A new hybrid parametric and machine learning model with homogeneity hint for European-style index option pricing

Das, Shom Prasad; Padhy, Sudarsan

doi:10.1007/s00521-016-2303-y

Cited by 31 publications

(14 citation statements)

References 27 publications

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“…On the other hand, methods such as extreme learning machines and support vector regressions are mainly nonparametric models, with which financial predictions can be done. Das and Padhy [91] suggest a combination of parametric and nonparametric methods mentioned here in order to increase the predictive power of option pricing models. Here, the hybrid mode superiority is due to return distributions being nonnormal and the need for adaptive learning, which arises from the extreme learning machines method.…”

Section: Discussionmentioning

confidence: 99%

“…Questions Related studies Gap 1-; there is a need to focus on the future of artificial neural networks in stock market prediction models in developing countries RQ1: to integrate the findings of the previous research in the field of artificial intelligence in stock market prediction models in different parts of the world, with more focus on the developing economies [10,19,22,26,60,[118][119][120][121][122][123][124][125][126][127][128][129][130][131] Gap 2: there is a much wider scope in the field of artificial intelligence to implement other methodological approaches; for instance, conducting a case study or a survey or an experiment using a hybrid model can further help in the development of more generalized models of stock prediction RQ2: to adopt other methodological approaches involving a case study, an experiment, or a survey Experiments: [11,29,75,91,97] Survey: [96,114] Case study: [91,[132][133][134][135][136] Gap 3: the scope of research lies in choosing the optimal algorithm as the solution. A combination of other themes may provide better insights and models for stock market prediction.…”

Section: Gapsmentioning

confidence: 99%

“…RQ3: to conduct more research using a combination of other themes for stock market prediction, also because the existing artificial neural networks have not been able to provide effective results [137] and this calls for more advanced techniques such as neuro-fuzzy methods, genetic algorithm, option pricing models, machine learning techniques, and component analysis models Neuro-fuzzy systems: [104,105,[138][139][140][141]] Genetic algorithm and linear representation methods: [28,93,[142][143][144][145][146][147][148]] Linear regression models: [21,116,122,149] Option pricing model, machine learning techniques and hybrid combinations with neural networks: [34,91,[150][151][152][153] Component analysis model: [22,46,62,137,154] Gap4: more studies are required to maintain consistency throughout the time period since the inception of these three topics, viz., AI, neural networks, and stock market forecasting RQ4: the majority of the study is concentrated on longer periods. Very few studies have conducted an experiment or a case study on the trading prices spread over the previous 6 months or less [30,38,50,75,81,91,95,98,…”

Section: Gapsmentioning

confidence: 99%

See 2 more Smart Citations

Sailing through the COVID-19 Crisis by Using AI for Financial Market Predictions

Sharma

Erkut

Jain

et al. 2020

Mathematical Problems in Engineering

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The outbreak of COVID-19 has brought the world to an unprecedented position where financial and mental resources are drying up. Livelihoods are being lost, and it is becoming tough to save lives. These are the times to think of unprecedented solutions to the financial challenges being faced. Artificial intelligence (AI) has provided a fresh approach to finance through its implementation in the prediction of financial market prices by promising more generalizable results for stock market forecasting. Immense literature has attempted to apply AI and machine learning for predicting stock market returns and volatilities. The research on the applications of AI in finance lacks a consolidated overview of different research directions, findings, methodological approaches, and contributions. Therefore, there is a need to consolidate the extant literature in this upcoming field to consolidate the findings, identify the research gaps in the existing literature, and set a research agenda for future researchers. This paper addresses this need by synthesizing the extant literature in the form of a systematic review for addressing the use of AI in stock market predictions and interpreting the results in a narrative review. The gap formed through this article is the use of a combination of AI as a subject with the neural network as another area and stock market forecasting as another theme, and it will pave the way for future research studies. The analyses help highlight four important gaps in the existing literature on the subject.

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Section: Discussionmentioning

confidence: 99%

Section: Gapsmentioning

confidence: 99%

Section: Gapsmentioning

confidence: 99%

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Sailing through the COVID-19 Crisis by Using AI for Financial Market Predictions

Sharma

Erkut

Jain

et al. 2020

Mathematical Problems in Engineering

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“…Fernández et al (2013) used Monte Carlo simulations to calibrate the parameters for static and dynamic Stochastic Alpha, Beta, Rho models. The probability of optimal usage of hybridisation of parametric option pricing models such as the Black–Scholes–Merton and Monte Carlo Simulation versus nonparametric machine learning models has been tested by Das and Padhy (2017).…”

Section: Findings and Discussionmentioning

confidence: 99%

Research in market-calibrated option pricing analysis

Singh

Bhardwaj

Sharma

et al. 2019

QRFM

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Purpose This paper aims to consolidate and review the literature in the field of market-calibrated option pricing analysis. By doing so, the paper brings out the gaps in the extant literature and makes suggestions for future researchers in the field. Design/methodology/approach The methodology used in this research is inspired by the works of Ferreira et al. (2016), Jabbour (2013), Lage Junior and Godinho Filho (2010), Seuring (2013) and Sharma et al. (2018). A total of 1,500 papers written on the pricing of options globally are collated from the Web of Science ranging across 2010-2018. Findings Most of the research papers present mathematical proposals to value options; without calibrating it with real market data points. The authors bring out five important gaps in the extant literature. Originality/value This is arguably the first study that consolidates the literature in the field of market calibrated option pricing analysis with a view to suggest directions for future researchers.

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“…Contrary to Andreou et al (2010) and Das and Padhy (2017), we use these variables as the input of the Black-Scholes model for two reasons. First, our model aims to solve the canonical option valuation problem and our approach is the same as those in the traditional finance literature.…”

Section: Model Design Under Constraintsmentioning

confidence: 99%

Option valuation under no-arbitrage constraints with neural networks

Cao

Liu

Zhai

2021

European Journal of Operational Research

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In this paper, we start from the no-arbitrage constraints in option pricing and develop a novel hybrid gated neural network (hGNN) based option valuation model. We adopt a multiplicative structure of hidden layers to ensure model differentiability. We also select the slope and weights of input layers to satisfy the no-arbitrage constraints. Meanwhile, a separate neural network is constructed for predicting option-implied volatilities. Using S&P 500 options, our empirical analyses show that the hGNN model substantially outperforms well-established alternative models in the out-of-sample forecasting and hedging exercises. The superior prediction performance stems from our model's ability in describing options on the boundary, and in offering analytical expressions for option Greeks which generate better hedging results.

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A new hybrid parametric and machine learning model with homogeneity hint for European-style index option pricing

Cited by 31 publications

References 27 publications

Sailing through the COVID-19 Crisis by Using AI for Financial Market Predictions

Sailing through the COVID-19 Crisis by Using AI for Financial Market Predictions

Research in market-calibrated option pricing analysis

Option valuation under no-arbitrage constraints with neural networks

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