Assessment of the effect of the financial crisis on agents’ expectations through symbolic regression

Agents’ perceptions on the state of the economy can be affected during economic crises. Tendency surveys are the main source of agents’ expectations. The main objective of this study is to assess the impact of the 2008 financial crisis on agents’ expectations. With this aim, we evaluate the capacity of survey-based expectations to anticipate economic growth in the United States, Japan, Germany and the United Kingdom. We propose a symbolic regression (SR) via genetic programming approach to derive mathematical functional forms that link survey-based expectations to GDP growth. By combining the main SR-generated indicators, we generate estimates of the evolution of GDP. Finally, we analyse the effect of the crisis on the formation of expectations, and we find an improvement in the capacity of agents’ expectations to anticipate economic growth after the crisis in all countries except Germany.Peer ReviewedPostprint (author's final draft

Modelling tourism demand to Spain with machine learning techniques. The impact of forecast horizon on model selection

This study assesses the influence of the forecast horizon on the forecasting performance of several machine learning techniques. We compare the fo recastaccuracy of Support Vector Regression (SVR) to Neural Network (NN) models, using a linear model as a benchmark. We focus on international tourism demand to all seventeen regions of Spain. The SVR with a Gaussian radial basis function kernel outperforms the rest of the models for the longest forecast horizons. We also find that machine learning methods improve their forecasting accuracy with respect to linear models as forecast horizons increase. This results shows the suitability of SVR for medium and long term forecasting.Peer ReviewedPostprint (published version

Transmisión internacional de las rentabilidades y volatilidades entre NYSE e IBEX35

En este trabajo se realiza un estudio de la dinámica a corto plazo seguida por las rentabilidades y las volatilidades condicionadas entre el mercado NYSE y el mercado IBEX35 mediante una aproximación discreta. Para ello, se utiliza una especificación multivariante para la media y varianza condicionada que permiten, mediante procesos de generación de datos del tipo vectorial autorregresivo (VAR) y GARCH multivariante, analizar la respuesta dinámica simulada del sistema y estimar las covarianzas condicionadas de ambos mercados. Los resultados muestran cómo la respuesta de cada mercado a las innovaciones producidas en cada país es casi inmediata, siendo mayor la correspondiente el mercado doméstico que al mercado extranjero, y cómo la covarianza condicionada es variable en el tiempo en ambos mercados

Forecasting Business surveys indicators: neural networks vs. time series models

The objective of this paper is to compare different forecasting methods for the short run forecasting of Business Survey Indicators. We compare the forecasting accuracy of Artificial Neural Networks -ANN- vs. three different time series models: autoregressions -AR-, autoregressive integrated moving average -ARIMA- and self-exciting threshold autoregressions -SETAR-. We consider all the indicators of the question related to a country’s general situation regarding overall economy, capital expenditures and private consumption -present judgement, compared to same time last year, expected situation by the end of the next six months- of the World Economic Survey -WES- carried out by the Ifo Institute for Economic Research in co-operation with the International Chamber of Commerce. The forecast competition is undertaken for fourteen countries of the European Union. The main results of the forecast competition are offered for raw data for the period ranging from 1989 to 2008, using the last eight quarters for comparing the forecasting accuracy of the different techniques. ANN and ARIMA models outperform SETAR and AR models. Enlarging the observed time series of Business Survey Indicators is of upmost importance in order of assessing the implications of the current situation and its use as input in quantitative forecast models

Extraction of the underlying structure of systematic risk from non-Gaussian multivariate financial time series using independent component analysis: Evidence from the Mexican stock exchange

Regarding the problems related to multivariate non-Gaussianity of financial time series, i.e., unreliable results in extraction of underlying risk factors -via Principal Component Analysis or Factor Analysis-, we use Independent Component Analysis (ICA) to estimate the pervasive risk factors that explain the returns on stocks in the Mexican Stock Exchange. The extracted systematic risk factors are considered within a statistical definition of the Arbitrage Pricing Theory (APT), which is tested by means of a two-stage econometric methodology. Using the extracted factors, we find evidence of a suitable estimation via ICA and some results in favor of the APT.Peer ReviewedPostprint (published version

Posicionamiento multivariante de las expectativas de estudiantes graduados, profesores y empresarios mediante componentes principales categóricos

El presente artículo analiza las percepciones y las expectativas de los graduados universitarios, los profesores y los empresarios en cuanto a la adquisición de competencias desde una óptica multivariante. Para ello se comparan las competencias y habilidades desarrolladas durante los estudios universitarios (percepciones) y las que se les exigen a los estudiantes graduados para incorporarse al mercado laboral (expectativas). El estudio se basa en una encuesta realizada entre licenciados y académicos de la Universidad de Barcelona y empresarios catalanes. A partir del análisis estadístico efectuado, se detecta la existencia de un desajuste entre la percepción sobre las competencias desarrolladas y las expectativas sobre el nivel necesario para incorporarse en el mercado laboral. Se observa cómo las diferencias varían en función del colectivo analizado. A partir de los indicadores sintéticos creados se detecta que el mayor grado de discrepancia se da entre los empresarios para las diferentes competencias analizadas

Empirical modelling of survey-based expectations for the design of economic indicators in five European regions

This is a post-peer-review, pre-copyedit version of an article published in Empirica. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10663-017-9395-1”.In this study we use agents’ expectations about the state of the economy to generate indicators of economic activity in twenty-six European countries grouped in five regions (Western, Eastern, and Southern Europe, and Baltic and Scandinavian countries). We apply a data-driven procedure based on evolutionary computation to transform survey variables in economic growth rates. In a first step, we design five independent experiments to derive a formula using survey variables that best replicates the evolution of economic growth in each region by means of genetic programming, limiting the integration schemes to the main mathematical operations. We then rank survey variables according to their performance in tracking economic activity, finding that agents’ ‘‘perception about the overall economy compared to last year’’ is the survey variable with the highest predictive power. In a second step, we assess the out-of-sample forecast accuracy of the evolved indicators. Although we obtain different results across regions, Austria, Slovakia, Portugal, Lithuania and Sweden are the economies of each region that show the best forecast results. We also find evidence that the forecasting performance of the survey-based indicators improves during periods of higher growth.This is a post-peer-review, pre-copyedit version of an article published in Empirica. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10663-017-9395-1”.Peer ReviewedPostprint (author's final draft

Quantification of survey expectations by means of symbolic regression via genetic programming to estimate economic growth in central and eastern european economies

Tendency surveys are the main source of agents' expectations. This study has a twofold aim. First, it proposes a new method to quantify survey-based expectations by means of symbolic regression (SR) via genetic programming. Second, it combines the main SR-generated indicators to estimate the evolution of GDP, obtaining the best results for the Czech Republic and Hungary. Finally, it assesses the impact of the 2008 financial crisis, finding that the capacity of agents' expectations to anticipate economic growth in most Central and Eastern European economies improved after the crisis.Peer ReviewedPostprint (author's final draft

Dynamic distances between stock markets: use of uncertainty indices measures

We discuss the benefits of using neighbourhood relations between stock markets based on time criteria, such as the time differences between countries and the simultaneous opening hours between markets, when they are compared with the distance in kilometres of their capitals (...

Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model

This study presents an extension of the Gaussian process regression model for multiple-input multiple-output forecasting. This approach allows modelling the cross-dependencies between a given set of input variables and generating a vectorial prediction. Making use of the existing correlations in international tourism demand to all seventeen regions of Spain, the performance of the proposed model is assessed in a multiple-step-ahead forecasting comparison. The results of the experiment in a multivariate setting show that the Gaussian process regression model significantly improves the forecasting accuracy of a multi-layer perceptron neural network used as a benchmark. The results reveal that incorporating the connections between different markets in the modelling process may prove very useful to refine predictions at a regional level.Peer ReviewedPostprint (author's final draft