An Open Innovation Intraday Implied Volatility for Pricing Australian Dollar Options

Le, Thi; Hoque, Ariful; Hassan, Kamrul

doi:10.3390/joitmc7010023

Cited by 5 publications

(7 citation statements)

References 63 publications

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“…Recently, a great deal of research in forecasting forex and stock market prices has been undertaken [39][40][41][42][43]. Kang et al, 2019, proposed Generative Adversarial Network architecture with Long Short-Term Memory (LSTM) as a generator and Multi-Layer Perceptron (MLP) as a discriminator.…”

Section: Related Workmentioning

confidence: 99%

A New Stock Price Forecasting Method Using Active Deep Learning Approach

Alkhatib

Khazaleh

Alkhazaleh

et al. 2022

Journal of Open Innovation: Technology, Market, and Complexity

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Stock price prediction is a significant research field due to its importance in terms of benefits for individuals, corporations, and governments. This research explores the application of the new approach to predict the adjusted closing price of a specific corporation. A new set of features is used to enhance the possibility of giving more accurate results with fewer losses by creating a six-feature set (that includes High, Low, Volume, Open, HiLo, OpSe), rather than the traditional four-feature set (High, Low, Volume, Open). The study also investigates the effect of data size by using datasets (Apple, ExxonMobil, Tesla, Snapchat) of different sizes to boost open innovation dynamics. The effect of the business sector in terms of the loss result is also considered. Finally, the study included six deep learning models, MLP, GRU, LSTM, Bi-LSTM, CNN, and CNN-LSTM, to predict the adjusted closing price of the stocks. The six variables used (High, Low, Open, Volume, HiLo, and OpSe) are evaluated according to the model’s outcome, showing fewer losses than the original approach, which utilizes the original feature set. The results show that LSTM-based models improved using the new approach, even though all models showed a comparative result wherein no model showed better results or continuously outperformed other models. Finally, the added new features positively affected the prediction models’ performance.

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Section: Related Workmentioning

confidence: 99%

A New Stock Price Forecasting Method Using Active Deep Learning Approach

Alkhatib

Khazaleh

Alkhazaleh

et al. 2022

Journal of Open Innovation: Technology, Market, and Complexity

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“…Predicting the values of currency exchanges and stock markets has been the subject of a large number of studies [19][20][21][22][23] in recent years. In [24], a generative adversarial network architecture is described, and LSTM is recommended for use as a generator.…”

Section: Background Of the Studymentioning

confidence: 99%

Framework for Predicting and Modeling Stock Market Prices Based on Deep Learning Algorithms

Aldhyani

Alzahrani

2022

Electronics

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The creation of trustworthy models of the equities market enables investors to make better-informed choices. A trading model may lessen the risks that are connected with investing and make it possible for traders to choose companies that offer the highest dividends. However, due to the high degree of correlation between stock prices, analysis of the stock market is made more difficult by batch processing approaches. The prediction of the stock market has entered a technologically advanced era with the advent of technological marvels such as global digitization. For this reason, artificial intelligence models have become very important due to the continuous increase in market capitalization. The novelty of the proposed study is the development of the robustness time series model based on deep leaning for forecasting future values of stock marketing. The primary purpose of this study was to develop an intelligent framework with the capability of predicting the direction in which stock market prices will move based on financial time series as inputs. Among the cutting-edge technologies, artificial intelligence has become the backbone of many different models that predict the direction of markets. In particular, deep learning strategies have been effective at forecasting market behavior. In this article, we propose a framework based on long short-term memory (LSTM) and a hybrid of a convolutional neural network (CNN-LSTM) with LSTM to predict the closing prices of Tesla, Inc. and Apple, Inc. These predictions were made using data collected over the past two years. The mean squared error (MSE), root mean squared error (RMSE), normalization root mean squared error (NRMSE), and Pearson’s correlation (R) measures were used in the computation of the findings of the deep learning stock prediction models. Between the two deep learning models, the CNN-LSTM model scored slightly better (Tesla: R-squared = 98.37%; Apple: R-squared = 99.48%). The CNN-LSTM model showed a superior performance compared with the single deep learning LSTM and existing systems in predicting stock market prices.

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“…Specifically, the predictive power of implied volatility smirk to forecast foreign exchange return is investigated. In Le et al (2021), the intraday implied volatility for pricing currency options is studied.…”

Section: Introductionmentioning

confidence: 99%

Pricing Options with Vanishing Stochastic Volatility

Mastroeni

2022

Risks

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In the past years, there has been an extensive investigation of the class of stochastic volatility models for the evaluation of options and complex derivatives. These models have proven to be extremely useful in generalizing the classic Black–Scholes economy and accounting for discrepancies between observation and predictions in the simple log-normal, constant-volatility model. In this paper, we study the structure of an options market with a stochastic volatility that will eventually vanish (i.e., reaches zero) for very short periods of time with probability of one. We investigate the form of pricing measures in this situation, first in a simple binomial case, and then for a diffusion model, by constructing a weak approximation in discrete space and continuous time. The market described allows fleeting arbitrage opportunities, since a vanishing volatility prevents the construction of an equivalent measure, so that pricing contingent claims are, a priori, not obvious. Nevertheless, we can still produce a fair pricing equation. Let us note that this issue is not only of theoretical relevance, as the phenomenon of very low volatility has indeed been observed in the financial markets and the economy for quite a long time in the recent past.

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An Open Innovation Intraday Implied Volatility for Pricing Australian Dollar Options

Cited by 5 publications

References 63 publications

A New Stock Price Forecasting Method Using Active Deep Learning Approach

A New Stock Price Forecasting Method Using Active Deep Learning Approach

Framework for Predicting and Modeling Stock Market Prices Based on Deep Learning Algorithms

Pricing Options with Vanishing Stochastic Volatility

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