How to prepare yourself for a Quants job in the financial market?   Strong knowledge of option pricing theory (quantitative models for pricing and hedging)

Slides:



Advertisements
Similar presentations
Chp.4 Lifetime Portfolio Selection Under Uncertainty
Advertisements

Overview of Quantitative Finance and Risk Management Research By Dr. Cheng-Few Lee Distinguished Professor, Rutgers University, USA Distinguished Professor,
UNIT 1 CONCEPT OF MANAGERIAL ECONOMICS (continue)
Chapter 2. Unobserved Component models Esther Ruiz PhD Program in Business Administration and Quantitative Analysis Financial Econometrics.
25 September 2009 School of Economics 1 Information Session on Second Major in Applied Statistics (APS) Prepared by Kwong Koon Shing.
Applications of Stochastic Processes in Asset Price Modeling Preetam D’Souza.
Introduction to Algorithmic Trading Strategies Lecture 3 Pairs Trading by Cointegration Haksun Li
By: Piet Nova The Binomial Tree Model.  Important problem in financial markets today  Computation of a particular integral  Methods of valuation 
Numerical Method Inc. Ltd. URL: Presented by Ken Yiu.
L7: Stochastic Process 1 Lecture 7: Stochastic Process The following topics are covered: –Markov Property and Markov Stochastic Process –Wiener Process.
Workshop on Stochastic Differential Equations and Statistical Inference for Markov Processes Day 1: January 19 th, Day 2: January 28 th Lahore University.
Andrey Itkin, Math Selected Topics in Applied Mathematics – Computational Finance Andrey Itkin Course web page
Ivan Bercovich Senior Lecture Series Friday, April 17 th, 2009.
FIN 685: Risk Management Topic 5: Simulation Larry Schrenk, Instructor.
Blending Knowledge, Skills and Experience in a Professional Science Master’s Program Presenter Paul W. Eloe Department of Mathematics Date: October 14,
A Workshop on Subject GRE / AGRE Maths in 9 Classes, II Hours each Day & Three mock tests for AGRE By: Satyadhar Joshi
Master of Science in Financial Mathematics and Stastistics Orientation Session, Fall 2009.
How does this Program equip students for a successful career in financial engineering? - technically skilled and financially streetwise (development of.
Options and Speculative Markets Introduction to option pricing André Farber Solvay Business School University of Brussels.
1 National Integrated Project on Fundamental Mathematics Tung-Hai University Chiao-Tung University Taiwan University Tsing-Hwa University 2001~ 2005.
Bruno Dupire Bloomberg LP CRFMS, UCSB Santa Barbara, April 26, 2007
Why attending this Program Sharpening the quantitative skills in   Pricing, hedging and risk measurement of derivative securities   Implementing risk.
Recruitment
5.2Risk-Neutral Measure Part 2 報告者:陳政岳 Stock Under the Risk-Neutral Measure is a Brownian motion on a probability space, and is a filtration for.
Financial Engineering Club Career Path and Prep. Entry Level Career Paths Type 1: Research based Background: Physics, Electrical Engineering, Applied.
Rene A. Carmona Bendheim Center for Finance Department of Operations Research & Financial Engineering Princeton University Portfolio Risk in the Electricity.
9.4 Forward Measure Forward Price Zero-Coupon Bond as Numeraire Theorem
Masters in Information Science and Technology (IST) Thesis and Non-Thesis Option (30 Credits)
Derivatives Introduction to option pricing André Farber Solvay Business School University of Brussels.
Background Required. Mathematical Courses Calculus I and II Multivariable Courses Linear Algebra Differential Equations (ODE and PDE’s) Probability Statistics.
Opportunities in Quantitative Finance in the Department of Mathematics.
Lecture Presentation Software to accompany Investment Analysis and Portfolio Management Seventh Edition by Frank K. Reilly & Keith C. Brown Chapter 23.
JUMP DIFFUSION MODELS Karina Mignone Option Pricing under Jump Diffusion.
Lecture 11 Implementation Issues – Part 2. Monte Carlo Simulation An alternative approach to valuing embedded options is simulation Underlying model “simulates”
Ewa Lukasik - Jakub Lawik - Juan Mojica - Xiaodong Xu.
Advanced Risk Management I Lecture 6 Non-linear portfolios.
Corporate Banking and Investment Risk tolerance and optimal portfolio choice Marek Musiela, BNP Paribas, London.
A Brief Introduction of FE. What is FE? Financial engineering (quantitative finance, computational finance, or mathematical finance): –A cross-disciplinary.
1 Derivatives & Risk Management: Part II Models, valuation and risk management.
Smart Monte Carlo: Various Tricks Using Malliavin Calculus Quantitative Finance, NY, Nov 2002 Eric Benhamou Goldman Sachs International.
Derivative Financial Products Donald C. Williams Doctoral Candidate Department of Computational and Applied Mathematics, Rice University Thesis Advisors.
Estimating Credit Exposure and Economic Capital Using Monte Carlo Simulation Ronald Lagnado Vice President, MKIRisk IPAM Conference on Financial Mathematics.
1 MathFinance Colloquium Frankfurt, June 1 st, 2006 Exploring the Limits of Closed Pricing Formulas in the Black and Scholes.
Optimal Malliavin weighting functions for the simulations of the Greeks MC 2000 (July ) Eric Ben-Hamou Financial Markets Group London School of.
TheoryApplication Discrete Continuous - - Stochastic differential equations - - Ito’s formula - - Derivation of the Black-Scholes equation - - Markov processes.
Abstract  The volatility ambiguity is a major model of risk in Black-Scholes pricing formula.  We will study, from the point of view of a supervising.
Lecture 1: Introduction to QF4102 Financial Modeling
© K.Cuthbertson, D. Nitzsche FINANCIAL ENGINEERING: DERIVATIVES AND RISK MANAGEMENT (J. Wiley, 2001) K. Cuthbertson and D. Nitzsche Lecture Asset Price.
FMG PH.D. seminar (21 October 1999) slide 1 Dynamic Hedging with Transaction Costs Outline: -Introduction -Adjusted path and Strategy -Leland model and.
Investment Performance Measurement, Risk Tolerance and Optimal Portfolio Choice Marek Musiela, BNP Paribas, London.
CEP Job Research Junior Derivatives Trader. Job Nature responsible for the execution of hedges for the banks’ portfolio create monitor and maintain hedge.
Fang-Bo Yeh, Dept. of Mathematics, Tunghai Univ.2004.Jun.29 1 Financial Derivatives The Mathematics Fang-Bo Yeh Mathematics Department System and Control.
Monte-Carlo Simulations Seminar Project. Task  To Build an application in Excel/VBA to solve option prices.  Use a stochastic volatility in the model.
1 Chapter 10 Estimating Risk and Return McGraw-Hill/Irwin Copyright © 2012 by The McGraw-Hill Companies, Inc. All rights reserved.
Geometry of Stochastic Differential Equations John Armstrong (KCL), Damiano Brigo (Imperial) New perspectives in differential geometry, Rome, November.
Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 18.1 Exotic Options Chapter 18.
Lecture 8: Finance: GBM model for share prices, futures and options, hedging, and the Black-Scholes equation Outline: GBM as a model of share prices futures.
Stats Term Test 4 Solutions. c) d) An alternative solution is to use the probability mass function and.
National 5 Lifeskills Mathematics Understanding what it is & why it could be the course choice for you.
S TOCHASTIC M ODELS L ECTURE 4 P ART III B ROWNIAN M OTION Nan Chen MSc Program in Financial Engineering The Chinese University of Hong Kong (Shenzhen)
Building strong career with business analytics certification.
1 Seattle University Master’s of Science in Business Analytics Key skills, learning outcomes, and a sample of jobs to apply for, or aim to qualify for,
Applications of Stochastic Processes in Asset Price Modeling Preetam D’Souza.
Kelly McManus, FSA John Hancock Financial Services
Black-Scholes Model for European vanilla options
CS 179 Lecture 17 Options Pricing.
Mathematical Finance An Introduction
By Harsh Tiwari.
Numerical Methods in Finance
5.3 Martingale Representation Theorem
Presentation transcript:

How to prepare yourself for a Quants job in the financial market?   Strong knowledge of option pricing theory (quantitative models for pricing and hedging)   Strong software design and development skills using C++   Mastery of advanced mathematics and numerical analysis arising in financial modeling (probability theory, stochastic processes, numerical analysis) General skills: Analytic, quantitative and problem solving skills; strong communication skills

Roles and responsibilities  Develop mathematical models for pricing, hedging and risk management of derivative securities.  Support of trading activities by explaining model behavior, identifying risk sources in portfolios, carrying out scenario analysis.  Design efficient numerical algorithms and implement high performance computing solutions – delivery to systems and applications.

Relevant courses in our MSc Programs Financial Mathematics MATH571Mathematical Models of Financial Derivatives [Fall, 07] MATH572Interest Rate Models[Spring, 08] MAFS524Software Development with C++ for Quantitative Finance[Spring, 08] MAFS525Computational Methods for Pricing Structured Financial Products[Spring, 08] MAFS523Advanced Credit Risk Models[Summer, 08]

Statistics courses MAFS513Quantitative Analysis of Financial Time Series [Fall, 07] MAFS511Advanced Data Analysis with Statistical Programming [Spring, 08] MAFS522Quantitative and Statistical Risk Analysis [Summer, 08]

Foundation courses MAFS501Stochastic Calculus [Fall, 07] MAFS502Advanced Probability and Statistics [Fall, 07]

MAFS 501 Stochastic Calculus[3-0-0:3] Random walk models. Filtration. Martingales. Brownian motions. Diffusion processes. Forward and backward Kolmogorov equations. Ito’s calculus. Stochastic differential equations. Stochastic optimal control problems in finance.

MAFS 502 Advanced Probability and Statistics [3-0-0:3] Probability spaces, measurable functions and distributions, conditional probability, conditional expectations, asymptotic theorems, stopping times, martingales, Markov chains, Brownian motion, sampling distributions, sufficiency, statistical decision theory, statistical inference, unbiased estimation, method of maximum likelihood.

MAFS 513 Quantitative Analysis of Financial Time Series[3-0-0:3] Analysis of asset returns: autocorrelation, predictability and prediction. Volatility models: GARCH-type models, long range dependence. High frequency data analysis: transactions data, duration. Markov switching and threshold models. Multivariate time series: cointegration models and vector GARCH models.

MATH 571 Mathematical Models of Financial Derivatives[3-0-0:3] Black-Scholes-Merton framework, dynamic hedging, replicating portfolio. Martingale theory of option pricing, risk neutral measure. Exotic options: barrier options, lookback options and Asian options. Free boundary value pricing models: American options, reset options.

Upon completion of the program, students are expected to achieve the following intellectual abilities: A broad knowledge and understanding of the financial products commonly traded in the markets and various practical aspects of risk management. A broad knowledge and understanding of the financial products commonly traded in the markets and various practical aspects of risk management. Use of mathematical and statistical tools to construct quantitative models in derivative pricing, quantitative trading strategies, risk management, and scenario simulation, including appropriate solution methods and interpretation of results. Use of mathematical and statistical tools to construct quantitative models in derivative pricing, quantitative trading strategies, risk management, and scenario simulation, including appropriate solution methods and interpretation of results.

To graduate from the MSc program, each student is required to complete 30 credits of which  6 credits from the list of foundation courses  9 credits from the list of courses in statistics  9 credits from the list of courses in financial mathematics  6 credits as free electives* Needs to maintain a graduation grade point average of B grade or above.