Forecasting the Polish zloty with non-linear models

The literature on exchange rate forecasting is vast. Many researchers have tested whether implications of theoretical economic models or the use of advanced econometric techniques can help explain future movements in exchange rates. The results of the empirical studies for major world currencies show that forecasts from a naive random walk tend to be comparable or even better than forecasts from more sophisticated models. In the case of the Polish zloty, the discussion in the literature on exchange rate forecasting is scarce. This article fills this gap by testing whether non-linear time series models are able to generate forecasts for the nominal exchange rate of the Polish zloty that are more accurate than forecasts from a random walk. Our results confirm the main findings from the literature, namely that it is difficult to outperform a naive random walk in exchange rate forecasting contest.Exchange rate forecasting; Polish zloty; Markov-switching models; Artificial neural networks

Forecasting the Polish Zloty with Non-Linear Models

The literature on exchange rate forecasting is vast. Many researchers have tested whether implications of theoretical economic models or the use of advanced econometric techniques can help explain future movements in exchange rates. The results of the empirical studies for major world currencies show that forecasts from a naive random walk tend to be comparable or even better than forecasts from more sophisticated models. In the case of the Polish zloty, the discussion in the literature on exchange rate forecasting is scarce. This article fills this gap by testing whether non-linear time series models are able to generate forecasts for the nominal exchange rate of the Polish zloty that are more accurate than forecasts from a random walk. Our results confirm the main findings from the literature, namely that it is dificult to outperform a naive random walk in exchange rate forecasting contest.exchange rate forecasting, Polish zloty, Markov-switching models, artificial neural networks

Can social microblogging be used to forecast intraday exchange rates?

The Efficient Market Hypothesis (EMH) is widely accepted to hold true under certain assumptions. One of its implications is that the prediction of stock prices at least in the short run cannot outperform the random walk model. Yet, recently many studies stressing the psychological and social dimension of financial behavior have challenged the validity of the EMH. Towards this aim, over the last few years, internet-based communication platforms and search engines have been used to extract early indicators of social and economic trends. Here, we used Twitter's social networking platform to model and forecast the EUR/USD exchange rate in a high-frequency intradaily trading scale. Using time series and trading simulations analysis, we provide some evidence that the information provided in social microblogging platforms such as Twitter can in certain cases enhance the forecasting efficiency regarding the very short (intradaily) forex.Comment: This is a prior version of the paper published at NETNOMICS. The final publication is available at http://www.springer.com/economics/economic+theory/journal/1106

A Duration-Dependent Regime Switching Model for an Open Emerging Economy

We employ duration-dependent Markov-switching vector auto-regression (DDMSVAR) methodology to construct an economic cycle model for an emerging economy. By modifying the software codes for DDMSVAR methodology written by Pelagatti (2003), we show how to estimate the economic cycles in an emerging economy where macroeconomic shocks are suddenly observed and their levels are deep. The monthly values of net international reserves, domestic debt, inflation and industrial production in the Turkish economy from January 1989 to July 2007 are used for constructing the empirical analysis. Empirical evidence shows that DDMSVAR model can be successfully used in an emerging economy to estimate the cycles using basic macroeconomic indicators.duration dependent regime switching model, economic cycles, Markov models, Turkish economy

Can Turkish Recessions Be Predicted?

There is much scepticism about the ability to predict recessions. Harding and Pagan (2010b)have argued that this is because the definition of a recession involves the signs of future growth rates of economic activity and there is little predictability of these from the past. Turkey represents an interesting case study since growth in Turkish GDP features quite high serial correlation, suggesting that growth itself is predictable. Thus I want to examine whether it is possible to predict recessions in Turkey. As there seems only a small published literature on this it will be necessary to indicate what definition of recession is to be used and what information might be available to make a prediction of such an event. We found that using information from past macroeconomic variables would result in only limited success in predicting Turkish recessions.Conditional CAPM

Economic regimes identification using machine learning technics

43 páginas.Trabajo de Máster en Economía, Finanzas y Computación. Director: Dr. José Manuel Bravo Caro. Economic conditions over long time periods can be distinguished by regimes. Regime identification has been object of numerous investigations in economics and financial modeling for years. Recently, new machine learning technics such as decision trees, support vector machines and neural networks, among others, followed by alternative datasets and cheap computational processing power became available, allowing for alternative ways to model complex economic relationships. In the present work, we develop a supervised machine learning classifier using Random Forest technic to identify economic regimes using the S&P 500 stock market index series.Las condiciones económicas durante largos períodos de tiempo pueden distinguirse por regímenes. La identificación del régimen ha sido objeto de numerosas investigaciones en economía y modelos financieros durante años. Recientemente, se pusieron a disposición nuevas técnicas de aprendizaje automático, como árboles de decisión, máquinas de suporte vectorial y redes neuronales, entre otras, seguidas de conjuntos de datos alternativos y una capacidad de procesamiento computacional barata, que permite formas alternativas de modelar relaciones económicas complejas. En el presente trabajo, desarrollamos un clasificador de aprendizaje automático supervisado utilizando la técnica de Random Forest para identificar regímenes económicos utilizando la serie del índices de mercado S&P 500