Deep Convolution Neural Network with Dropout in Modeling Exchange Rate Volatility

Samuel Wanjiru; Anthony Waititu; Anthony Wanjoya

doi:doi:10.11648/j.ijdsa.20220802.14

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Deep Convolution Neural Network with Dropout in Modeling Exchange Rate Volatility

Samuel Wanjiru, Anthony Waititu, Anthony Wanjoya

Published in International Journal of Data Science and Analysis (Volume 8, Issue 2)

Received: 5 March 2022 Accepted: 24 March 2022 Published: 31 March 2022

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Abstract

Exchange rate data possesses time-series features such as a trend. Based on a convolutional neural network (CNN) deep learning algorithm, which has the advantages of detecting patterns, extracting effective features, finding interdependence of time series data, and its computational efficiency, this paper proposes a convolutional neural network with dropout model-based approach to model and forecast exchange rates. In the meantime, this paper uses the CNN to first model and predict exchange rates and the corresponding results of this model are compared with those of the CNN-WD. The experimental results showed that the CNN-WD is superior to the CNN model in terms of the error value, fitting degree and training time. The dataset used for this research is that of daily exchange rates for the period between December 1, 2003, and October 15, 2021, which is comprised of 6528 daily trading observations. Adjusted closing rates are chosen. First, this paper adopts a CNN to effectively identify patterns and extract relevant data features of the exchange rate dataset, making use of the past 21 days. Dropout regularization is then adopted to help prevent the CNN model from overfitting data by temporarily removing a neuron from the network along with all its incoming and outgoing connections during training if its generated random value is below the set dropout rate. This paper further evaluates the reducibility and identifiability of the CNN-WD. As an application, this paper uses the CNN-WD to forecast the next month’s average tea price in Kenya.

Published in	International Journal of Data Science and Analysis (Volume 8, Issue 2)
DOI	10.11648/j.ijdsa.20220802.14
Page(s)	38-46
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Convolutional Neural Network (CNN), Dropout Regularization, Convolutional Neural Network with Dropout (CNN-WD)

References

[1]	D. Erdemlioglu, S. Laurent and C. J. Neely, "Econometric modeling of exchange rate volatility and jumps," in Handbook of Research Methods and Applications in Empirical Finance, Edward Elgar Publishing, 2013.
[2]	A. G. Halunga and C. D. Orme, "First-order asymptotic theory for parametric misspecification tests of GARCH models," Econometric Theory, vol. 25, p. 364–410, 2009.
[3]	B. Dickson, "What are convolutional neural networks (CNN)," URL: https://bdtechtalks.com/2020/01/06/convolutional-neural-networks-cnn-convnets, 2020.
[4]	W. Lu, J. Li, Y. Li, A. Sun and J. Wang, "A CNN-LSTM-based model to forecast stock prices," Complexity, vol. 2020, 2020.
[5]	R. F. Engle, "Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation," Econometrica: Journal of the econometric society, p. 987–1007, 1982.
[6]	T. Bollerslev, "Generalized autoregressive conditional heteroskedasticity," Journal of econometrics, vol. 31, p. 307–327, 1986.
[7]	R. Uppal and T. Wang, "Model misspecification and underdiversification," The Journal of Finance, vol. 58, p. 2465–2486, 2003.
[8]	W. Kristjanpoller, A. Fadic and M. C. Minutolo, "Volatility forecast using hybrid neural network models," Expert Systems with Applications, vol. 41, p. 2437–2442, 2014.
[9]	S. Galeshchuk, "Neural networks performance in exchange rate prediction," Neurocomputing, vol. 172, p. 446–452, 2016.
[10]	A. Borovykh, S. Bohte and C. W. Oosterlee, "Conditional time series forecasting with convolutional neural networks," arXiv preprint arXiv: 1703.04691, 2017.
[11]	I. E. Livieris, S. Stavroyiannis, E. Pintelas, T. Kotsilieris and P. Pintelas, "A dropout weight-constrained recurrent neural network model for forecasting the price of major cryptocurrencies and CCi30 index," Evolving Systems, p. 1–16, 2021.
[12]	A. W. Gichuhi, "Nonparametric changepoint analysis for bernoulli random variables based on neural networks," 2008.
[13]	J. G. Hwang and A. A. Ding, "Prediction intervals for artificial neural networks," Journal of the American Statistical Association, vol. 92, p. 748–757, 1997.
[14]	H. J. Sussmann, "Uniqueness of the weights for minimal feedforward nets with a given input-output map," Neural networks, vol. 5, p. 589–593, 1992.
[15]	G. Brassington, "Mean absolute error and root mean square error: which is the better metric for assessing model performance?," in EGU General Assembly Conference Abstracts, 2017.
[16]	K. Das and R. N. Behera, "A survey on machine learning: concept, algorithms and applications," International Journal of Innovative Research in Computer and Communication Engineering, vol. 5, p. 1301–1309, 2017.

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APA Style

Samuel Wanjiru, Anthony Waititu, Anthony Wanjoya. (2022). Deep Convolution Neural Network with Dropout in Modeling Exchange Rate Volatility. International Journal of Data Science and Analysis, 8(2), 38-46. https://doi.org/10.11648/j.ijdsa.20220802.14

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ACS Style

Samuel Wanjiru; Anthony Waititu; Anthony Wanjoya. Deep Convolution Neural Network with Dropout in Modeling Exchange Rate Volatility. Int. J. Data Sci. Anal. 2022, 8(2), 38-46. doi: 10.11648/j.ijdsa.20220802.14

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AMA Style

Samuel Wanjiru, Anthony Waititu, Anthony Wanjoya. Deep Convolution Neural Network with Dropout in Modeling Exchange Rate Volatility. Int J Data Sci Anal. 2022;8(2):38-46. doi: 10.11648/j.ijdsa.20220802.14

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@article{10.11648/j.ijdsa.20220802.14,
  author = {Samuel Wanjiru and Anthony Waititu and Anthony Wanjoya},
  title = {Deep Convolution Neural Network with Dropout in Modeling Exchange Rate Volatility},
  journal = {International Journal of Data Science and Analysis},
  volume = {8},
  number = {2},
  pages = {38-46},
  doi = {10.11648/j.ijdsa.20220802.14},
  url = {https://doi.org/10.11648/j.ijdsa.20220802.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20220802.14},
  abstract = {Exchange rate data possesses time-series features such as a trend. Based on a convolutional neural network (CNN) deep learning algorithm, which has the advantages of detecting patterns, extracting effective features, finding interdependence of time series data, and its computational efficiency, this paper proposes a convolutional neural network with dropout model-based approach to model and forecast exchange rates. In the meantime, this paper uses the CNN to first model and predict exchange rates and the corresponding results of this model are compared with those of the CNN-WD. The experimental results showed that the CNN-WD is superior to the CNN model in terms of the error value, fitting degree and training time. The dataset used for this research is that of daily exchange rates for the period between December 1, 2003, and October 15, 2021, which is comprised of 6528 daily trading observations. Adjusted closing rates are chosen. First, this paper adopts a CNN to effectively identify patterns and extract relevant data features of the exchange rate dataset, making use of the past 21 days. Dropout regularization is then adopted to help prevent the CNN model from overfitting data by temporarily removing a neuron from the network along with all its incoming and outgoing connections during training if its generated random value is below the set dropout rate. This paper further evaluates the reducibility and identifiability of the CNN-WD. As an application, this paper uses the CNN-WD to forecast the next month’s average tea price in Kenya.},
 year = {2022}
}

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TY  - JOUR
T1  - Deep Convolution Neural Network with Dropout in Modeling Exchange Rate Volatility
AU  - Samuel Wanjiru
AU  - Anthony Waititu
AU  - Anthony Wanjoya
Y1  - 2022/03/31
PY  - 2022
N1  - https://doi.org/10.11648/j.ijdsa.20220802.14
DO  - 10.11648/j.ijdsa.20220802.14
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 38
EP  - 46
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20220802.14
AB  - Exchange rate data possesses time-series features such as a trend. Based on a convolutional neural network (CNN) deep learning algorithm, which has the advantages of detecting patterns, extracting effective features, finding interdependence of time series data, and its computational efficiency, this paper proposes a convolutional neural network with dropout model-based approach to model and forecast exchange rates. In the meantime, this paper uses the CNN to first model and predict exchange rates and the corresponding results of this model are compared with those of the CNN-WD. The experimental results showed that the CNN-WD is superior to the CNN model in terms of the error value, fitting degree and training time. The dataset used for this research is that of daily exchange rates for the period between December 1, 2003, and October 15, 2021, which is comprised of 6528 daily trading observations. Adjusted closing rates are chosen. First, this paper adopts a CNN to effectively identify patterns and extract relevant data features of the exchange rate dataset, making use of the past 21 days. Dropout regularization is then adopted to help prevent the CNN model from overfitting data by temporarily removing a neuron from the network along with all its incoming and outgoing connections during training if its generated random value is below the set dropout rate. This paper further evaluates the reducibility and identifiability of the CNN-WD. As an application, this paper uses the CNN-WD to forecast the next month’s average tea price in Kenya.
VL  - 8
IS  - 2
ER  -

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Author Information

Samuel Wanjiru

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Anthony Waititu

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Anthony Wanjoya

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

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Plain Text BibTeX RIS

APA Style

Samuel Wanjiru, Anthony Waititu, Anthony Wanjoya. (2022). Deep Convolution Neural Network with Dropout in Modeling Exchange Rate Volatility. International Journal of Data Science and Analysis, 8(2), 38-46. https://doi.org/10.11648/j.ijdsa.20220802.14

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ACS Style

Samuel Wanjiru; Anthony Waititu; Anthony Wanjoya. Deep Convolution Neural Network with Dropout in Modeling Exchange Rate Volatility. Int. J. Data Sci. Anal. 2022, 8(2), 38-46. doi: 10.11648/j.ijdsa.20220802.14

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AMA Style

Samuel Wanjiru, Anthony Waititu, Anthony Wanjoya. Deep Convolution Neural Network with Dropout in Modeling Exchange Rate Volatility. Int J Data Sci Anal. 2022;8(2):38-46. doi: 10.11648/j.ijdsa.20220802.14

Copy | Download

@article{10.11648/j.ijdsa.20220802.14,
  author = {Samuel Wanjiru and Anthony Waititu and Anthony Wanjoya},
  title = {Deep Convolution Neural Network with Dropout in Modeling Exchange Rate Volatility},
  journal = {International Journal of Data Science and Analysis},
  volume = {8},
  number = {2},
  pages = {38-46},
  doi = {10.11648/j.ijdsa.20220802.14},
  url = {https://doi.org/10.11648/j.ijdsa.20220802.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20220802.14},
  abstract = {Exchange rate data possesses time-series features such as a trend. Based on a convolutional neural network (CNN) deep learning algorithm, which has the advantages of detecting patterns, extracting effective features, finding interdependence of time series data, and its computational efficiency, this paper proposes a convolutional neural network with dropout model-based approach to model and forecast exchange rates. In the meantime, this paper uses the CNN to first model and predict exchange rates and the corresponding results of this model are compared with those of the CNN-WD. The experimental results showed that the CNN-WD is superior to the CNN model in terms of the error value, fitting degree and training time. The dataset used for this research is that of daily exchange rates for the period between December 1, 2003, and October 15, 2021, which is comprised of 6528 daily trading observations. Adjusted closing rates are chosen. First, this paper adopts a CNN to effectively identify patterns and extract relevant data features of the exchange rate dataset, making use of the past 21 days. Dropout regularization is then adopted to help prevent the CNN model from overfitting data by temporarily removing a neuron from the network along with all its incoming and outgoing connections during training if its generated random value is below the set dropout rate. This paper further evaluates the reducibility and identifiability of the CNN-WD. As an application, this paper uses the CNN-WD to forecast the next month’s average tea price in Kenya.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - Deep Convolution Neural Network with Dropout in Modeling Exchange Rate Volatility
AU  - Samuel Wanjiru
AU  - Anthony Waititu
AU  - Anthony Wanjoya
Y1  - 2022/03/31
PY  - 2022
N1  - https://doi.org/10.11648/j.ijdsa.20220802.14
DO  - 10.11648/j.ijdsa.20220802.14
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 38
EP  - 46
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20220802.14
AB  - Exchange rate data possesses time-series features such as a trend. Based on a convolutional neural network (CNN) deep learning algorithm, which has the advantages of detecting patterns, extracting effective features, finding interdependence of time series data, and its computational efficiency, this paper proposes a convolutional neural network with dropout model-based approach to model and forecast exchange rates. In the meantime, this paper uses the CNN to first model and predict exchange rates and the corresponding results of this model are compared with those of the CNN-WD. The experimental results showed that the CNN-WD is superior to the CNN model in terms of the error value, fitting degree and training time. The dataset used for this research is that of daily exchange rates for the period between December 1, 2003, and October 15, 2021, which is comprised of 6528 daily trading observations. Adjusted closing rates are chosen. First, this paper adopts a CNN to effectively identify patterns and extract relevant data features of the exchange rate dataset, making use of the past 21 days. Dropout regularization is then adopted to help prevent the CNN model from overfitting data by temporarily removing a neuron from the network along with all its incoming and outgoing connections during training if its generated random value is below the set dropout rate. This paper further evaluates the reducibility and identifiability of the CNN-WD. As an application, this paper uses the CNN-WD to forecast the next month’s average tea price in Kenya.
VL  - 8
IS  - 2
ER  -

Copy | Download