Having recently completed an online course in financial engineering from the university of Columbia (https://www.coursera.org/learn/financial-engineering-2/home/welcome) I was interested by how to it would be possible to begin to build models of more complex financial derivatives. One such example, perhaps for its infamous reputation was the collaterized debt obligation (CDO). In particular how construction of these can affect the end mechanics of such products. I performed the modelling in Mathworks MATLAB. As CDO’s are themselves an object it makes sense to approach this with an OOP approach, creating a class for CDO’s, and instantizing each time we want to construct a CDO.

Simulink documentation is short on examples that are (a) simple and (b) do not come from the engineering domain. We present one that meets both criteria and so may aid another non-engineer curious about Simulink.The models presented in this submission are motivated by a topical, if hardly important :), question. Chartered Financial Analyst (CFA) is an esteemed professional certification in the field of finance. The CFA program includes three examinations, or levels; to qualify for the CFA charter, a candidate must complete Levels I-III, one examination per year, possibly skipping years and retaking failed examinations. A CFA charterholder may be proud of completing the program on first attempt, i.e. without retaking any examination. What is the actual weight of such an accomplishment, as suggested by the fraction of CFA charterholders with a 'first-time pass' (FTP)? The statistic is not reported, and relevant data is limited to pass rates for Levels I-III: 40, 40 and 50 per cent respectively, taken to be constant through time.We focus on drop-out behavior of candidates as the parameter of interest: intuitively, if everyone who fails an examination abandons the program, any charterholder must be an FTP, whereas hordes of repeat test takers ought to make a first-time pass rare. We make several additional assumptions, notably, that (a) candidates taking an examination for the first time and those retaking it have the same chances of passing and dropping out if failed; (b) a candidate failing an exam re-takes it at first opportunity, i.e. next year; (c) the drop-out rate is constant for Levels I-III. (We may fix it at 50%, for example). Please make a guess, and proceed to the files. Do let me know if you spot an error, or scope for better design!

Portfolio optimization is a mathematical approach that provides a trade-off between expected profit and risk and commonly used to make investment decisions across a collection of financial assets. This submission quickly reviews the concept of portfolio optimization and presents an application of this approach in energy systems.Acknowledgements: This submission is developed for 2060 Project at the Institute for Integrated Energy Systems at the University of Victoria, Canada. The project has been funded by Pacific Institute for Climate Solutions, Natural Resources Canada and Natural Sciences and Engineering Research Council of Canada. This funding is gratefully acknowledged. Image courtesy of jscreationzs at FreeDigitalPhotos.net.

From a general point of view, the process of making something better is optimization. If we have a function f(x), in optimization, we want to find an argument x whose relevant cost is optimum.Most nonlinear optimization problems that appear in different areas of engineering, science and management cannot be analytically solved. Different methods and interesting optimization techniques have widely emerged and many of their successful applications have been reported, e.g., music composition, ﬁnancial forecasting, aircraft design, job-shop scheduling and drug design. Evolutionary Algorithms (EAs) are more successful than other optimization techniques. In this group, the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Harmony Search Algorithm (HSA) are applied to solve the optimization problems within the context of expensive optimization.A novel optimization algorithm called Hyper-Spherical Search (HSS) algorithm is proposed to solve the non-linear mixed integer optimization problems. Like other evolutionary algorithms, the proposed algorithm starts with an initial population. Population individuals are of two types: particles and hyper-sphere centers that all together form particle sets. Searching the hyper-sphere inner space made by the hyper-sphere center and its particle is the basis of the proposed evolutionary algorithm. The HSS algorithm hopefully converges to a state at which there exists only one hyper-sphere center (SC) and its particles are at the same position and have the same cost function value as the hyper-sphere center. Applying the proposed algorithm to some benchmark cost functions, shows its ability in dealing with different types of optimization problems.This is the paper published in Neural Computing and Application Journal:H. Karami, M. J. Sanjari, G. B. Gharehpetian, "Hyper-Spherical Search (HSS) Algorithm: A Novel Meta-heuristic Algorithm to Optimize Nonlinear Functions", Neural Computing and Applications, Vol. 25, issue: 6, pp. 1455-1465, 2014Link: http://link.springer.com/article/10.1007/s00521-014-1636-7And this is one example of its application in a problem in the field of electrical engineering:M.J. Sanjari, H.Karami, A.H. Yatim, G.B.Gharehpetian, "Application of Hyper-Spherical Search algorithm for optimal energy resources dispatch in residential microgrids", Applied Soft Computing (Elsevier), Volume 37, pp. 15–23, 2015Link: http://www.sciencedirect.com/science/article/pii/S1568494615005001All of the source codes and extra information can be found in my personal website at http://hkarami.com/

This toolbox contains the functions which can be used to simulate some of the well-known fractional order chaotic systems, such as:-Chen's system,-Arneodo's system,-Genesio-Tesi's system,-Lorenz's system,-Newton-Leipnik's system,-Rossler's system,-Lotka-Volterra system,-Duffing's system,-Van der Pol's oscillator,-Volta's system,-Lu's system,-Liu's system,-Chua's systems,-Financial system,-3 cells CNN.The functions numerically compute a solution of the fractional nonlinear differential equations, which describe the chaotic system. Each function returns the state trajectory (attractor) for total simulation time. For more details see book:Ivo Petras, Fractional-Order Nonlinear Systems: Modeling, Analysis and Simulation, Springer, Series: Nonlinear Physical Science, 2011, ISBN 978-3-642-18100-9.http://www.springer.com/engineering/control/book/978-3-642-18100-9or Chinese edition:Higher Education Press, Series: Nonlinear Physical Science, 2011, ISBN 978-7-04-031534-9.http://academic.hep.com.cn/im/CN/book/978-7-04-031534-9Zentralblatt MATH Database review:http://www.zentralblatt-math.org/portal/en/zmath/en/search/?q=an:05851602&type=pdf&format=complete

Applying the inverse transform method to the normal distribution entails evaluation of the inverse normal. This is the Beasley-Springer-Moro algorithm for approximating the inverse normal.Input: u, a sacalar or matrix with elements between 0 and 1 Output: x, an approximation for the inverse normal at uReference: Pau Glasserman, Monte Carlo methods in financial engineering, vol. 53 of applications of Mathematics (New York), Springer-Verlag, new York, 2004, p.67-68

In the age of computerized trading, financial services companies and independent traders must quickly develop and deploy dynamic technical trading systems. The technical trader's toolbox includes three forms of our new trading system based on dynamic technical analysis and a set of functions for graphical presentation, performance calculation and optimization of trading systems. The risk of trading systems is determined by applying Probability of Ruin Analysis. MATLAB's vast built-in mathematical and financial functionality along with the fact that is both an interpreted and compiled programming language make this technical trader's toolbox easily extendable by adding new complicated systems with minimum programming effort.The theoretical work has been published in Financial Engineering News Journal May/June 2003.Instructions setup:1.Load Matlab 2.Double click FintradeTool.mat from directory TechTradeTool3.In the directory TechTradeTool you should enter Run in the command window 4. Sample data are provided in sub-directory data