Portfolio Optimization: A Technical Perspective - GAMS

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30 30 <strong>GAMS</strong> & Grid Computing • Scalable: – support of massive grids, but also – multi-cpu / multiple cores desktop machines – “1 CPU - Grid” • Platform independent • Only minor changes to model required • Separation of model and solution method Model stays maintainable
31 31 Simple Serial Solve Loop Loop(p(pp), ret.fx = rmin +(rmax-rmin) /(card(pp)+1)*ord(pp) ; Solve minvar min var using miqcp; xres(i,p) = x.l(i); report(p,i,'inc') = xi.l(i); report(p,i,'dec') = xd.l(i) ); Loop How do we get to parallel and distributed computing? Generation Solution Update
Page 1 and 2: 1 1 Portfolio Optimization: A Techn
Page 3 and 4: 3 3 Agenda Introduction Mathematica
Page 5 and 6: 5 5 Agenda Introduction Mathematica
Page 7 and 8: 7 7 Portfolio Optimization Models -
Page 9 and 10: 9 9 Modeling Approaches - Programmi
Page 11 and 12: 11 11 MV Model Algebra Variance of
Page 13 and 14: 13 13 Modeling Issues Basic MV-Mode
Page 15 and 16: 15 15 More Complex Business Rules R
Page 17 and 18: 17 17 GAMS Formulation Variables xi
Page 19 and 20: 19 19 Efficient Frontier and Portfo
Page 21 and 22: 21 21 Stochastic Programming (SP) S
Page 23 and 24: 23 23 Challenges Deterministic equi
Page 25 and 26: 25 25 More Theory and Templates The
Page 27 and 28: 27 27 Grid Computing loop(pp, #calc
Page 29: 29 29 Advantages of Grid Computing
Page 33 and 34: 33 33 Results for 4096 MIPS on Cond
Page 35 and 36: 35 35 The End Thank you! … Questi

Portfolio Optimization: A Technical Perspective - GAMS

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