Serkan Aras

Dokuz Eylul University | DEÜ · Department of Econometrics

Although artificial neural networks have recently gained importance in time series applications, some methodological shortcomings still continue to exist. One of these shortcomings is the selection of the final neural network model to be used to evaluate its performance in test set among many neural networks. The general way to overcome this proble...

In today’s competitive global economy, businesses must adjust themselves constantly to ever-changing markets. Therefore, predicting future events in the marketplace is crucial to the maintenance of successful business activities. In this study, sales forecasts for a global furniture retailer operating in Turkey were made using state space models, A...

Machine learning techniques have been used frequently for volatility forecasting. However, previous studies have built these hybrid models in a form of a first-order GARCH(1,1) process by following general use for GARCH models. But the way of estimating parameters for GARCH and machine learning models differs considerably. Hence, we have investigat...

Modelling the volatility of Bitcoin, the cryptocurrency with the largest market share, has recently attracted considerable attention from researchers, practitioners and investors in financial markets and portfolio management. For this purpose, a wide variety of GARCH-type models have been employed. However, there is no consensus in the literature o...

Forecasting inflation accurately in a data-rich environment is a challenging task and an active research field which still contains various unanswered methodological questions. One of them is how to find and extract the information with the most predictive power for a variable of interest when there are many highly correlated predictors, as in the...

Credit risk arises as a result of the failure of the loans given by banks to the customers to fulfill their obligations at the end of the specified term. Technological advances allow the use of machine learning methods in various sectors. These methods aim to facilitate the identification of customers at risk with the system adapted to the creditwo...

To improve forecasting accuracy, researchers employed various combination techniques for a long time. When researchers deal with time series data by using dissimilar models, the combined forecasts of these models are expected to be superior. Deriving a weighting scheme performing better than simple but hard−to−beat combining methods has always been...

It is a well-established fact that energy consumption and production, as the primary sources of greenhouse gases, contribute to climate change and global warming issues. The analysis and estimation of the factors that contribute to these harmful gases will be of great assistance in the development of policies to reduce carbon dioxide emissions. In...

With the developing technology, mobile payment systems have become increasingly popular. In the public transport industry, this system has been convenient to the sector in terms of purchasing, using, carrying and storing tickets. One of the greatest challenges encountered in the mobile payment system in this sector is fraud. Fraud reduces customer...

With the developing technology, mobile payment systems have become increasingly popular. In the public transport industry, this system has convenient to the sector in terms of purchasing, using, carrying and storing tickets. One of the greatest challenges encountered in the mobile payment system in this sector is fraud. Fraud reduces customer satis...

Given the popularity and market capitalisation of Bitcoin, modelling its volatility is of extremely crucial for investors, and decision makers to take the right position against the possible risk situations. Traditionally, the GARCH-type models have been widely used to model any financial asset. However, it is a challenging task to decide on which...

With developing technology, face-to-face interactions with customers have ceded their place to digital communication during loan applications. This transition saves time and costs regarding credit allocation. However, it also leads to more exposure to risky situations because it is harder to understand customer profiles without cultivating a p...

Predicting the direction of stock prices is one of the most challenging tasks that a modeller undertakes. Researchers and practitioners from various fields such as finance, the stock market, statistics, and computer sciences are interested in estimating the true trend of the stock price index. In practice, it is more important to maximise the accur...

Günümüzde kripto para birimlerinin önemi gittikçe artmaktadır. Kripto para birimleri sanal oyun platformlarında kullanılırken, şu an pek çok kurum ve kuruluş tarafından ödeme aracı olarak kullanılmaktadır. Güvenlik risklerine karşı blockchain (Blok Zinciri) adı verilen algoritması ile üretimi sağlanmaktadır. Kripto para fiyatlarının doğru ol...

The modelling and forecasting of the volatility of the stock markets has been a topic of great interest in the world of finance.The motivation of the study was threefold: (1) we employ elev- en GARCH models such as, sGARCH, EGARCH, GJR-GARCH, APGARCH, IGARCH, csGARCH, FIGARCH, AVGARCH, TGARCH, NGARCH and NAGARCH to produce better forecasts, (2)...

The concept of volatility has an important place in the field of finance in terms of option pricing and risk management. Hedge fund managers, financial institutions and market regulators have much interested in modelling and forecasting volatility. The higher volatility makes the financial market unstable and causes the higher gains or losses. The...

When the literature regarding applications of neural networks is investigated, it appears that a substantial issue is what size the training data should be when modelling a time series through neural networks. The aim of this paper is to determine the size of training data to be used to construct a forecasting model via a multiple-breakpoint test a...

When the literature regarding applications of neural networks is investigated, it appears that a substantial issue is what size the training data should be when modelling a time series through neural networks. The aim of this paper is to determine the size of training data to be used to construct a forecasting model via a multiple-breakpoint test a...

Neural networks are one of the widely-used time series forecasting methods in time series applications. Among different neural network architectures and learning algorithms, the most popular choice is the feedforward Multilayer Perceptron (MLP). However, it suffers from some drawbacks such as getting trapped in local minima, human intervention duri...

To improve the forecasting accuracies, researchers have long been using various combination techniques. In particular, the use of dissimilar methods for forecasting time series data is expected to provide superior results. Although numerous combination techniques have been proposed until date, the simple combination techniques —such as mean and med...

There is a strong relationship between economic growth and demand for electricity. Among OECD countries, Turkey has the most rapid increase in electricity consumption for the last decade. As a developing country, it is expected that Turkey will need more electricity in future. In addition to sustaining its growing position, it is also vital for the...

Combining various forecasting models is an important tool for researchers to attain more accurate forecasts. A great number of papers have shown that selecting single models as dissimilar models, or methods based on different information as possible leads to better forecasting performances. However, there is not a certain rule regarding the number...

Günümüzde yapılan para talebinin modellenmesine ilişkin literatürü incelediğimizde, modelleme yöntemi olarak ço- ğunlukla kointegrasyon testlerinin kullanılmış olduğu ve bu testlerden Engle-Granger (1987) yanı sıra Johansen-Juselius (1990) tarafından önerilen kointegrasyon tekniklerinin birçok araştırmacı tarafından çoğu ülkenin para talebinin mode...

Döviz kuru uluslararası para piyasalarında en önemli ekonomik göstergeler arasında yer almaktadır. Büyük miktarda para transferi gerçekleştiren çok uluslu firmaların, döviz kuru hareketlerini doğru şekilde tahmin edebilmesi kazançlarında önemli iyileşmelere yer açabilecektir. Döviz kuru piyasasının yüksek oynaklık, doğrusal olmama ve düzensizlik gi...

At the present day, we investigate literature about Money demand, we can see that cointegration techniques are used in most of studies. Money demand econometric modelling techniques are used by a great number of researchers. Mostly, cointegration techniques are being used as estimating methods for any money demand function.Cointegration techniques...