013 MANAGING OPTIONS POSITIONS MARCH 2013 AGENDA INTRODUCTION OPTION VALUATION & RISK MEASURES THE GREEKS PRE-TRADE RICH VS. CHEAP ANALYSIS SELECTING TERM STRUCTURE PORTFOLIO CONSTRUCTION CONDITIONAL RISK

More information

Derivatives Questions Question 1 Explain carefully the difference between hedging, speculation, and arbitrage.

Derivatives Questions Question 1 Explain carefully the difference between hedging, speculation, and arbitrage. Derivatives Questions Question 1 Explain carefully the difference between hedging, speculation, and arbitrage. Question 2 What is the difference between entering into a long forward contract when the forward

More information

Lecture 4: Barrier Options

Lecture 4: Barrier Options Lecture 4: Barrier Options Jim Gatheral, Merrill Lynch Case Studies in Financial Modelling Course Notes, Courant Institute of Mathematical Sciences, Fall Term, 2001 I am grateful to Peter Friz for carefully

More information

Mispriced Index Option Portfolios George Constantinides University of Chicago

Mispriced Index Option Portfolios George Constantinides University of Chicago George Constantinides University of Chicago (with Michal Czerwonko and Stylianos Perrakis) We consider 2 generic traders: Introduction the Index Trader (IT) holds the S&P 500 index and T-bills and maximizes

More information

Lecture 3: Asymptotics and Dynamics of the Volatility Skew

Lecture 3: Asymptotics and Dynamics of the Volatility Skew Lecture 3: Asymptotics and Dynamics of the Volatility Skew Jim Gatheral, Merrill Lynch Case Studies in Financial Modelling Course Notes, Courant Institute of Mathematical Sciences, Fall Term, 2001 I am

More information

VIX Fear of What? October 13, Research Note. Summary. Introduction

VIX Fear of What? October 13, Research Note. Summary. Introduction Research Note October 13, 2016 VIX Fear of What? by David J. Hait Summary The widely touted fear gauge is less about what might happen, and more about what already has happened. The VIX, while promoted

More information

The early bird gets the worm. (Benjamin Franklin)

The early bird gets the worm. (Benjamin Franklin) Plain Vanilla SPX-Options Hedging: The Effect of Smile-Adjustments and the Lark versus Owl Question. Chrilly Donninger Chief Scientist, Sibyl-Project Sibyl-Working-Paper, Feb. 2014 Rev. 1, 2014.02.26 http://www.godotfinance.com/

More information

M. Günhan Ertosun, Sarves Verma, Wei Wang

M. Günhan Ertosun, Sarves Verma, Wei Wang MSE 444 Final Presentation M. Günhan Ertosun, Sarves Verma, Wei Wang Advisors: Prof. Kay Giesecke, Benjamin Ambruster Four Different Ways to model : Using a Deterministic Volatility Function (DVF) used

More information

Modeling the Implied Volatility Surface. Jim Gatheral Global Derivatives and Risk Management 2003 Barcelona May 22, 2003

Modeling the Implied Volatility Surface. Jim Gatheral Global Derivatives and Risk Management 2003 Barcelona May 22, 2003 Modeling the Implied Volatility Surface Jim Gatheral Global Derivatives and Risk Management 2003 Barcelona May 22, 2003 This presentation represents only the personal opinions of the author and not those

More information

Applying the Principles of Quantitative Finance to the Construction of Model-Free Volatility Indices

Applying the Principles of Quantitative Finance to the Construction of Model-Free Volatility Indices Applying the Principles of Quantitative Finance to the Construction of Model-Free Volatility Indices Christopher Ting http://www.mysmu.edu/faculty/christophert/ Christopher Ting : christopherting@smu.edu.sg

More information

Pricing of Stock Options using Black-Scholes, Black s and Binomial Option Pricing Models. Felcy R Coelho 1 and Y V Reddy 2

Pricing of Stock Options using Black-Scholes, Black s and Binomial Option Pricing Models. Felcy R Coelho 1 and Y V Reddy 2 MANAGEMENT TODAY -for a better tomorrow An International Journal of Management Studies home page: www.mgmt2day.griet.ac.in Vol.8, No.1, January-March 2018 Pricing of Stock Options using Black-Scholes,

More information

CONSTRUCTION OF VOLATILITY INDICES USING A MULTINOMIAL TREE APPROXIMATION METHOD

CONSTRUCTION OF VOLATILITY INDICES USING A MULTINOMIAL TREE APPROXIMATION METHOD CONSTRUCTION OF VOLATILITY INDICES USING A MULTINOMIAL TREE APPROXIMATION METHOD Dragos Bozdog, Ionuţ Florescu, Khaldoun Khashanah, and Hongwei Qiu Dept. of Mathematical Sciences, Stevens Institute of

More information

A Poor Man s Guide. Quantitative Finance

A Poor Man s Guide. Quantitative Finance Sachs A Poor Man s Guide To Quantitative Finance Emanuel Derman October 2002 Email: emanuel@ederman.com Web: www.ederman.com PoorMansGuideToQF.fm September 30, 2002 Page 1 of 17 Sachs Summary Quantitative

More information

Synthetic options. Synthetic options consists in trading a varying position in underlying asset (or

Synthetic options. Synthetic options consists in trading a varying position in underlying asset (or Synthetic options Synthetic options consists in trading a varying position in underlying asset (or utures on the underlying asset 1 ) to replicate the payo proile o a desired option. In practice, traders

More information

FIN FINANCIAL INSTRUMENTS SPRING 2008

FIN FINANCIAL INSTRUMENTS SPRING 2008 FIN-40008 FINANCIAL INSTRUMENTS SPRING 2008 The Greeks Introduction We have studied how to price an option using the Black-Scholes formula. Now we wish to consider how the option price changes, either

More information

Derivative Securities

Derivative Securities Derivative Securities he Black-Scholes formula and its applications. his Section deduces the Black- Scholes formula for a European call or put, as a consequence of risk-neutral valuation in the continuous

More information

Trading Volatility in Commodities. Péter Dobránszky. 25 th March 2009, New York

Trading Volatility in Commodities. Péter Dobránszky. 25 th March 2009, New York Trading Volatility in Commodities Péter Dobránszky 25 th March 2009, New York Introduction Assessing which commodity trading models thrive in volatility Getting new focus because of the underperformance

More information

Implied Volatility Surface

Implied Volatility Surface Implied Volatility Surface Liuren Wu Zicklin School of Business, Baruch College Options Markets (Hull chapter: 16) Liuren Wu Implied Volatility Surface Options Markets 1 / 1 Implied volatility Recall the

More information

Managing the Newest Derivatives Risks

Managing the Newest Derivatives Risks Managing the Newest Derivatives Risks Michel Crouhy IXIS Corporate and Investment Bank / A subsidiary of NATIXIS Derivatives 2007: New Ideas, New Instruments, New markets NYU Stern School of Business,

More information

Europe warms to weekly options

Europe warms to weekly options Europe warms to weekly options After their introduction in the US more than a decade ago, weekly options have now become part of the investment toolkit of many financial professionals worldwide. Volume

More information

2 f. f t S 2. Delta measures the sensitivityof the portfolio value to changes in the price of the underlying

2 f. f t S 2. Delta measures the sensitivityof the portfolio value to changes in the price of the underlying Sensitivity analysis Simulating the Greeks Meet the Greeks he value of a derivative on a single underlying asset depends upon the current asset price S and its volatility Σ, the risk-free interest rate

More information

Predicting the Market

Predicting the Market Predicting the Market April 28, 2012 Annual Conference on General Equilibrium and its Applications Steve Ross Franco Modigliani Professor of Financial Economics MIT The Importance of Forecasting Equity

More information

The Interaction of Implied Equity Volatility, Stochastic Interest, and Volatility Control Funds for Modeling Variable Products.

The Interaction of Implied Equity Volatility, Stochastic Interest, and Volatility Control Funds for Modeling Variable Products. Equity-Based Insurance Guarantees Conference Nov. 5-6, 2018 Chicago, IL The Interaction of Implied Equity Volatility, Stochastic Interest, and Volatility Control Funds for Modeling Variable Products Mark

More information

Corporate Finance, Module 21: Option Valuation. Practice Problems. (The attached PDF file has better formatting.) Updated: July 7, 2005

Corporate Finance, Module 21: Option Valuation. Practice Problems. (The attached PDF file has better formatting.) Updated: July 7, 2005 Corporate Finance, Module 21: Option Valuation Practice Problems (The attached PDF file has better formatting.) Updated: July 7, 2005 {This posting has more information than is needed for the corporate

More information

The Greek Letters Based on Options, Futures, and Other Derivatives, 8th Edition, Copyright John C. Hull 2012

The Greek Letters Based on Options, Futures, and Other Derivatives, 8th Edition, Copyright John C. Hull 2012 The Greek Letters Based on Options, Futures, and Other Derivatives, 8th Edition, Copyright John C. Hull 2012 Introduction Each of the Greek letters measures a different dimension to the risk in an option

More information

Pricing with a Smile. Bruno Dupire. Bloomberg

Pricing with a Smile. Bruno Dupire. Bloomberg CP-Bruno Dupire.qxd 10/08/04 6:38 PM Page 1 11 Pricing with a Smile Bruno Dupire Bloomberg The Black Scholes model (see Black and Scholes, 1973) gives options prices as a function of volatility. If an

More information

Appendix: Basics of Options and Option Pricing Option Payoffs

Appendix: Basics of Options and Option Pricing Option Payoffs Appendix: Basics of Options and Option Pricing An option provides the holder with the right to buy or sell a specified quantity of an underlying asset at a fixed price (called a strike price or an exercise

More information

Leverage Effect, Volatility Feedback, and Self-Exciting MarketAFA, Disruptions 1/7/ / 14

Leverage Effect, Volatility Feedback, and Self-Exciting MarketAFA, Disruptions 1/7/ / 14 Leverage Effect, Volatility Feedback, and Self-Exciting Market Disruptions Liuren Wu, Baruch College Joint work with Peter Carr, New York University The American Finance Association meetings January 7,

More information

Volatility By A.V. Vedpuriswar

Volatility By A.V. Vedpuriswar Volatility By A.V. Vedpuriswar June 21, 2018 Basics of volatility Volatility is the key parameter in modeling market risk. Volatility is the standard deviation of daily portfolio returns. 1 Estimating

More information

Volatility surfaces, stress testing and OCC portfolio margin. Ravi K. Jain. Volatility surfaces and stress testing

Volatility surfaces, stress testing and OCC portfolio margin. Ravi K. Jain. Volatility surfaces and stress testing Volatility surfaces, stress testing and OCC portfolio margin Ravi K. Jain Volatility surfaces and stress testing Calculating the current implied volatility of an option or the entire options chain of listed

More information

Stochastic Models. Introduction to Derivatives. Walt Pohl. April 10, Department of Business Administration

Stochastic Models. Introduction to Derivatives. Walt Pohl. April 10, Department of Business Administration Stochastic Models Introduction to Derivatives Walt Pohl Universität Zürich Department of Business Administration April 10, 2013 Decision Making, The Easy Case There is one case where deciding between two

More information

LECTURE 12. Volatility is the question on the B/S which assumes constant SD throughout the exercise period - The time series of implied volatility

LECTURE 12. Volatility is the question on the B/S which assumes constant SD throughout the exercise period - The time series of implied volatility LECTURE 12 Review Options C = S e -δt N (d1) X e it N (d2) P = X e it (1- N (d2)) S e -δt (1 - N (d1)) Volatility is the question on the B/S which assumes constant SD throughout the exercise period - The

More information

Fixed Income and Risk Management

Fixed Income and Risk Management Fixed Income and Risk Management Fall 2003, Term 2 Michael W. Brandt, 2003 All rights reserved without exception Agenda and key issues Pricing with binomial trees Replication Risk-neutral pricing Interest

More information

A Lower Bound for Calls on Quadratic Variation

A Lower Bound for Calls on Quadratic Variation A Lower Bound for Calls on Quadratic Variation PETER CARR Head of Quantitative Financial Research, Bloomberg LP, New York Director of the Masters Program in Math Finance, Courant Institute, NYU Chicago,

More information

OPTIONS CALCULATOR QUICK GUIDE

OPTIONS CALCULATOR QUICK GUIDE OPTIONS CALCULATOR QUICK GUIDE Table of Contents Introduction 3 Valuing options 4 Examples 6 Valuing an American style non-dividend paying stock option 6 Valuing an American style dividend paying stock

More information

Appendix A Financial Calculations

Appendix A Financial Calculations Derivatives Demystified: A Step-by-Step Guide to Forwards, Futures, Swaps and Options, Second Edition By Andrew M. Chisholm 010 John Wiley & Sons, Ltd. Appendix A Financial Calculations TIME VALUE OF MONEY

More information

Dynamic Relative Valuation

Dynamic Relative Valuation Dynamic Relative Valuation Liuren Wu, Baruch College Joint work with Peter Carr from Morgan Stanley October 15, 2013 Liuren Wu (Baruch) Dynamic Relative Valuation 10/15/2013 1 / 20 The standard approach

More information

Consistently Modeling Joint Dynamics of Volatility and Underlying To Enable Effective Hedging

Consistently Modeling Joint Dynamics of Volatility and Underlying To Enable Effective Hedging Consistently Modeling Joint Dynamics of Volatility and Underlying To Enable Effective Hedging Artur Sepp Bank of America Merrill Lynch, London artur.sepp@baml.com Global Derivatives Trading & Risk Management

More information

Simple Robust Hedging with Nearby Contracts

Simple Robust Hedging with Nearby Contracts Simple Robust Hedging with Nearby Contracts Liuren Wu and Jingyi Zhu Baruch College and University of Utah April 29, 211 Fourth Annual Triple Crown Conference Liuren Wu (Baruch) Robust Hedging with Nearby

More information

Gas storage: overview and static valuation

Gas storage: overview and static valuation In this first article of the new gas storage segment of the Masterclass series, John Breslin, Les Clewlow, Tobias Elbert, Calvin Kwok and Chris Strickland provide an illustration of how the four most common

More information

Lecture 18. More on option pricing. Lecture 18 1 / 21

Lecture 18. More on option pricing. Lecture 18 1 / 21 Lecture 18 More on option pricing Lecture 18 1 / 21 Introduction In this lecture we will see more applications of option pricing theory. Lecture 18 2 / 21 Greeks (1) The price f of a derivative depends

More information

Lecture 11: Stochastic Volatility Models Cont.

Lecture 11: Stochastic Volatility Models Cont. E4718 Spring 008: Derman: Lecture 11:Stochastic Volatility Models Cont. Page 1 of 8 Lecture 11: Stochastic Volatility Models Cont. E4718 Spring 008: Derman: Lecture 11:Stochastic Volatility Models Cont.

More information

Risk and Portfolio Management Spring Equity Options: Risk and Portfolio Management

Risk and Portfolio Management Spring Equity Options: Risk and Portfolio Management Risk and Portfolio Management Spring 2010 Equity Options: Risk and Portfolio Management Summary Review of equity options Risk-management of options on a single underlying asset Full pricing versus Greeks

More information