The determination methodology for Futures Spread Margins

Size: px
Start display at page:

Download "The determination methodology for Futures Spread Margins"

Transcription

1 The determination methodology for Futures Spread Margins RM Office Version.0

2 Index Introduction... 3 Definition and aim of the Futures Spread Margins Calculation methodology... 4 Page di 6

3 Introduction This document describes the methodology used for the determination of Futures Spread Margins. First of all this kind of margins is defined, describing the aims and the objectives of their existence (point. Then the document describes in detail the calculation methodology applied by the CC&G to determine the amount of the Future Spread Margin (point 3. Definition and aim of the Futures Spread Margins The aim of Futures Spread Margins is to guarantee Futures positions having opposite sign on different maturities (Futures Spread position considering the lower risk level expressed by the interest rate variations: these margins are applied on Futures Spread positions of the same Class. These margins are only computed for Futures positions having opposite sign on different maturities and are equal to the number of Futures Spread positions multiplied for the Future Spread Margin fixed by the CC&G. The Class is the set of contracts of the same kind having the same underlying asset (for example Futures on ENI for the Class of Futures or Options on Fiat shares for the class of options. The number of Futures Spread positions for each Class is equal to Min (Σ long positions; Σ short positions. Page 3 di 6

4 3 Calculation methodology The Futures Spread Margin is determined by the CC&G taking into account that the aim of the Futures Spread Margin is to cover the market risk of two futures position having opposite sign and different maturities. Accordingly the aim is to determine the greatest daily variation, reasonably possible, between the difference of Futures prices F and F (calendar spread having different maturities, and the same difference (calendar spread on the following day. Using the previous notation, the value of a calendar spread on a stock that does not ρ pay dividends is: t ρ t SPR = F F = Se Se, where S is the price of the underlying, ρ e ρ are the interest rates applied on the second and the first maturity and t and t represent the time to maturity. To consider explicitly the case of a calendar spread on a stock that pays dividends between the first and the second maturity, the following notation should be used: SPR = F F = ρdtd ρ ( t ρ t S De e Se expected dividend within the two maturities 3. d, where De ρ dt represents the present value of the Nevertheless this generalization is useless, because the expected dividend affects the level of the calendar spread but it does not have any influence on its variations between two days; so the application of the previous expression can be generalized with a calendar spread without dividends. SPR If the first day A the calendar spread is equal to ρ ( t ρ e e ρt ρt t A = Se Se = S, it can be assumed that the following day B the spread becomes equal to SPR = ( S ± S B e 365 ( ρ ± ρ t ( ρ ± ρ e t 365. So it has been assumed, during a day, a variation of the underlying of ± S, a variation of the interest rates of ± ρ and ± ρ ; moreover a day from the time to maturity has been subtracted. 3 When there are two or more dividends and some of these dividends are distributed before the first maturity and the others between the first and the second maturity we get: STR n ρd itd i ρt = F F = S Die e S i= m i= D e i ρ t d i d i e ρ t Page 4 di 6

5 SPR = SPR Formalizing, the variation of the calendar spread SPR is: B SPR A = ( S ± S e ( ρ ± ρ t ( ρ ± ρ 365 e t 365 S ρ ( t ρ t e e To obtain the largest SPR, some assumptions should be made on ± S, ± ρ and ± ρ. This procedure is repeated for every couple of Futures maturities. The Futures Spread Margin (MFS is assumed to be equal to the highest value of the largest SPR computed for each couple of futures maturities: MFS = Max{ SPR }; i j i, j. The following approach is used to define the hypothesis on ± S, ± ρ and ± ρ : ± S is coherently assumed to be equal to the current Margin Interval, while ρ and ± ρ are fixed equal to the highest daily variations (in absolute value for the appropriate risk free rates. For example, to compute the possible variation of a calendar spread constituted with a Future of first maturity and another Future of third maturity, the largest variations of one month rates and the highest variations of three months rates are considered. The hypothesis about the interest rates variations are appropriately conservative because it is supposed that, between the first and the third maturity of the calendar spread, the yield curve has a torsion equal to ρ + ρ : that is that the two maturities experience simultaneously their largest variation since the introduction of the Euro (January 4 th, 999 and that these variations are of opposite sign. With regards to the signs of the variations to apply, the most protective result can be obtained by assuming an increase in the price of the underlying (+ S equal to the side of the margin interval, a decrease of the interest rate for the first maturity (- ρ and an increase of the interest rate for the second maturity 4 (+ ρ. The calendar spreads that are being considered are based on mid-market prices, while in principle half of the bid-ask spread should be added to the short Futures price and subtracted from the long Futures price, like formalized in the following analytical. 4 This statement can be explained (without using the analytical characteristics of the exponential function in the following way: an interest rate increase for the second maturity raises the Futures price of the second maturity date, a decrease of the rates for the first maturity lowers the Futures price for the first maturity date and the calendar spread (price difference rises. The positive S causes an increase in the price of both the maturities but has a larger effect on the prices of the second maturity (the delta of a Future increases with the time to maturity, raising (slightly the calendar spread. Page 5 di 6

6 expression, where BAS e BAS are the bid-ask spread of the first and second maturities: SPR BA = ( S ± S e t BAS + BAS e + ( ρ ± ρ t ( ρ ± ρ To take into account the bid-ask spread, an additional term (obtained by applying the methodology previously described equal to the highest bid-ask spread given to the market makers for the first maturity of the Future, must be added to the mathematical Futures Spread margin.. Page 6 di 6

Methodologies for determining the parameters used in Margin Calculations for Equities and Equity Derivatives. Manual

Methodologies for determining the parameters used in Margin Calculations for Equities and Equity Derivatives. Manual Methodologies for determining the parameters used in Margin Calculations for Equities and Equity Derivatives Manual Aprile, 2017 1.0 Executive summary... 3 2.0 Methodologies for determining Margin Parameters

More information

Forwards and Futures

Forwards and Futures Forwards and Futures An Undergraduate Introduction to Financial Mathematics J. Robert Buchanan 2010 Forwards Definition A forward is an agreement between two parties to buy or sell a specified quantity

More information

Contents. Methodologies for determining Initial Margins. Manual

Contents. Methodologies for determining Initial Margins. Manual Contents Methodologies for determining Initial Margins Manual Version 1 as of 12 October 2017 1.0 Executive summary... 1 2.0 Margin Calculation for Equity and Equity Derivatives... 1 2.1. Types of Initial

More information

Forwards and Futures. MATH 472 Financial Mathematics. J Robert Buchanan

Forwards and Futures. MATH 472 Financial Mathematics. J Robert Buchanan Forwards and Futures MATH 472 Financial Mathematics J Robert Buchanan 2018 Objectives In this lesson we will learn: the definitions of financial instruments known as forward contracts and futures contracts,

More information

The Method for Determining Initial Margins

The Method for Determining Initial Margins The Method for Determining Initial Margins RM Office Version 1.0 Summary Foreword... 3 1. Types of Initial Margins... 3 2. Calculating the Ordinary Initial Margins... 4 3. Defining the Parameters... 6

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

THE UNIVERSITY OF TEXAS AT AUSTIN Department of Information, Risk, and Operations Management

THE UNIVERSITY OF TEXAS AT AUSTIN Department of Information, Risk, and Operations Management THE UNIVERSITY OF TEXAS AT AUSTIN Department of Information, Risk, and Operations Management BA 386T Tom Shively PROBABILITY CONCEPTS AND NORMAL DISTRIBUTIONS The fundamental idea underlying any statistical

More information

Lecture Quantitative Finance Spring Term 2015

Lecture Quantitative Finance Spring Term 2015 and Lecture Quantitative Finance Spring Term 2015 Prof. Dr. Erich Walter Farkas Lecture 06: March 26, 2015 1 / 47 Remember and Previous chapters: introduction to the theory of options put-call parity fundamentals

More information

Default Fund Manual. Calculation Methodology of the Contribution Quota to the Default Fund Energy Derivatives Section

Default Fund Manual. Calculation Methodology of the Contribution Quota to the Default Fund Energy Derivatives Section Default Fund Manual Calculation Methodology of the Contribution Quota to the Default Fund Energy Derivatives Section Version 1.3 - September 2017 Contents 1.0 Foreword...3 2.0 Parameters...4 3.0 Calculation

More information

Chapter 9 - Mechanics of Options Markets

Chapter 9 - Mechanics of Options Markets Chapter 9 - Mechanics of Options Markets Types of options Option positions and profit/loss diagrams Underlying assets Specifications Trading options Margins Taxation Warrants, employee stock options, and

More information

Chapter 17. The. Value Example. The Standard Error. Example The Short Cut. Classifying and Counting. Chapter 17. The.

Chapter 17. The. Value Example. The Standard Error. Example The Short Cut. Classifying and Counting. Chapter 17. The. Context Short Part V Chance Variability and Short Last time, we learned that it can be helpful to take real-life chance processes and turn them into a box model. outcome of the chance process then corresponds

More information

Portfolio Management and Optimal Execution via Convex Optimization

Portfolio Management and Optimal Execution via Convex Optimization Portfolio Management and Optimal Execution via Convex Optimization Enzo Busseti Stanford University April 9th, 2018 Problems portfolio management choose trades with optimization minimize risk, maximize

More information

MVP Manual Margin Calculation for Cash and Repo Transactions on Bonds Markets

MVP Manual Margin Calculation for Cash and Repo Transactions on Bonds Markets MVP Manual MVP Manual Margin Calculation for Cash and Repo Transactions on Bonds Markets Version 1.18 May 2015 Contents Foreword...3 a) Calculation of Mark-To-Market Margins...3 Step 1. Retrieval of market

More information

Cassa as Central Counterparty for Equity Cash Markets The Method for Calculating Initial Margins

Cassa as Central Counterparty for Equity Cash Markets The Method for Calculating Initial Margins Cassa as Central Counterparty for Equity Cash Markets The Method for Calculating Initial Margins RM Office Version 2.1 Index Foreword... 3 a) Scope... 3 b) Objectives... 3 1. Method for calculating Initial

More information

Market making on the IDEM

Market making on the IDEM Market making on the IDEM Index 1. Market making on the IDEM 3 2. Application process 5 3. Market making performance evaluation 5 4. Risk-protection functionalities 6 5. FTSE MIB* index futures and mini-futures

More information

THEORETICAL INTERMARKET MARGINS SYSTEM

THEORETICAL INTERMARKET MARGINS SYSTEM TIMS THEORETICAL INTERMARKET MARGINS SYSTEM by The Options Clearing Corporation USER SPECIFICATIONS Index Introduction... 3 Section 1. Overview of TIMS Margin Calculations... 4 Section 2. Data Requirements...

More information

PRiME Margining Guide

PRiME Margining Guide PRiME Margining Guide June 2017 Document Version 1.3 Copyright 2003-2017 HKEX All Rights Reserved This document describes the algorithm of PRiME. No part of this PRiME Margining Guide may be copied, distributed,

More information

Some Characteristics of Data

Some Characteristics of Data Some Characteristics of Data Not all data is the same, and depending on some characteristics of a particular dataset, there are some limitations as to what can and cannot be done with that data. Some key

More information

MARS. Margining System. User Specifications

MARS. Margining System. User Specifications MARS Margining System User Specifications Version 1 - October 2017 1 Contents 1.0 Overview of MARS Margin Calculations... 4 2.0 Data Requirements... 10 1. 2. 3. Risk Array (theoretical values)... 10 Class

More information

Statistical Methods in Practice STAT/MATH 3379

Statistical Methods in Practice STAT/MATH 3379 Statistical Methods in Practice STAT/MATH 3379 Dr. A. B. W. Manage Associate Professor of Mathematics & Statistics Department of Mathematics & Statistics Sam Houston State University Overview 6.1 Discrete

More information

On a Manufacturing Capacity Problem in High-Tech Industry

On a Manufacturing Capacity Problem in High-Tech Industry Applied Mathematical Sciences, Vol. 11, 217, no. 2, 975-983 HIKARI Ltd, www.m-hikari.com https://doi.org/1.12988/ams.217.7275 On a Manufacturing Capacity Problem in High-Tech Industry Luca Grosset and

More information

Financial Risk Management and Governance Beyond VaR. Prof. Hugues Pirotte

Financial Risk Management and Governance Beyond VaR. Prof. Hugues Pirotte Financial Risk Management and Governance Beyond VaR Prof. Hugues Pirotte 2 VaR Attempt to provide a single number that summarizes the total risk in a portfolio. What loss level is such that we are X% confident

More information

Arithmetic. Mathematics Help Sheet. The University of Sydney Business School

Arithmetic. Mathematics Help Sheet. The University of Sydney Business School Arithmetic Mathematics Help Sheet The University of Sydney Business School Common Arithmetic Symbols is not equal to is approximately equal to is identically equal to infinity, which is a non-finite number

More information

Statistical Arbitrage in Balancing Markets

Statistical Arbitrage in Balancing Markets Statistical Arbitrage in Balancing Markets and the Impact of Time Delay Stefan Kermer, Derek Bunn 1 Agenda Introduction Austrian Imbalance Settlement Design Market Players Perspectives Predicting the Conditional

More information

Instantaneous rate of change (IRC) at the point x Slope of tangent

Instantaneous rate of change (IRC) at the point x Slope of tangent CHAPTER 2: Differentiation Do not study Sections 2.1 to 2.3. 2.4 Rates of change Rate of change (RC) = Two types Average rate of change (ARC) over the interval [, ] Slope of the line segment Instantaneous

More information

Gamma. The finite-difference formula for gamma is

Gamma. The finite-difference formula for gamma is Gamma The finite-difference formula for gamma is [ P (S + ɛ) 2 P (S) + P (S ɛ) e rτ E ɛ 2 ]. For a correlation option with multiple underlying assets, the finite-difference formula for the cross gammas

More information

MARKET MAKING SCHEME FOR EXCHANGE TRADED PRODUCTS

MARKET MAKING SCHEME FOR EXCHANGE TRADED PRODUCTS 20 NOVEMBER 2017 Issue Date: 21 November 2017 EFFECTIVE DATE: 3 January 2018 Document type MARKET MAKING SCHEME FOR EXCHANGE TRADED PRODUCTS 1 1. MAIN PRINCIPLES 1.1 DOCUMENTATION The appointment of each

More information

Asian Option Pricing: Monte Carlo Control Variate. A discrete arithmetic Asian call option has the payoff. S T i N N + 1

Asian Option Pricing: Monte Carlo Control Variate. A discrete arithmetic Asian call option has the payoff. S T i N N + 1 Asian Option Pricing: Monte Carlo Control Variate A discrete arithmetic Asian call option has the payoff ( 1 N N + 1 i=0 S T i N K ) + A discrete geometric Asian call option has the payoff [ N i=0 S T

More information

Rho and Delta. Paul Hollingsworth January 29, Introduction 1. 2 Zero coupon bond 1. 3 FX forward 2. 5 Rho (ρ) 4. 7 Time bucketing 6

Rho and Delta. Paul Hollingsworth January 29, Introduction 1. 2 Zero coupon bond 1. 3 FX forward 2. 5 Rho (ρ) 4. 7 Time bucketing 6 Rho and Delta Paul Hollingsworth January 29, 2012 Contents 1 Introduction 1 2 Zero coupon bond 1 3 FX forward 2 4 European Call under Black Scholes 3 5 Rho (ρ) 4 6 Relationship between Rho and Delta 5

More information

Optimal Investment for Generalized Utility Functions

Optimal Investment for Generalized Utility Functions Optimal Investment for Generalized Utility Functions Thijs Kamma Maastricht University July 05, 2018 Overview Introduction Terminal Wealth Problem Utility Specifications Economic Scenarios Results Black-Scholes

More information

The impact of the current crisis on the Italian labour market

The impact of the current crisis on the Italian labour market The impact of the current crisis on the Italian labour market Francesco D Amuri January 27, 2010 Preliminary draft: please do not quote. To be updated with the latest LFS data (2009:3) available shortly.

More information

Name For those going into. Algebra 1 Honors. School years that begin with an ODD year: do the odds

Name For those going into. Algebra 1 Honors. School years that begin with an ODD year: do the odds Name For those going into LESSON 2.1 Study Guide For use with pages 64 70 Algebra 1 Honors GOAL: Graph and compare positive and negative numbers Date Natural numbers are the numbers 1,2,3, Natural numbers

More information

Math 103: The Mean Value Theorem and How Derivatives Shape a Graph

Math 103: The Mean Value Theorem and How Derivatives Shape a Graph Math 03: The Mean Value Theorem and How Derivatives Shape a Graph Ryan Blair University of Pennsylvania Thursday October 27, 20 Math 03: The Mean Value Theorem and How Derivatives Thursday October Shape

More information

Value at Risk Ch.12. PAK Study Manual

Value at Risk Ch.12. PAK Study Manual Value at Risk Ch.12 Related Learning Objectives 3a) Apply and construct risk metrics to quantify major types of risk exposure such as market risk, credit risk, liquidity risk, regulatory risk etc., and

More information

Variance, Standard Deviation Counting Techniques

Variance, Standard Deviation Counting Techniques Variance, Standard Deviation Counting Techniques Section 1.3 & 2.1 Cathy Poliak, Ph.D. cathy@math.uh.edu Department of Mathematics University of Houston 1 / 52 Outline 1 Quartiles 2 The 1.5IQR Rule 3 Understanding

More information

SPAN Methodology Derivatives Market

SPAN Methodology Derivatives Market Table of Contents SPAN Methodology Derivatives Market Introduction... 2 Detailed Description of SPAN Elements... 3 Detailed rules for calculating margins... 6 Practical examples of margin requirement calculations...

More information

T.I.H.E. IT 233 Statistics and Probability: Sem. 1: 2013 ESTIMATION

T.I.H.E. IT 233 Statistics and Probability: Sem. 1: 2013 ESTIMATION In Inferential Statistic, ESTIMATION (i) (ii) is called the True Population Mean and is called the True Population Proportion. You must also remember that are not the only population parameters. There

More information

The Black-Scholes-Merton Model

The Black-Scholes-Merton Model Normal (Gaussian) Distribution Probability Density 0.5 0. 0.15 0.1 0.05 0 1.1 1 0.9 0.8 0.7 0.6? 0.5 0.4 0.3 0. 0.1 0 3.6 5. 6.8 8.4 10 11.6 13. 14.8 16.4 18 Cumulative Probability Slide 13 in this slide

More information

The Self-financing Condition: Remembering the Limit Order Book

The Self-financing Condition: Remembering the Limit Order Book The Self-financing Condition: Remembering the Limit Order Book R. Carmona, K. Webster Bendheim Center for Finance ORFE, Princeton University November 6, 2013 Structural relationships? From LOB Models to

More information

Fair value of insurance liabilities

Fair value of insurance liabilities Fair value of insurance liabilities A basic example of the assessment of MVM s and replicating portfolio. The following steps will need to be taken to determine the market value of the liabilities: 1.

More information

Options. An Undergraduate Introduction to Financial Mathematics. J. Robert Buchanan. J. Robert Buchanan Options

Options. An Undergraduate Introduction to Financial Mathematics. J. Robert Buchanan. J. Robert Buchanan Options Options An Undergraduate Introduction to Financial Mathematics J. Robert Buchanan 2014 Definitions and Terminology Definition An option is the right, but not the obligation, to buy or sell a security such

More information

Math 140 Introductory Statistics. First midterm September

Math 140 Introductory Statistics. First midterm September Math 140 Introductory Statistics First midterm September 23 2010 Box Plots Graphical display of 5 number summary Q1, Q2 (median), Q3, max, min Outliers If a value is more than 1.5 times the IQR from the

More information

OPTIONS. Options: Definitions. Definitions (Cont) Price of Call at Maturity and Payoff. Payoff from Holding Stock and Riskfree Bond

OPTIONS. Options: Definitions. Definitions (Cont) Price of Call at Maturity and Payoff. Payoff from Holding Stock and Riskfree Bond OPTIONS Professor Anant K. Sundaram THUNERBIR Spring 2003 Options: efinitions Contingent claim; derivative Right, not obligation when bought (but, not when sold) More general than might first appear Calls,

More information

P VaR0.01 (X) > 2 VaR 0.01 (X). (10 p) Problem 4

P VaR0.01 (X) > 2 VaR 0.01 (X). (10 p) Problem 4 KTH Mathematics Examination in SF2980 Risk Management, December 13, 2012, 8:00 13:00. Examiner : Filip indskog, tel. 790 7217, e-mail: lindskog@kth.se Allowed technical aids and literature : a calculator,

More information

TOPIC: PROBABILITY DISTRIBUTIONS

TOPIC: PROBABILITY DISTRIBUTIONS TOPIC: PROBABILITY DISTRIBUTIONS There are two types of random variables: A Discrete random variable can take on only specified, distinct values. A Continuous random variable can take on any value within

More information

DATA HANDLING Five-Number Summary

DATA HANDLING Five-Number Summary DATA HANDLING Five-Number Summary The five-number summary consists of the minimum and maximum values, the median, and the upper and lower quartiles. The minimum and the maximum are the smallest and greatest

More information

Calculating VaR. There are several approaches for calculating the Value at Risk figure. The most popular are the

Calculating VaR. There are several approaches for calculating the Value at Risk figure. The most popular are the VaR Pro and Contra Pro: Easy to calculate and to understand. It is a common language of communication within the organizations as well as outside (e.g. regulators, auditors, shareholders). It is not really

More information

IIntroduction the framework

IIntroduction the framework Author: Frédéric Planchet / Marc Juillard/ Pierre-E. Thérond Extreme disturbances on the drift of anticipated mortality Application to annuity plans 2 IIntroduction the framework We consider now the global

More information

Advanced Topics in Derivative Pricing Models. Topic 4 - Variance products and volatility derivatives

Advanced Topics in Derivative Pricing Models. Topic 4 - Variance products and volatility derivatives Advanced Topics in Derivative Pricing Models Topic 4 - Variance products and volatility derivatives 4.1 Volatility trading and replication of variance swaps 4.2 Volatility swaps 4.3 Pricing of discrete

More information

Measures of Dispersion (Range, standard deviation, standard error) Introduction

Measures of Dispersion (Range, standard deviation, standard error) Introduction Measures of Dispersion (Range, standard deviation, standard error) Introduction We have already learnt that frequency distribution table gives a rough idea of the distribution of the variables in a sample

More information

Order book resilience, price manipulations, and the positive portfolio problem

Order book resilience, price manipulations, and the positive portfolio problem Order book resilience, price manipulations, and the positive portfolio problem Alexander Schied Mannheim University PRisMa Workshop Vienna, September 28, 2009 Joint work with Aurélien Alfonsi and Alla

More information

CHAPTER 5 STOCHASTIC SCHEDULING

CHAPTER 5 STOCHASTIC SCHEDULING CHPTER STOCHSTIC SCHEDULING In some situations, estimating activity duration becomes a difficult task due to ambiguity inherited in and the risks associated with some work. In such cases, the duration

More information

1. Why is it important for corporate managers to understand how bonds and shares are priced?

1. Why is it important for corporate managers to understand how bonds and shares are priced? CHAPTER 4 CONCEPT REVIEW QUESTIONS 1. Why is it important for corporate managers to understand how bonds and shares are priced? Managers need to know this because (1) firms regularly issue stocks and bonds

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

Martingale Pricing Theory in Discrete-Time and Discrete-Space Models

Martingale Pricing Theory in Discrete-Time and Discrete-Space Models IEOR E4707: Foundations of Financial Engineering c 206 by Martin Haugh Martingale Pricing Theory in Discrete-Time and Discrete-Space Models These notes develop the theory of martingale pricing in a discrete-time,

More information

Normal Model (Part 1)

Normal Model (Part 1) Normal Model (Part 1) Formulas New Vocabulary The Standard Deviation as a Ruler The trick in comparing very different-looking values is to use standard deviations as our rulers. The standard deviation

More information

ELEMENTS OF MONTE CARLO SIMULATION

ELEMENTS OF MONTE CARLO SIMULATION APPENDIX B ELEMENTS OF MONTE CARLO SIMULATION B. GENERAL CONCEPT The basic idea of Monte Carlo simulation is to create a series of experimental samples using a random number sequence. According to the

More information

TEACHING NOTE 98-04: EXCHANGE OPTION PRICING

TEACHING NOTE 98-04: EXCHANGE OPTION PRICING TEACHING NOTE 98-04: EXCHANGE OPTION PRICING Version date: June 3, 017 C:\CLASSES\TEACHING NOTES\TN98-04.WPD The exchange option, first developed by Margrabe (1978), has proven to be an extremely powerful

More information

Pricing Volatility Derivatives with General Risk Functions. Alejandro Balbás University Carlos III of Madrid

Pricing Volatility Derivatives with General Risk Functions. Alejandro Balbás University Carlos III of Madrid Pricing Volatility Derivatives with General Risk Functions Alejandro Balbás University Carlos III of Madrid alejandro.balbas@uc3m.es Content Introduction. Describing volatility derivatives. Pricing and

More information

Copyright 2005 Pearson Education, Inc. Slide 6-1

Copyright 2005 Pearson Education, Inc. Slide 6-1 Copyright 2005 Pearson Education, Inc. Slide 6-1 Chapter 6 Copyright 2005 Pearson Education, Inc. Measures of Center in a Distribution 6-A The mean is what we most commonly call the average value. It is

More information

Microeconomics 2nd Period Exam Solution Topics

Microeconomics 2nd Period Exam Solution Topics Microeconomics 2nd Period Exam Solution Topics Group I Suppose a representative firm in a perfectly competitive, constant-cost industry has a cost function: T C(q) = 2q 2 + 100q + 100 (a) If market demand

More information

University of California, Los Angeles Department of Statistics. Final exam 07 June 2013

University of California, Los Angeles Department of Statistics. Final exam 07 June 2013 University of California, Los Angeles Department of Statistics Statistics C183/C283 Instructor: Nicolas Christou Final exam 07 June 2013 Name: Problem 1 (20 points) a. Suppose the variable X follows the

More information

Greek parameters of nonlinear Black-Scholes equation

Greek parameters of nonlinear Black-Scholes equation International Journal of Mathematics and Soft Computing Vol.5, No.2 (2015), 69-74. ISSN Print : 2249-3328 ISSN Online: 2319-5215 Greek parameters of nonlinear Black-Scholes equation Purity J. Kiptum 1,

More information

Math 2311 Bekki George Office Hours: MW 11am to 12:45pm in 639 PGH Online Thursdays 4-5:30pm And by appointment

Math 2311 Bekki George Office Hours: MW 11am to 12:45pm in 639 PGH Online Thursdays 4-5:30pm And by appointment Math 2311 Bekki George bekki@math.uh.edu Office Hours: MW 11am to 12:45pm in 639 PGH Online Thursdays 4-5:30pm And by appointment Class webpage: http://www.math.uh.edu/~bekki/math2311.html Math 2311 Class

More information

5.3 Standard Deviation

5.3 Standard Deviation Math 2201 Date: 5.3 Standard Deviation Standard Deviation We looked at range as a measure of dispersion, or spread of a data set. The problem with using range is that it is only a measure of how spread

More information

FE610 Stochastic Calculus for Financial Engineers. Stevens Institute of Technology

FE610 Stochastic Calculus for Financial Engineers. Stevens Institute of Technology FE610 Stochastic Calculus for Financial Engineers Lecture 13. The Black-Scholes PDE Steve Yang Stevens Institute of Technology 04/25/2013 Outline 1 The Black-Scholes PDE 2 PDEs in Asset Pricing 3 Exotic

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

P.1 Algebraic Expressions, Mathematical models, and Real numbers. Exponential notation: Definitions of Sets: A B. Sets and subsets of real numbers:

P.1 Algebraic Expressions, Mathematical models, and Real numbers. Exponential notation: Definitions of Sets: A B. Sets and subsets of real numbers: P.1 Algebraic Expressions, Mathematical models, and Real numbers If n is a counting number (1, 2, 3, 4,..) then Exponential notation: b n = b b b... b, where n is the Exponent or Power, and b is the base

More information

Queens College, CUNY, Department of Computer Science Computational Finance CSCI 365 / 765 Fall 2017 Instructor: Dr. Sateesh Mane.

Queens College, CUNY, Department of Computer Science Computational Finance CSCI 365 / 765 Fall 2017 Instructor: Dr. Sateesh Mane. Queens College, CUNY, Department of Computer Science Computational Finance CSCI 365 / 765 Fall 2017 Instructor: Dr. Sateesh Mane c Sateesh R. Mane 2017 14 Lecture 14 November 15, 2017 Derivation of the

More information

1. MAPLE. Objective: After reading this chapter, you will solve mathematical problems using Maple

1. MAPLE. Objective: After reading this chapter, you will solve mathematical problems using Maple 1. MAPLE Objective: After reading this chapter, you will solve mathematical problems using Maple 1.1 Maple Maple is an extremely powerful program, which can be used to work out many different types of

More information

4.2 Probability Distributions

4.2 Probability Distributions 4.2 Probability Distributions Definition. A random variable is a variable whose value is a numerical outcome of a random phenomenon. The probability distribution of a random variable tells us what the

More information

Financial Risk Measurement/Management

Financial Risk Measurement/Management 550.446 Financial Risk Measurement/Management Week of September 23, 2013 Interest Rate Risk & Value at Risk (VaR) 3.1 Where we are Last week: Introduction continued; Insurance company and Investment company

More information

Improving Returns-Based Style Analysis

Improving Returns-Based Style Analysis Improving Returns-Based Style Analysis Autumn, 2007 Daniel Mostovoy Northfield Information Services Daniel@northinfo.com Main Points For Today Over the past 15 years, Returns-Based Style Analysis become

More information

Limits to Arbitrage. George Pennacchi. Finance 591 Asset Pricing Theory

Limits to Arbitrage. George Pennacchi. Finance 591 Asset Pricing Theory Limits to Arbitrage George Pennacchi Finance 591 Asset Pricing Theory I.Example: CARA Utility and Normal Asset Returns I Several single-period portfolio choice models assume constant absolute risk-aversion

More information

Option Pricing. Simple Arbitrage Relations. Payoffs to Call and Put Options. Black-Scholes Model. Put-Call Parity. Implied Volatility

Option Pricing. Simple Arbitrage Relations. Payoffs to Call and Put Options. Black-Scholes Model. Put-Call Parity. Implied Volatility Simple Arbitrage Relations Payoffs to Call and Put Options Black-Scholes Model Put-Call Parity Implied Volatility Option Pricing Options: Definitions A call option gives the buyer the right, but not the

More information

Robustness, Model Uncertainty and Pricing

Robustness, Model Uncertainty and Pricing Robustness, Model Uncertainty and Pricing Antoon Pelsser 1 1 Maastricht University & Netspar Email: a.pelsser@maastrichtuniversity.nl 29 October 2010 Swissquote Conference Lausanne A. Pelsser (Maastricht

More information

1 The EOQ and Extensions

1 The EOQ and Extensions IEOR4000: Production Management Lecture 2 Professor Guillermo Gallego September 16, 2003 Lecture Plan 1. The EOQ and Extensions 2. Multi-Item EOQ Model 1 The EOQ and Extensions We have explored some of

More information

Robust Optimization Applied to a Currency Portfolio

Robust Optimization Applied to a Currency Portfolio Robust Optimization Applied to a Currency Portfolio R. Fonseca, S. Zymler, W. Wiesemann, B. Rustem Workshop on Numerical Methods and Optimization in Finance June, 2009 OUTLINE Introduction Motivation &

More information

Statistics for Business and Economics

Statistics for Business and Economics Statistics for Business and Economics Chapter 5 Continuous Random Variables and Probability Distributions Ch. 5-1 Probability Distributions Probability Distributions Ch. 4 Discrete Continuous Ch. 5 Probability

More information

Macroeconomics. Lecture 5: Consumption. Hernán D. Seoane. Spring, 2016 MEDEG, UC3M UC3M

Macroeconomics. Lecture 5: Consumption. Hernán D. Seoane. Spring, 2016 MEDEG, UC3M UC3M Macroeconomics MEDEG, UC3M Lecture 5: Consumption Hernán D. Seoane UC3M Spring, 2016 Introduction A key component in NIPA accounts and the households budget constraint is the consumption It represents

More information

Four Major Asset Classes

Four Major Asset Classes Four Major Asset Classes Christopher Ting Christopher Ting http://www.mysmu.edu/faculty/christophert/ : christopherting@smu.edu.sg : 6828 0364 : LKCSB 5036 August 26, 2016 Christopher Ting QF 101 Week

More information

Comprehensive Exam. August 19, 2013

Comprehensive Exam. August 19, 2013 Comprehensive Exam August 19, 2013 You have a total of 180 minutes to complete the exam. If a question seems ambiguous, state why, sharpen it up and answer the sharpened-up question. Good luck! 1 1 Menu

More information

Extensions to the Black Scholes Model

Extensions to the Black Scholes Model Lecture 16 Extensions to the Black Scholes Model 16.1 Dividends Dividend is a sum of money paid regularly (typically annually) by a company to its shareholders out of its profits (or reserves). In this

More information

AP Statistics Chapter 6 - Random Variables

AP Statistics Chapter 6 - Random Variables AP Statistics Chapter 6 - Random 6.1 Discrete and Continuous Random Objective: Recognize and define discrete random variables, and construct a probability distribution table and a probability histogram

More information

Internet Appendix: High Frequency Trading and Extreme Price Movements

Internet Appendix: High Frequency Trading and Extreme Price Movements Internet Appendix: High Frequency Trading and Extreme Price Movements This appendix includes two parts. First, it reports the results from the sample of EPMs defined as the 99.9 th percentile of raw returns.

More information

Introduction to Real Options

Introduction to Real Options IEOR E4706: Foundations of Financial Engineering c 2016 by Martin Haugh Introduction to Real Options We introduce real options and discuss some of the issues and solution methods that arise when tackling

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

Practical example of an Economic Scenario Generator

Practical example of an Economic Scenario Generator Practical example of an Economic Scenario Generator Martin Schenk Actuarial & Insurance Solutions SAV 7 March 2014 Agenda Introduction Deterministic vs. stochastic approach Mathematical model Application

More information

The effects of transaction costs on depth and spread*

The effects of transaction costs on depth and spread* The effects of transaction costs on depth and spread* Dominique Y Dupont Board of Governors of the Federal Reserve System E-mail: midyd99@frb.gov Abstract This paper develops a model of depth and spread

More information

The investment game in incomplete markets.

The investment game in incomplete markets. The investment game in incomplete markets. M. R. Grasselli Mathematics and Statistics McMaster University RIO 27 Buzios, October 24, 27 Successes and imitations of Real Options Real options accurately

More information

Wiener Börse AG Changes in Specialist- and Market Maker System. February, 2015

Wiener Börse AG Changes in Specialist- and Market Maker System. February, 2015 Wiener Börse AG Changes in Specialist- and Market Maker System February, 2015 Quotation Time - Changes as of 01.04.2015 Extension of daily observation period new: 09.00 17.33 CET Quotation obligations

More information

Chapter 7 presents the beginning of inferential statistics. The two major activities of inferential statistics are

Chapter 7 presents the beginning of inferential statistics. The two major activities of inferential statistics are Chapter 7 presents the beginning of inferential statistics. Concept: Inferential Statistics The two major activities of inferential statistics are 1 to use sample data to estimate values of population

More information

Solutions for Assignment #2, Managerial Economics, ECO 351M, Fall 2016 Due, Monday Sept 26.

Solutions for Assignment #2, Managerial Economics, ECO 351M, Fall 2016 Due, Monday Sept 26. Solutions for Assignment #2, Managerial Economics, ECO 351M, Fall 2016 Due, Monday Sept 26. 1. From Ch. 8 of Kreps s Micro for Managers, a. Problem 8.1. (a), Figure 8.4, fixed, 50, plus rising, x/8000,

More information

A Scenario Based Method for Cost Risk Analysis

A Scenario Based Method for Cost Risk Analysis A Scenario Based Method for Cost Risk Analysis Paul R. Garvey The MITRE Corporation MP 05B000003, September 005 Abstract This paper presents an approach for performing an analysis of a program s cost risk.

More information

For students electing Macro (8701/Prof. Roe) & Micro (8703/Prof. Glewwe) option

For students electing Macro (8701/Prof. Roe) & Micro (8703/Prof. Glewwe) option WRITTEN PRELIMINARY Ph.D EXAMINATION Department of Applied Economics Jan./Feb. - 2011 Trade, Development and Growth For students electing Macro (8701/Prof. Roe) & Micro (8703/Prof. Glewwe) option Instructions

More information

Optimal Trading Strategy With Optimal Horizon

Optimal Trading Strategy With Optimal Horizon Optimal Trading Strategy With Optimal Horizon Financial Math Festival Florida State University March 1, 2008 Edward Qian PanAgora Asset Management Trading An Integral Part of Investment Process Return

More information

Financial Mathematics

Financial Mathematics Financial Mathematics Introduction Interest can be defined in two ways. 1. Interest is money earned when money is invested. Eg. You deposited RM 1000 in a bank for a year and you find that at the end of

More information

FINANCIAL MATHEMATICS WITH ADVANCED TOPICS MTHE7013A

FINANCIAL MATHEMATICS WITH ADVANCED TOPICS MTHE7013A UNIVERSITY OF EAST ANGLIA School of Mathematics Main Series UG Examination 2016 17 FINANCIAL MATHEMATICS WITH ADVANCED TOPICS MTHE7013A Time allowed: 3 Hours Attempt QUESTIONS 1 and 2, and THREE other

More information

Pricing and hedging in incomplete markets

Pricing and hedging in incomplete markets Pricing and hedging in incomplete markets Chapter 10 From Chapter 9: Pricing Rules: Market complete+nonarbitrage= Asset prices The idea is based on perfect hedge: H = V 0 + T 0 φ t ds t + T 0 φ 0 t ds

More information

Please respond to: LME Clear Market Risk Risk Management Department

Please respond to: LME Clear Market Risk Risk Management Department Please respond to: LME Clear Market Risk Risk Management Department lmeclear.marketrisk@lme.com THE LONDON METAL EXCHANGE AND LME CLEAR LIMITED 10 Finsbury Square, London EC2A 1AJ Tel +44 (0)20 7113 8888

More information