Purpose

This study constructs time series model, artificial neural networks (ANNs) and statistical topologies to examine the volatility and forecast foreign exchange rates. The Mauritian forex market has been utilized as a case study, and daily data for nominal spot rate (during a time period of five years spanning from 2014 to 2018) for EUR/MUR, GBP/MUR, CAD/MUR and AUD/MUR have been applied for the predictions.

Design/methodology/approach

Autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH) models are used as a basis for time series modelling for the analysis, along with the non-linear autoregressive network with exogenous inputs (NARX) neural network backpropagation algorithm utilizing different training functions, namely, Levenberg–Marquardt (LM), Bayesian regularization and scaled conjugate gradient (SCG) algorithms. The study also features a hybrid kernel principal component analysis (KPCA) using the support vector regression (SVR) algorithm as an additional statistical tool to conduct financial market forecasting modelling. Mean squared error (MSE) and root mean square error (RMSE) are employed as indicators for the performance of the models.

Findings

The results demonstrated that the GARCH model performed better in terms of volatility clustering and prediction compared to the ARIMA model. On the other hand, the NARX model indicated that LM and Bayesian regularization training algorithms are the most appropriate method of forecasting the different currency exchange rates as the MSE and RMSE seemed to be the lowest error compared to the other training functions. Meanwhile, the results reported that NARX and KPCA–SVR topologies outperformed the linear time series models due to the theory based on the structural risk minimization principle. Finally, the comparison between the NARX model and KPCA–SVR illustrated that the NARX model outperformed the statistical prediction model. Overall, the study deduced that the NARX topology achieves better prediction performance results compared to time series and statistical parameters.

Research limitations/implications

The foreign exchange market is considered to be instable owing to uncertainties in the economic environment of any country and thus, accurate forecasting of foreign exchange rates is crucial for any foreign exchange activity. The study has an important economic implication as it will help researchers, investors, traders, speculators and financial analysts, users of financial news in banking and financial institutions, money changers, non-banking financial companies and stock exchange institutions in Mauritius to take investment decisions in terms of international portfolios. Moreover, currency rates instability might raise transaction costs and diminish the returns in terms of international trade. Exchange rate volatility raises the need to implement a highly organized risk management measures so as to disclose future trend and movement of the foreign currencies which could act as an essential guidance for foreign exchange participants. By this way, they will be more alert before conducting any forex transactions including hedging, asset pricing or any speculation activity, take corrective actions, thus preventing them from making any potential losses in the future and gain more profit.

Originality/value

This is one of the first studies applying artificial intelligence (AI) while making use of time series modelling, the NARX neural network backpropagation algorithm and hybrid KPCA–SVR to predict forex using multiple currencies in the foreign exchange market in Mauritius.

中文翻译：

---

时间序列建模，NARX神经网络和混合KPCA–SVR方法预测毛里求斯的外汇市场

目的

本研究构建时间序列模型，人工神经网络（ANN）和统计拓扑，以检查波动率并预测汇率。毛里求斯外汇市场已被用作案例研究，欧元/欧元，英镑/欧元，加元/欧元和澳元/欧元的名义即期汇率（2014年至2018年的五年期间）的每日数据具有已应用于预测。

设计/方法/方法

自回归综合移动平均（ARIMA）和广义自回归条件异方差（GARCH）模型用作分析的时间序列建模的基础，并且将非线性自回归网络与带有不同训练的外源输入（NARX）神经网络反向传播算法一起使用函数，即Levenberg-Marquardt（LM），贝叶斯正则化和比例共轭梯度（SCG）算法。该研究还采用混合核主成分分析（KPCA），其中使用支持向量回归（SVR）算法作为进行金融市场预测建模的附加统计工具。均方误差（MSE）和均方根误差（RMSE）被用作模型性能的指标。

发现

结果表明，与ARIMA模型相比，GARCH模型在波动率聚类和预测方面表现更好。另一方面，NARX模型表明，LM和贝叶斯正则化训练算法是预测不同货币汇率的最合适方法，因为与其他训练函数相比，MSE和RMSE似乎是最低的误差。同时，结果表明，基于基于结构风险最小化原理的理论，NARX和KPCA–SVR拓扑优于线性时间序列模型。最后，NARX模型与KPCA–SVR的比较表明，NARX模型优于统计预测模型。全面的，

研究局限/意义

由于任何国家经济环境的不确定性，外汇市场被认为是不稳定的，因此，准确预测汇率对任何外汇交易都至关重要。这项研究具有重要的经济意义，因为它将帮助毛里求斯的研究人员，投资者，交易员，投机商和金融分析师，银行和金融机构中的金融新闻用户，货币兑换商，非银行金融公司和证券交易所等机构做出投资决策。就国际投资而言。此外，货币汇率的不稳定性可能会增加交易成本并降低国际贸易的回报。汇率波动引起了实施高度组织化的风险管理措施的必要性，以便披露外币的未来趋势和走势，这可以作为外汇参与者的基本指导。通过这种方式，他们将在进行任何外汇交易（包括对冲，资产定价或任何投机活动）之前更加警觉，采取纠正措施，从而防止他们在未来造成任何潜在损失并获得更多利润。

创意/价值

这是在利用时间序列建模，NARX神经网络反向传播算法和混合KPCA-SVR来预测毛里求斯在外汇市场中使用多种货币的外汇的同时应用人工智能（AI）的第一批研究之一。