The tools that are offered to investors in financial markets are fluctuating. As this fluctuation causes losses as well as earnings, it is characterised as a risk for the investor. Especially, fluctuations that may occur in globally important markets and financial instruments have great significance, not just for investor but also for the global economy. Volatility, as a measure of fluctuations taking place in markets, is often used particularly by investors and all economic actors. Therefore, in recent years, future volatility predictions have gained importance. The aim of this research is forecasting future volatility values using the historical data of S&P 500, FTSE 100 and NIKKEI 225 stock market indexes. The progress of historical volatility values in years is presented and generated univariate time series is modelled with artificial neural networks. Future forecasts are done with the obtained model and results are interpreted. Keywords: Artificial neural networks, volatility, time series analysis, stock market indexes.
Measuring logistics efficiency is important to understand the strengths and weaknesses of a country's logistics operations and to be able to do necessary improvements. A common practice in the literature is applying Data Envelopment Analysis (DEA) with World Bank's logistics performance index (LPI) values for measuring logistics efficiency of countries. While DEA is a powerful methodology for relative efficiency measurement, a more sophisticated branch of DEA models is Network DEA (NDEA), especially for processes with inner sub-processes. The purpose of this study is to present a novel NDEA model for measuring logistics efficiencies and sub-efficiencies of countries.Design/methodology/approach -This study presents a relational two-stage network data envelopment analysis model to measure relative efficiency of a country's logistics process. For the first time in literature, total logistics process of a country is divided into two sub-processes as production and service stages.Findings -Proposed Network DEA model utilizes international LPI scores and macroeconomic indicators to measure OECD countries' logistics efficiencies for bi-yearly periods between 2010 and 2018. Obtained results favors 3 countries out of 37 with high logistics efficiencies. Also, by grouping the countries in terms of development level, results show that although developed countries have better logistics outputs in terms of LPI index, most logistically efficient countries are developing economies in general.Discussion -This study with proposed NDEA model is open for further research and development. The model could be varied with different capital and labor measures, also could be improved by adding some domestic LPI or other logistics indicators.
A novel ARIMA-ANN hybrid model for time series analysis based on least squares optimization Performance comparison with seven other models by applying to three well known time series data High forecasting performance, especially for relatively shorter term forecasting horizons , Figure A. Graphic Illustration of Optimized ARIMA-ANN Hybrid Model Purpose: The purpose of this study is to present a novel ARIMA-ANN hybrid model for time series analysis. Proposed optimized ARIMA-ANN (OptAA) hybrid model is applied to three well-known time series data with varying forecasting horizons. For determination of the forecasting performance, results are compared with the results of other models. Theory and Methods: Proposed model uses a least squares optimization of ARIMA and ANN models of the time series data to decompose it into linear and nonlinear components. After the first decomposition, error series of the linear part is transferred to the nonlinear component to revise the nonlinear part, which is then remodeled with ANN. The sum of the ARIMA model forecast of the linear part and ANN forecast of the revised nonlinear part is the final forecast of the hybrid model. Three time series data, Wolf's sunspot, Canadian lynx and GBP/USD exchange rate are used for forecasting performance comparison purposes. Proposed hybrid model's forecasting performance is compared with four major ARIMA-ANN hybrid models, ARIMA, ANN and random walk model. Results:Obtained results show that OptAA hybrid model is a very powerful methodology for time series forecasting.Especially for short term forecasting horizons proposed hybrid model shows better performance than other compared models. Conclusion:OptAA hybrid model is open for further research. Testing the model with different neural network parameters such that learning algorithm, network architecture, activation functions etc. is possible. Also applying the model to different time series and forecasting horizons helps to improve the generalization of the model.
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