Quantitative Finance Collector is a blog on Quantitative finance analysis, methods in mathematical finance focusing on derivative pricing, quantitative trading and quantitative risk management.
Mar
9
Stochastic volatility models, specifically, Heston model, SABR model, are introduced before and become the widely used among academia and industry. However, the calibration process is difficult because generally the pricing requires numerical integration, and calibration requires to find five and eight parameters instead of only one for Black Scholes model.
Found a paper Calibrating Option Pricing Models with Heuristics, where the author look into the calibration of Heston (1993) and Bates (1996) models. Finding parameters that make the models consistent with market prices means solving a non-convex optimisation problem. Optimisation heuristics is suggested for this issue, more specifically they show that Differential Evolution and Particle Swarm Optimisation are both able to give good solutions to the problem.
Take a look if you are interested, in the Appendix the R and Matlab codes are given for a better understanding. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1566975
Found a paper Calibrating Option Pricing Models with Heuristics, where the author look into the calibration of Heston (1993) and Bates (1996) models. Finding parameters that make the models consistent with market prices means solving a non-convex optimisation problem. Optimisation heuristics is suggested for this issue, more specifically they show that Differential Evolution and Particle Swarm Optimisation are both able to give good solutions to the problem.
Take a look if you are interested, in the Appendix the R and Matlab codes are given for a better understanding. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1566975
May
28
Modern portfolio optimization started with Markowitz Efficient Frontier, Heuristic search and optimization is a new approach for solving complex problems that overcomes many shortcomings of traditional optimization techniques. Heuristic optimization techniques are general purpose methods that are very flexible and can be applied to many types of objective functions and constraints, especially where the objective function is non-convex and has many local minima. This is in particular the case when the risk is expressed as VaR, expected shortfall, Omega, maximum loss etc., and when the future returns of the individual assets are modelled as scenarios.
An interesting paper "A Data-Driven Optimization Heuristic for Downside Risk Minimization" demonstrates how to apply Heuristic optimization method under constraint of downside risk, code can be downloaded at http://comisef.eu/?q=resources_data_driven_opt, take a look if interested.
An interesting paper "A Data-Driven Optimization Heuristic for Downside Risk Minimization" demonstrates how to apply Heuristic optimization method under constraint of downside risk, code can be downloaded at http://comisef.eu/?q=resources_data_driven_opt, take a look if interested.
