Maxeler applications in financial engineering ALEKSANDAR RAKIĆEVIĆprof. BRATISLAV PETROVIĆ PhD studentMenthor University of Belgrade Faculty of Organizational.

Slides:



Advertisements
Similar presentations
Expected Utility and Post-Retirement Investment and Spending Strategies William F. Sharpe STANCO 25 Professor of Finance Stanford University
Advertisements

Credit Risk Plus.
Introduction CreditMetrics™ was launched by JP Morgan in 1997.
FIN 685: Risk Management Topic 6: VaR Larry Schrenk, Instructor.
Risk Management and Operations Solutions Derivative Pricing for Risk Calculations – Challenges and Approaches Research Workshop.
Risk Management Jan Röman OM Technology Securities Systems AB.
Asset Optimization & Decision Support Services. 1 Overview of Service Program Proprietary service platform that marries 30+ years of Engineering & Subject.
By: Piet Nova The Binomial Tree Model.  Important problem in financial markets today  Computation of a particular integral  Methods of valuation 
ORFE Princeton Preview Class 2015 Presented by Prof. Alain L. Kornhauser Department Representative Operations Research & Financial Engineering For more.
SPREADSHEETS IN EDUCATION OF LOGISTICS MANAGERS AT FACULTY OF ORGANIZATIONAL SCIENCES: AN EXAMPLE OF INVENTORY DYNAMICS SIMULATION L. Djordjevic, D. Vasiljevic.
Andrey Itkin, Math Selected Topics in Applied Mathematics – Computational Finance Andrey Itkin Course web page
Efficient Portfolios MGT 4850 Spring 2008 University of Lethbridge.
Probabilistic Models Value-at-Risk (VaR) Chance constrained programming – Min variance – Max return s.t. Prob{function≥target}≥α – Max Prob{function≥target}
CF-3 Bank Hapoalim Jun-2001 Zvi Wiener Computational Finance.
Financial Markets Prof. André Farber SOLVAY BUSINESS SCHOOL UNIVERSITÉ LIBRE DE BRUXELLES Saigon, April 2004.
Why attending this Program Sharpening the quantitative skills in   Pricing, hedging and risk measurement of derivative securities   Implementing risk.
© Natixis 2006 Hybrid Products David Besançon 27 mars 2008.
1 Down Place Hammersmith London UK 530 Lytton Ave. Palo Alto CA USA.
Presenter: Alexander Kaganov Supervisor: Paul Chow
Relevance of Simulation Models for Assessments of LivingLabs Activity University of Maribor Faculty of Organizational Sciences www
Ewa Lukasik - Jakub Lawik - Juan Mojica - Xiaodong Xu.
PicsouGrid Viet-Dung DOAN. Agenda Motivation PicsouGrid’s architecture –Pricing scenarios PicsouGrid’s properties –Load balancing –Fault tolerance Perspectives.
Estimating Market Value-at- Risk Using GPUs NVIDIA GPU Developer’s Summit, 2009 Matthew Dixon 1 Jike Chong 2 1 Department of Computer Science, UC Davis.
Structuring and Pricing Complex Credit Assets with Monte Carlo – Jörg Günther.
Practical analysis and valuation of heterogeneous telecom services Case-based analysis.
ORFE Princeton Preview Class 2016 Presented by Prof. Alain L. Kornhauser Department Representative Operations Research & Financial Engineering For more.
Irwin/McGraw-Hill 1 Market Risk Chapter 10 Financial Institutions Management, 3/e By Anthony Saunders.
The Oxford Guide to Financial Modeling by Ho & Lee Chapter 15. Risk Management The Oxford Guide to Financial Modeling Thomas S. Y. Ho and Sang Bin Lee.
Value at Risk: Market Risk Models Han Zhang Director, Head of Market Risk Analytics Corporate Market and Institutional Risk August 23, 2013 University.
Your Career Connects Our Focus! BSI Financial Group Suit 2700, 161 Bay Street 27 th Floor, TD Canada Trust Tower PO BOX 508 Toronto M5J.
1 Down Place Hammersmith London UK 530 Lytton Ave. Palo Alto CA USA.
Modern Portfolio Theory. History of MPT ► 1952 Horowitz ► CAPM (Capital Asset Pricing Model) 1965 Sharpe, Lintner, Mossin ► APT (Arbitrage Pricing Theory)
Are Supercomputers returning to Investment Banking?
Risk Management Exercises. Exercise Value at Risk calculations.
Valuation and Portfolio Risk Management with Mortgage- Backed Security.
Financial mathematics, 16/ , KTH Per-Olov Åsén, Risk Modeling and Quantitative Analysis.
Evaluating Natural Resource Investments: A Dynamic Option Simulation Approach Chung-Gee Lin Dept. of Business Mathematics Soochow University, Taiwan 2004.
Estimating Credit Exposure and Economic Capital Using Monte Carlo Simulation Ronald Lagnado Vice President, MKIRisk IPAM Conference on Financial Mathematics.
A Cursory Introduction to Real Options Andrew Brown 5/2/02.
Monte-Carlo Simulation. Mathematical basis The discounted price is a martingale (MA4257 and MA5248).
FIN 614: Financial Management Larry Schrenk, Instructor.
© The MathWorks, Inc. ® ® Monte Carlo Simulations using MATLAB Vincent Leclercq, Application engineer
Presentation 2: Objectives  To introduce you with the Principles of Investment Strategy and Modern Portfolio Theory  Topics to be covered  Indifference.
Generalized Model of Lockage Delay Based on Historic Data Michael R. Hilliard, Ph.D. Center for Transportation Analysis Oak Ridge National Laboratory Smart.
Kick-Off Meeting Tuesday, September 29, OUTLINE  Officer Board Introduction  About the Org  Events Planned  Speakers + Career Opportunities.
 Asset Models in Life Assurance - Views from the Stochastic Accreditation Working Party 17 November 2003 Faculty of Actuaries Sessional Meeting.
 Measures the potential loss in value of a risky asset or portfolio over a defined period for a given confidence interval  For example: ◦ If the VaR.
Monte Carlo Simulation Natalia A. Humphreys April 6, 2012 University of Texas at Dallas.
Stodder, Efficient Frontier Portfolio Optimization – Finding the Efficient Frontier Theory, and a Practical Example.
Credit Risk Losses and Credit VaR
Enterprise Risk Management An Introduction Frank Reynolds, Reynolds, Thorvardson, Ltd.
«Applied mathematics and information systems» Economics and Management Faculty.
The Applications of the Cholesky decomposition Teodora Aleksi ć, 391/2012.
Using Mind Genomics to identify the needs of the economy and to create better educational program ALEKSANDAR RAKIĆEVIĆ PhD student University of Belgrade.
Using the Efficient Frontier in DFA 2000 CAS Dynamic Financial Analysis Seminar New York Marriott Marquis, New York, NY, July 17-18, 2000 by William C.
Maxeler Technologies Multiscale Dataflow Technology solves mission-critical compute problems with 20-50x improvement in performance & energy Application.
Types of risk Market risk
The Three Common Approaches for Calculating Value at Risk
Risk Mgt and the use of derivatives
STUDY OF PARALLEL MONTE CARLO SIMULATION TECHNIQUES
Types of risk Market risk
High-accuracy PDE Method for Financial Derivative Pricing Shan Zhao and G. W. Wei Department of Computational Science National University of Singapore,
سياسات التوزيع الفصل السادس عشر.
William F. Sharpe STANCO 25 Professor of Finance
15. Topics not Covered.
Monte Carlo Simulation
Chapter 8 Risk and Required Return
Advanced Financial Modelling.
Department of Statistics & Actuarial Science
LO 5-1 Compute various measures of return on multi-year investments.
Presentation transcript:

Maxeler applications in financial engineering ALEKSANDAR RAKIĆEVIĆprof. BRATISLAV PETROVIĆ PhD studentMenthor University of Belgrade Faculty of Organizational Sciences (FON)

MAXELER FINANCIAL APPLICATIONS Interest rate curve fitting Pricing/valuation Model calibration Scenario analysis Risk analysis Portfolio optimization High frequency trading 2 / 7

THESE PROBLEMS ARE COMPUTATIONALLY EXPENSIVE Some intensive calculations: –expected values for trees based models with thousands of steps –correlation and covariance matrices for thousands of assets –multi variate Monte Carlo simulations with multiple payoffs –runing models for high frequency trading 3 / 7

Pricing CDOs 4 / 7

Risk analysis using Monte Carlo simulation 5 / 7

WHAT ARE THE EFFECTS OF MAXELER APPLICATIONS Example: Acceleration of complex credit derivatives calculations at JP Morgan Code transformation: 898 → 3696 (growth factor 4) Speedup: 220x (overnight → several min) Power usage: - 6% American Finance Technology Award (2011) 6 / 7

SYSTEM THEORY AND CONTROL LABORATORY Faculty of organizational sciences, Belgrade University Research area: –Modeling and control, –Optimization, –Computational inteligence, –Machine learning. Application domains: Finance and management. ALEKSANDAR RAKIĆEVIĆ CONTACT INFO: 7 / 7

Feedback: Privacy Policy Feedback
Do Not Sell My Personal Information
About project: SlidePlayer Terms of Service

© 2025 SlidePlayer.com. Inc. All rights reserved.