Generalized ant programming in option pricing: determining implied volatilities based on American put options

Keber, Christian; Schuster, M.G.

doi:10.1109/cifer.2003.1196251

Cited by 12 publications

(8 citation statements)

References 33 publications

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“…Implied volatility measures the intrinsic dependence of past stock prices not only with time but with other factors affecting the price over a period of time. Keber and Schuster [17] used generalized ant programming to derive analytical approximations to determine the implied volatility for American put options. Generalized ant programming is a new method inspired by genetic programming approach introduced by Koza [19] and Ant Colony System (ACS) [9].…”

Section: Ant Colony Optimization (Aco)mentioning

confidence: 99%

Ant Colony Optimization to price exotic options

Kumar

Chadha

Thulasiram

et al. 2009

2009 IEEE Congress on Evolutionary Computation

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Option pricing is one of the challenging problems in finance. Finding the best time to exercise an option is a even more challenging problem, especially since the price of the underlying assets change rapidly. In this work, we study complex path dependent options by exploiting and extending a novel idea that we proposed earlier using a nature inspired meta-heuristic algorithm. Ant Colony Optimization (ACO). ACO has been used extensively in combinatorial optimization problems and recently in dynamic applications such as mobile ad-hoc networks where the objective is find a shortest path. However, in finance, especially in option pricing, we look for best time to exercise an option. Specifically, we use ants to decide on the best time to exercise so that the holder of the option contract will get the maximum benefit from his/her investment. Our algorithm and implementation suggests a better way to price options than traditional techniques such as Monte Carlo simulation or binomial lattice algorithm. Our pricing results compare very well with other techniques and at the same time the computational cost is reduced to a large extent.

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Section: Ant Colony Optimization (Aco)mentioning

confidence: 99%

Ant Colony Optimization to price exotic options

Kumar

Chadha

Thulasiram

et al. 2009

2009 IEEE Congress on Evolutionary Computation

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“…Implied volatility measures the intrinsic dependence of past stock prices not only with time but with other factors affecting the price over a period of time. Keber and Schuster [18] used generalized ant programming to derive analytical approximations to determine the implied volatility for American put options. Generalized ant programming is a new method inspired by genetic programming approach introduced by Koza [20] and Ant Colony System (ACS) [9].…”

Section: Ant Colony Optimization (Aco)mentioning

confidence: 99%

An Aggregated Ant Colony Optimization approach for pricing options

Udayshankar

Kumar

Jha

et al. 2009

2009 IEEE International Symposium on Parallel &Amp; Distributed Processing

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Estimating the current cost of an option by predicting the underlying asset prices is the most common methodology for pricing options. Pricing options has been a challenging problem for a long time due to unpredictability in market which gives rise to unpredictability in the option prices. Also the time when the options have to be exercised has to be determined to maximize the profits. This paper proposes an algorithm for predicting the time and price when the option can be exercised to gain expected profits.The proposed method is based on Nature inspired algorithm i.e. Ant Colony Optimization (ACO) which is used extensively in combinatorial optimization problems and dynamic applications such as mobile ad-hoc networks where the objective is to find the shortest path. In option pricing, the primary objective is to find the best node in terms of price and time that would bring expected profit to the investor. Ants traverse the solution space (asset price movements) in the market to identify a profitable node. We have designed and implemented an Aggregated ACO algorithm to price options which is distributed and robust. The initial results are encouraging and we are continuing this work further.

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“…Therefore, analytical approximations [Bharadia et al, 1995[Bharadia et al, , 1996Chance, 1996;Keber, 1999] are considered. Keber and Schuster [Keber and Schuster, 2003] showed through experimentation that their formula produces accurate approximation results and outperforms other approximations described in the literature.…”

Section: Heuristic Approaches For Option Pricingmentioning

confidence: 99%

“…Genetic programming (GP) [Chen et al, 1999;Chidambaran et al, 1999] approaches have been used to price options in the literature. Keber and Schuster [Keber and Schuster, 2003] used ideas from genetic programming and ant systems called generalized ant programming (GAP) to derive formulas for calculating implied volatility of the underlying asset of an American put option. Implied volatility is the volatility of an asset that is calculated using the value of the option.…”

Section: Heuristic Approaches For Option Pricingmentioning

confidence: 99%

A parallel Particle swarm optimization algorithm for option pricing

Prasain

Jha

Thulasiraman

et al. 2010

2010 IEEE International Symposium on Parallel &Amp; Distributed Processing, Workshops and PHD Forum (IPDPSW)

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Financial derivatives play significant role in an investor's success. Financial option is one form of derivatives. Option pricing is one of the challenging and fundamental problems of computational finance. Due to highly volatile and dynamic market con- ii Abstract iii In this research, we have designed a sequential PSO based option pricing algorithm using basic principles of PSO. The algorithm is applicable for both European and American options, and handles both constant and variable volatility. We show that our results for European options compare well with Black-Scholes-Merton formula.Since it is very important and critical to lock-in profit making opportunities in the real market, we have also designed and developed parallel algorithm to expedite the computing process.We evaluate the performance of our algorithm on a cluster of multicore machines that supports three different architectures: shared memory, distributed memory, and a hybrid architectures. We conclude that for a shared memory architecture or a hybrid architecture, one-to-one mapping of particles to processors is recommended for performance speedup. We get a speedup of 20 on a cluster of four nodes with 8 dual-core processors per node.

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Generalized ant programming in option pricing: determining implied volatilities based on American put options

Cited by 12 publications

References 33 publications

Ant Colony Optimization to price exotic options

Ant Colony Optimization to price exotic options

An Aggregated Ant Colony Optimization approach for pricing options

A parallel Particle swarm optimization algorithm for option pricing

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