Long term currency forecast with multiple trend corrected exponential smoothing with shifting lags

In the current global economy, exchange rate forecasting is critical for investors and businesses seeking to make informed investment decisions and manage risk. While many short-term exchange rate forecasting methods exist, long-term forecasting methods are limited and often fail to account for the complex macroeconomic factors that influence exchange rate trends. However, investors need to have an analytically examined basis for deciding to invest, which requires knowing more about the future values of the related market currency. This paper proposes a new Multiple Trend Corrected Exponential Smoothing with Shifting Lags model to forecast long-term exchange rates, which incorporates multiple trend corrections and shifting lags to provide more accurate predictions of future currency values. We apply the proposed method to six currency pairs (USD/EUR, USD/NOK, USD/TRY, USD/CNY, USD/XOF, and USD/MGF) from 2006 to 2018 and compare its performance to existing methods, such as moving average, weighted moving average, and exponential smoothing. Our results show that the proposed model provides more accurate long-term exchange rate forecasts for developed countries than existing methods. Our findings have important implications for investors and businesses seeking to manage currency risk and make informed investment decisions in the global economy

Forecasting volatility with a stacked model based on a hybridized Artificial Neural Network

An appropriate calibration and forecasting of volatility and market risk are some of the main challenges faced by companies that have to manage the uncertainty inherent to their investments or funding opera- tions such as banks, pension funds or insurance companies. This has become even more evident after the 2007-2008 Financial Crisis, when the forecasting models assessing the market risk and volatility failed. Since then, a significant number of theoretical developments and methodologies have appeared to im- prove the accuracy of the volatility forecasts and market risk assessments. Following this line of thinking, this paper introduces a model based on using a set of Machine Learning techniques, such as Gradient Descent Boosting, Random Forest, Support Vector Machine and Artificial Neural Network, where those al- gorithms are stacked to predict S&P500 volatility. The results suggest that our construction outperforms other habitual models on the ability to forecast the level of volatility, leading to a more accurate assess- ment of the market ris

Forecasting Stock Market Volatility Using Wavelet Transformation Algorithm Of Garch Model

Kemeruapan pasaran saham adalah perkara penting terutamanya kepada dua pihak berkepentingan. Pengamal melalui kanta mata sendiri melihat pandangan tentang kesan kelakuan harga aset dan risiko Stock market volatility is of essential concern, particularly to two major stake-holder

Nonlinear volatility models in economics: smooth transition and neural network augmented GARCH, APGARCH, FIGARCH and FIAPGARCH models

Recently, Donaldson and Kamstra (1997) proposed a class of NN-GARCH models which are extended to a class of NN-GARCH family by Bildirici and Ersin (2009). The study aims to analyze the nonlinear behavior and leptokurtic distribution in petrol prices by utilizing a newly developed family of econometric models that deal with these concepts by benefiting from both LSTAR type and ANN based nonlinearity. With this purpose, the study proposed several LSTAR-GARCH-NN family models. It is noted that the multilayer perceptron (MLP) neural network and LSTAR models have significant architectural similarities. Accordingly, linear GARCH, fractionally integrated FI-GARCH, asymmetric power APGARCH and fractionally integrated asymmetric power APGARCH models are augmented with a family of Neural Network models. The study has following contributions: i. STAR-GARCH and LSTAR-GARCH are extended to their fractionally integrated asymmetric power versions and STAR-ST-FIGARCH and STAR-ST-APGARCH, STAR-ST-FIAPGARCH models are developed and evaluated. ii. By extending these models with neural networks, LSTAR-LST-GARCH-MLP family models are developed and investigated. These models benefit from LSTAR type nonlinearity and NN based nonlinear NN-GARCH models to capture time varying volatility and nonlinearity in petrol prices. ANN augmented versions of LSTAR-LST-GARCH models are as follows: LSTAR-LST-GARCH-MLP, LSTAR-LST-FIGARCH-MLP, LSTAR-LST-APGARCH-MLP and LSTAR-LST-FIAPGARCH-MLP. Empirical findings are collected as follows. i. To model petrol prices, fractionally integrated and asymmetric power versions provided improvements among the GARCH family models in terms of forecasting. ii. LSTAR-LST-GARCH model family is promising and show significant gains in out-of-sample forecasting. iii. MLP-GARCH family provided similar results with the LSTAR-LST-GARCH family models, except for the MLP-FIGARCH and MLP-FIAPGARCH models. iv. Volatility clustering, asymmetry and nonlinearity characteristics of petrol prices are captured most efficiently with the LSTAR-LST-GARCH-MLP models benefiting from forecasting capabilities of neural network techniques, whereas, among the newly developed models, LSTAR-LST-APGARCH-MLP model provided the best performance overall

Machine and deep learning applications for improving the measurement of key indicators for financial institutions: stock market volatility and general insurance reserving risk

Esta tesis trata de lograr mejoras en los modelos de estimación de los riesgo financieros y actuariales a través del uso de técnicas punteras en el campo del aprendizaje automático y profundo (machine y deep learning), de manera que los modelos de riesgo generen resultados que den un mejor soporte al proceso de toma de decisiones de las instituciones financieras. Para ello, se fijan dos objetivos. En primer lugar, traer al campo financiero y actuarial los mecanismos más punteros del campo del aprendizaje automático y profundo. Los algoritmos más novedosos de este campo son de amplia aplicación en robótica, conducción autónoma o reconocimiento facial, entre otros. En segundo lugar, se busca aprovechar la gran capacidad predictiva de los algoritmos anteriormente adaptados para construir modelos de riesgo más precisos y que, por tanto, sean capaces de generar resultados que puedan dar un mejor soporte a la toma de decisiones de las instituciones financieras. Dentro del universo de modelos de riesgos financieros, esta tesis se centra en los modelos de riesgo de renta variable y reservas de siniestros. Esta tesis introduce dos modelos de riesgo de renta variable y otros dos de reservas. Por lo que se refiere a la renta variable, el primero de los modelos apila algoritmos tales como redes neuronales, bosques aleatorios o regresiones aditivas múltiples con árboles con el objetivo de mejorar la estimación de la volatilidad y, por tanto, generar modelos de riesgo más precisos. El segundo de los modelos de riesgo adapta al mundo financiero y actuarial los Transformer, un tipo de red neuronal que, debido a su alta precisión, ha apartado al resto de algoritmos en el campo del procesamiento del lenguaje natural. Adicionalmente, se propone una extensión de esta arquitectura, llamada Multi-Transformer y cuyo objetivo es mejorar el rendimiento del algoritmo inicial mediante el ensamblaje y aleatorización de los mecanismos de atención. En lo relativo a los dos modelos de reservas introducidos por esta tesis el primero de ellos trata de mejorar la estimación de reservas y generar modelos de riesgo más precisos apilando algoritmos de aprendizaje automático con modelos de reservas basados en estadística bayesiana y Chain Ladder. El segundo modelo de reservas trata de mejorar los resultados de un modelo de uso habitual, como es el modelo de Mack, a través de la aplicación de redes neuronales recurrentes y conexiones residuales

Big Data, machine learning and challenges of high dimensionality in financial administration

Tese (doutorado)—Universidade de Brasília, Faculdade de Economia, Administração e Contabilidade e Gestão Pública, Programa de Pós-Graduação em Administração, 2019.A presente tese discute a emergência do Big Data e do aprendizado de máquinas em vários aspectos da administração de empresas, enfatizando as contribuições metodológicas deste paradigma baseado no raciocínio indutivo em finanças e os benefícios desta abordagem em relação a ferramentas econométricas e métodos tradicionais de análise de dados. Os fundamentos estatísticos do aprendizado de máquina são introduzidos e os desafios da alta dimensionalidade em problemas financeiros são analisados, incluindo as implicações práticas da incorporação de não- linearidades, a regularização do nível de complexidade adicional e a previsão em dados de alta frequência. Finalmente, três aplicações empíricas foram propostas, relativas, respectivamente, à previsão de volatilidade, à alocação de portfólio e à previsão da direção do preço de ações; Nessas aplicações, diferentes modelos de aprendizado de máquina foram explorados, e os insights dos resultados foram discutidos à luz da teoria financeira e das evidências empíricas.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).This thesis discusses the emergence of Big Data and machine learning and their applications in various aspects of Business Administration, emphasizing the methodological contributions of this inductive-based paradigm in finance and the improvements of this approach over econometric tools and traditionally well established methods of data analysis. The statistical foundations of machine learning are introduced and the challenges of high-dimensionality in finance problems are analyzed, including the practical implications of nonlinearity incorporation, regularization of the additional complexity level and forecasting for high-frequency data. Finally, three empirical applications are proposed, concerning respectively on volatility forecasting, portfolio allocation, and stock price direction prediction; in those applications, different machine learning models are explored, and the insights from the results were discussed in light of both the finance theory and the empirical evidences

Nonlinear volatility models in economics: smooth transition and neural network augmented GARCH, APGARCH, FIGARCH and FIAPGARCH models

Recently, Donaldson and Kamstra (1997) proposed a class of NN-GARCH models which are extended to a class of NN-GARCH family by Bildirici and Ersin (2009). The study aims to analyze the nonlinear behavior and leptokurtic distribution in petrol prices by utilizing a newly developed family of econometric models that deal with these concepts by benefiting from both LSTAR type and ANN based nonlinearity. With this purpose, the study proposed several LSTAR-GARCH-NN family models. It is noted that the multilayer perceptron (MLP) neural network and LSTAR models have significant architectural similarities. Accordingly, linear GARCH, fractionally integrated FI-GARCH, asymmetric power APGARCH and fractionally integrated asymmetric power APGARCH models are augmented with a family of Neural Network models. The study has following contributions: i. STAR-GARCH and LSTAR-GARCH are extended to their fractionally integrated asymmetric power versions and STAR-ST-FIGARCH and STAR-ST-APGARCH, STAR-ST-FIAPGARCH models are developed and evaluated. ii. By extending these models with neural networks, LSTAR-LST-GARCH-MLP family models are developed and investigated. These models benefit from LSTAR type nonlinearity and NN based nonlinear NN-GARCH models to capture time varying volatility and nonlinearity in petrol prices. ANN augmented versions of LSTAR-LST-GARCH models are as follows: LSTAR-LST-GARCH-MLP, LSTAR-LST-FIGARCH-MLP, LSTAR-LST-APGARCH-MLP and LSTAR-LST-FIAPGARCH-MLP. Empirical findings are collected as follows. i. To model petrol prices, fractionally integrated and asymmetric power versions provided improvements among the GARCH family models in terms of forecasting. ii. LSTAR-LST-GARCH model family is promising and show significant gains in out-of-sample forecasting. iii. MLP-GARCH family provided similar results with the LSTAR-LST-GARCH family models, except for the MLP-FIGARCH and MLP-FIAPGARCH models. iv. Volatility clustering, asymmetry and nonlinearity characteristics of petrol prices are captured most efficiently with the LSTAR-LST-GARCH-MLP models benefiting from forecasting capabilities of neural network techniques, whereas, among the newly developed models, LSTAR-LST-APGARCH-MLP model provided the best performance overall