An empirical note about estimation and forecasting Latin American Forex returns volatility: the role of long memory and random level shifts components

Portuguese Economic Journal - A set of RLS-type models with ARMA and ARFIMA dynamics is estimated and compared in a forecasting exercise with ARFIMA, GARCH and FIGARCH models. It is an extension of...     

Coordination in a skeptical two-group population

Journal of Economic Interaction and Coordination - This paper explores a situation in which a population split into two groups attempts to achieve the socially efficient outcome of a coordination...     

Is the Retail Gasoline Market Local or National?

This paper studies the effect of station density on prices in the retail gasoline market in the Czech Republic. We estimate the impact of the number of competing stations in various driving-distance ranges around each station on prices. We find that station density has a negative effect on prices; the effect decreases with distance and is statistically significant up to six kilometers. This suggests that the retail gasoline market is local rather than national.     

War and economy. Rediscovering the eighteenth-century military entrepreneur

Abstract

The detrimental effects traditionally assigned to warfare in the development of pre-industrial economies have obscured the prominent role that military entrepreneurs played in economic development in this period. Historiography minimises the extent to which war and the concomitant strengthening of the central state provided a whole new range of opportunities for capital investment, a tendency that has been strengthened by the paradigm of Redlich’s ‘decline of the soldier-entrepreneur’ and the technological determinism of the debate on the Military Revolution among others. The aim of this introduction is to look into the background of this relative lack of interest and to reaffirm the mutual dependence of eighteenth-century state-formation and the business of war.     

From private label to co-branded credit card: an assessment of the impact of a change in value proposition over customer value perception and company value capture

Journal of Financial Services Marketing - Purpose: The aim of this paper is to evaluate the success of a value proposition over time, considering two aspects: customer’s perceived value and...     

Family firm’s management composition: the role played by family members’ age in TMT and supervisor levels

Quality & Quantity - Family firms (FFs) are the backbone of entrepreneurial fabric in many countries. Management of such businesses is complex because of their features: the overlap between...     

Characterization of the Row Geometric Mean Ranking with a Group Consensus Axiom

An axiomatic approach is applied to the problem of extracting a ranking of the alternatives from a pairwise comparison ratio matrix. The ordering induced by row geometric mean method is proved to be uniquely determined by three independent axioms, anonymity (independence of the labelling of alternatives), responsiveness (a kind of monotonicity property) and aggregation invariance, which requires the preservation of group consensus, that is, the pairwise ranking between two alternatives should remain unchanged if unanimous individual preferences are combined by geometric mean.     

Machine learning for financial transaction classification across companies using character-level word embeddings of text fields

An important initial step in accounting is mapping financial transfers to the corresponding accounts. We devised machine-learning-based systems that automate this process. They use word embeddings with character-level features to process transaction texts. When considering 473 companies independently, our approach achieved an average top-1 accuracy of 80.50%, outperforming baselines that exclude the transaction texts or rely on a lexical bag-of-words text representation. We extended the approach to generalizes across companies and even across different corporate sectors. After standardization of the account structures and careful feature engineering, a single classifier trained on 44 companies from 28 sectors achieved a test accuracy of more than 80%. When trained on 43 companies and tested on the remaining one, the system achieved an average performance of 64.62%. This rate increased to nearly 70% when considering only the largest sector.     

Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.     

A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.