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Jul 23
I have tested several R optimization functions before: nlm, optim(Nelder-Mead), optim(BFGS), optim(SANN), nlminb, optim (L-BFGS-B) for a eight-parameter Vasicek interest rate model, overall I find that for my setting, nlminb is the best and all R functions finish within seconds. For detail please read the old post at R optimization function test

Pat at Portfolio Probe recently had a wonderful test on some heuristic optimization methods, including simulated annealing, traditional genetic algorithm, evolutionary algorithms. By using R packages and functions - Rmalschains, GenSA, genopt, DEoptim, soma, rgenoud, GA, NMOF and SANN method of optim, he finds that the Rmalschains and GenSA packages are standing out. Nice one, original article is at A comparison of some heuristic optimization methods.
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Jun 27
This memo explains how to use the MATLAB code for estimating a Markov Regime Switching Model with time varying transition probabilities. The code is developed by Zhuanxin Ding based on the original code by Marcelo Perlin for estimating a Markov
Regime Switching Model
with constant transition probability matrix.

Click here for an introduction paper and Matlab codes are here.
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Jun 13
I am contacted by a reader to post this announcement, just in case you are interested, some good speakers.

Institute of Mathematics of the National Academy of Sciences in association with Yerevan State University and American University of Armenia is organizing a Workshop on Stochastic and PDE Methods in Financial Mathematics in September 7 - 12, 2012 to be held in Yerevan, Armenia.

The program of the workshop will consist of invited 50 minutes plenary lectures and contributed 20 minutes talks, poster sessions as well as short presentations.

Scientific Committee: Rama Cont (Universite Paris VI-VII, France), Levon Goukasian (Pepperdine University, USA), Walter Schachermayer (University of Vienna, Austria), Henrik Shahgholian (KTH, Sweden), Johan Tysk (Uppsala University, Sweden)

Organizing Committee: A. Hakobyan (YSU, Armenia), M. Poghosyan (YSU, Armenia), R. Barkhudaryan (Institute of Mathematics, Armenia), A. Hajian (AUA, Armenia)

Main Speakers: Amel Bentata (Universite Pierre et Marie Curie (P6), France),  Rama Cont (Universite Paris VI-VII, France), Boualem Djehiche (KTH, Sweden), Diogo Gomes (Instituto Superior Tecnico, Portugal), Dmitry Kramkov (Carnegie Mellon University, USA), Michael Mania (A. Razmadze Mathematical Institute, Georgia), Peter Markowich (University of Cambridge, UK and University of Vienna, Austria), Aleksandar Mijatovic (University of Warwick, UK), George Papanicolaou (Stanford University, USA), Andrea Pascucci (Universita di Bologna, Italia), Huyen Pham (University Paris Diderot (Paris 7), IUF, France), Camelia Pop (Rutgers University, USA), Walter Schachermayer (University of Vienna, Austria), Henrik Shahgholian (KTH, Sweden), Halil Mete Soner (ETH Zürich, Switzerland), Josef Teichmann (ETH Zurich, Switzerland), Nizar Touzi (Ecole Polytechnique, France), Thaleia Zariphopoulou (Oxford-Man Institute of Quantitative Finance, UK)
Jun 12
General publication strategies: advice on paper publication, especially for early stage researchers.

New Book Fore­cast­ing: prin­ci­ples and practice: a free online book on forecasting with a fore­cast pack­age for R by Rob J Hyn­d­man and George Athana­sopou­los.

It’s All in the Timing: Simple Active Portfolio Strategies that Outperform Naïve Diversification: we develop 2 new methods of mean-variance portfolio selection (volatility timing and reward-to-risk timing) that deliver portfolios characterized by low turnover. These timing strategies outperform naïve diversification even in the presence of high transaction costs.

Option pricing models implemented in AirXCell: an online R application framework currently supporting a programmable spreadsheet, an R development environment and various financial calculation forms.

A Dynamic Copula Approach to Recovering the Index Implied Volatility Skew: We show that moderate tail dependence coupled with asymmetric correlation response to negative news is essential to explain the index implied volatility skew. Standard dynamic correlation models with zero tail dependence fail to generate a sufficiently steep implied volatility skew.
Jun 10
AirXCell is an online R application framework currently supporting a programmable spreadsheet, an R development environment and various financial calculation forms.

A new calculation form has been implemented recently within AirXCell for financial option pricing (option valuation). The option pricer within AirXCell enables the user to compute theoretical option prices. It already offers an extended set of basic and exotic models (about a dozen) than enables the user to price a wide range of option types:

American options,
European options,
Asian options,
Barrier options,
Binary options,
Currency translated options,
Lookback options,
Multiple assets options and
Multiple exercises options

Many more models are being implemented currently and will be added soon to AirXCell. In addition to the option pricing form, there are other forms especially useful in the same context that provides ways to load asset prices, visualize them, compute the theoretical and historical volatility.

This form is very valuable to quantitative researchers or any finance professional who needs to compute theoretical option prices easily and who is looking for a reliable option pricer.

The Option pricing form presents the user with an HTML form enabling her to set up the model with the required parameters values such as the underlying asset price, the strike price, the volatility of the underlying asset, etc.

For instance, the following form is presented to a user requesting the price of an european option using the Generalized Black Scholes model:

Again, there are many more models and option types coming soon as well as other forms for various other kind of calculations, still mostly oriented towards financial calculation.
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