Computational methods in finance: option pricing

Barucci, Emilio; Landi, Leonardo; Cherubini, Umberto

doi:10.1109/99.486762

Cited by 11 publications

(6 citation statements)

References 18 publications

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“…They have an ability to learn through trial and error where neurons adjust their weights of input variables to model the behavior or patterns of output levels. These networks have been employed to solve a variety of financial problems, including bank failure prediction (Tam and Kiang, 1992), stock picking (Kryzanowski et al, 1993), option pricing (Barucci et al, 1996), and management fraud detection (Eining et al, 1997).…”

Section: Using Probability Modelsmentioning

confidence: 99%

Digital Liability Risk: A Note on Estimating Exposure, Costs, and Implications

Raschke¹,

Lee²,

Charron³

et al. 2020

JBAFP

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Digital liabilities are the unknown future costs that occur after an event related to digital assets threatens organizational value. These events emerge from: (1) an IT data breach or cybersecurity failure; (2) IT infrastructure limitations that limit future opportunities; and (3) changes in business models that are limited due to IT infrastructure. Potential digital liabilities are not fully understood and can be difficult to quantify. Derived from prior research, this research note proposes four methods, modified from existing research literature, for estimating the cost of digital liabilities prior to a digital asset compromise. We conclude the research note by discussing opportunities for future research in this area.

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Section: Using Probability Modelsmentioning

confidence: 99%

Digital Liability Risk: A Note on Estimating Exposure, Costs, and Implications

Raschke¹,

Lee²,

Charron³

et al. 2020

JBAFP

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“…The next section gives a detailed explanation on one of them, the binomial lattice approach [6] which is used for the evaluation of the current study. 1 The author is at Indian Institute of Technology, Kanpur, U.P., India Email: gitikac@iitk.ac.in 2 Author for correspondence…”

Section: Introductionmentioning

confidence: 99%

“…Neural networks [1], [16] have been trained according to the Black-Scholes model and the results obtained from the neural networks are compared to the calculated results obtained from Black-Scholes formula. Other works involved genetic algorithms [5] followed by genetic programming (GP) [19], an extension of genetic algorithms.…”

Section: Introductionmentioning

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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“…Since 1 Author for correspondence the American options provides added flexibility, their premium is generally higher than European options. These two generally comprise vanilla options due to their simpler nature.…”

Section: Introductionmentioning

confidence: 99%

“…Neural networks [17], [1] have been trained according to the Black-Scholes model and the results obtained from the neural networks are compared to the calculated results obtained from Black-Scholes formula. Other works have involved genetic algorithms [5] followed by genetic programming (GP) [20], an extension of genetic algorithms.…”

Section: Introductionmentioning

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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Computational methods in finance: option pricing

Cited by 11 publications

References 18 publications

Digital Liability Risk: A Note on Estimating Exposure, Costs, and Implications

Digital Liability Risk: A Note on Estimating Exposure, Costs, and Implications

Ant Colony Optimization to price exotic options

An Aggregated Ant Colony Optimization approach for pricing options

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