A hybrid of bekk garch with neural network for modeling and forecasting time series

Gold prices change rapidly from time to time. The change is not only in the mean, but also in the variability of the series. The Malaysian Kijang Emas (MKE) is the official national bullion gold coin of Malaysia and it is high in demand. The purchase and resale prices of MKE are determined by the prevailing international gold market price. However, the value of Ringgit Malaysia (RM) that is used to purchase MKE is affected by United States (U.S.) dollar. Thus, the purpose of this study is to develop the best model for forecasting international gold prices, U.S. dollar index and MKE prices by investigating their co-movement. In an attempt to find the best model, fifteen years of data for MKE prices, international gold prices in U.S. dollar and U.S. dollar index were used. This study initially considered three standard methods namely bivariate generalized autoregressive conditional heteroskedasticity (GARCH), trivariate GARCH and multilayer feed-forward neural network (MFFNN). Bivariate and trivariate GARCH are from Baba-Engle-Kraft-Kroner (BEKK) GARCH. The current study further hybridized these methods to improve forecasting accuracy. Bivariate and trivariate GARCH were used to examine the relationship between gold prices and U.S. dollar. The trivariate GARCH was modified to develop GARCH-in-mean model due to the existence risk that was expected in the data. Analysis was done by using E-Views software. However, analysis using MFFNN model and hybridized models were carried out using MATLAB software. Analyses of performances were evaluated using mean absolute percentage error (MAPE) and mean square error (MSE). The MAPE for all in and out sample forecasts were less than 1%. The lowest values of MAPE were 0.8% for gold prices and 0.2% for U.S. dollar index. These low values were produced by using trivariate GARCH-in-mean model that was developed by the current study either as a single or hybdridized model with MFFNN. MSE recorded the values when trivariate GARCH-in-mean model was hybridized with MFFNN using 15 hidden nodes

Development of Accuracy for the Weighted Fuzzy Time Series Forecasting Model Using Lagrange Quadratic Programming

Limitation within the WFTS model, which relies on midpoints within intervals and linguistic variable relationships for assigning weights. This reliance can result in reduced accuracy, especially when dealing with extreme values during trend to seasonality transformations. This study employs the Weighted Fuzzy Time Series (WFTS) method to adjust predictive values based on actual data. Using Lagrange Quadratic Programming (LQP), estimated weights enhance the WFTS model. MAPE assesses accuracy as the model analyzes monthly IHSG closing prices from January 2017 to January 2023.The MAPE value of 0.61% results from optimizing WFTS with LQP. It utilizes a deterministic approach based on set membership counts in class intervals, continuously adjusting weights during fuzzification, minimizing the deviation between forecasted and actual data values.The Weighted Fuzzy Time Series Forecasting Model with Lagrange Quadratic Programming is effective in forecasting, indicated by a low MAPE value. This method evaluates each data point and adjusts weights, offering reliable investment insights for IHSG strategies.

Non-Probabilistic Inverse Fuzzy Model in Time Series Forecasting

Many models and techniques have been proposed by researchers to improve forecasting accuracy using fuzzy time series. However, very few studies have tackled problems that involve inverse fuzzy function into fuzzy time series forecasting. In this paper, we modify inverse fuzzy function by considering new factor value in establishing the forecasting model without any probabilistic approaches. The proposed model was evaluated by comparing its performance with inverse and non�inverse fuzzy time series models in forecasting the yearly enrollment data of several universities, such as Alabama University, Universiti Teknologi Malaysia (UTM), and QiongZhou University; the yearly car accidents in Belgium; and the monthly Turkish spot gold price. The results suggest that the proposed model has potential to improve the forecasting accuracy compared to the existing inverse and non-inverse fuzzy time series models. This paper contributes to providing the better future forecast values using the systematic rules. Keywords: Fuzzy time series, inverse fuzzy function, non-probabilistic model, non-inverse fuzzy model, future forecas

Prediction of Malaysian–Indonesian Oil Production and Consumption Using Fuzzy Time Series Model

Fuzzy time series has been implemented for data prediction in the various sectors, such as education, finance-economic, energy, traffic accident, others. Moreover, many proposed models have been presented to improve the forecasting accuracy. However, the interval-length adjustment and the out-sample forecast procedure are still issues in fuzzy time series forecasting, where both issues are yet clearly investigated in the pre�vious studies. In this paper, a new adjustment of the interval-length and the partition number of the data set is proposed. Additionally, the determining of the out-sample forecast is also discussed. The yearly oil production (OP) and oil consumption (OC) of Malaysia and Indonesia from 1965 to 2012 are examined to evaluate the performance of fuzzy time series and the probabilistic time series models. The result indicates that the fuzzy time series model is better than the probabilistic models, such as regression time series, exponential smoothing in terms of the forecasting accuracy. This paper thus highlights the effect of the proposed interval length in reducing the forecasting error sig�nificantly, as well as the main differences between the fuzzy and probabilistic time series models. Keywords: Fuzzy time series; index of linguistic; oil production–consumption; interval�length; forecasting accurac

Modeling and Forecasting Naira / USD Exchange Rate In Nigeria: a Box - Jenkins ARIMA approach

In the financial as well as managerial decision making process, forecasting is a crucial element (Majhi et al, 2009). Most research have been made on forecasting of financial and economic variables through the help of researchers in the last decades using series of fundamental and technical approaches yielding different results (Musa et al, 2014). The theory of forecasting exchange rate has been in existence for many centuries where different models yield different forecasting results either in the sample or out of sample (Onasanya & Adeniji, 2013). A country’s exchange rate is one of the most closely monitored indicators, as fluctuations in exchange rates can have far reaching economic consequences (Ribeiro, 2016). The recent financial turmoil all over the world demonstrates the urgency of perfect information of the exchange rates (Shim, 2000). Understanding the forecasting of exchange rate behaviour is important to monetary policy (Simwaka, 2007). One of the important variables that have considerable influence on other socio – economic variables in Nigeria is the Nigerian naira / dollar exchange rate (Ismail, 2009). Owing to the critical role played by exchange rate dynamics in international trade and overall economic performance of all countries in general, the need for a good forecasting tool cannot be ruled out. In this study, we model and forecast the Naira / USD exchange rates over the period 1960 – 2017. Our diagnostic tests such as the ADF test indicate that EXC time series data is I (1). Based on the minimum AIC value, the study presents the ARIMA (1, 1, 1) model as the optimal model. The ADF test further indicates that the residuals of the ARIMA (1, 1, 1) model are stationary and thus bear the characteristics of a white noise process. It is also important to note that our forecast evaluation statistics, namely ME, RMSE, MAE, MPE, MAPE and Theil’s U absolutely show that our forecast accuracy is quite good. Our forecast actually indicates that the Naira will continue to depreciate. The main policy implication from this study is that the Central Bank of Nigeria (CBN), should devalue the Naira in order to not only restore exchange rate stability but also encourage local manufacturing and promote foreign capital inflows

Modeling and Forecasting Naira / USD Exchange Rate In Nigeria: a Box - Jenkins ARIMA approach

In the financial as well as managerial decision making process, forecasting is a crucial element (Majhi et al, 2009). Most research have been made on forecasting of financial and economic variables through the help of researchers in the last decades using series of fundamental and technical approaches yielding different results (Musa et al, 2014). The theory of forecasting exchange rate has been in existence for many centuries where different models yield different forecasting results either in the sample or out of sample (Onasanya & Adeniji, 2013). A country’s exchange rate is one of the most closely monitored indicators, as fluctuations in exchange rates can have far reaching economic consequences (Ribeiro, 2016). The recent financial turmoil all over the world demonstrates the urgency of perfect information of the exchange rates (Shim, 2000). Understanding the forecasting of exchange rate behaviour is important to monetary policy (Simwaka, 2007). One of the important variables that have considerable influence on other socio – economic variables in Nigeria is the Nigerian naira / dollar exchange rate (Ismail, 2009). Owing to the critical role played by exchange rate dynamics in international trade and overall economic performance of all countries in general, the need for a good forecasting tool cannot be ruled out. In this study, we model and forecast the Naira / USD exchange rates over the period 1960 – 2017. Our diagnostic tests such as the ADF test indicate that EXC time series data is I (1). Based on the minimum AIC value, the study presents the ARIMA (1, 1, 1) model as the optimal model. The ADF test further indicates that the residuals of the ARIMA (1, 1, 1) model are stationary and thus bear the characteristics of a white noise process. It is also important to note that our forecast evaluation statistics, namely ME, RMSE, MAE, MPE, MAPE and Theil’s U absolutely show that our forecast accuracy is quite good. Our forecast actually indicates that the Naira will continue to depreciate. The main policy implication from this study is that the Central Bank of Nigeria (CBN), should devalue the Naira in order to not only restore exchange rate stability but also encourage local manufacturing and promote foreign capital inflows

Malaysian bilateral trade relations and economic growth

This paper examines the structure and trends of Malaysian bilateral exports and imports and then investigates whether these bilateral exports and imports have caused Malaysian economic growth. Although the structure of Malaysia’s trade has changed quite significantly over the last three decades, the direction of Malaysia’s trade remains generally the same. Broadly, ASEAN, the EU, East Asia, the US and Japan continue to be the Malaysia’s major trading partners. The Granger causality tests have shown that it is the bilateral imports that have caused economic growth in Malaysia rather than the bilateral exports

Exchange rate misalignments in ASEAN-5 countries

The purpose of this paper is to estimate the exchange rate misalignments for Indonesia, Malaysia, Philippines, Singapore and Thailand before the currency crisis. By employing the sticky-price monetary exchange rate model in the environment of vector error-correction, the results indicate that the Indonesia rupiah, Malaysian ringgit, Philippines peso and Singapore dollar were overvalued before the currency crisis while Thai baht was undervalued on the eve of the crisis. However, they suffered modest misalignment. Therefore, little evidence of exchange misalignment is found to exist in 1997:2. In particular, Indonesia rupiah, Malaysia ringgit, Philippines peso and Singapore dollar were only overvalued about 1 to 4 percent against US dollar while the Thai baht was only 2 percent undervalued against US dollar