**Preface to "Quantitative Methods for Economics and Finance"**

Since the mid-twentieth century, it has been clear that the more classical mathematical models were not enough to explain the complexity of financial and economic series. Since then, the effort to develop new tools and mathematical models for their application to economics and finance has been remarkable. However, it is still necessary to continue developing new tools, as well as continue studying the latest tools developed for the study of the financial and economic series. These tools can come from techniques and models taken from physics or new branches of mathematics such as fractals and dynamical systems, or new statistical techniques such as big data.

This book is a collection of the 19 papers that appeared in the Special Issue "Quantitative Methods for Economics and Finance" for the journal Mathematics. The purpose of this Special Issue is to gather a collection of articles reflecting the latest developments in different fields of economics and finance where mathematics plays an important role.

Khan et al. reexamine the relationship between policy uncertainty and stock prices in the United States, using a dynamically simulated autoregressive distributed lag setting.

Bengoa et al. use structural gravity model theory to study if the co-existence of bilateral investment treaties and two major regional agreements exert any effects on foreign direct investment in eleven Latin American countries.

Mart´ın Cervantes et al. apply SRA copulas to analyze the relationship between price and trading volume of four stock market indexes.

Kim et al. use copula models to study the relationship between Bitcoin, gold, and the S&P500 index.

Kao et al. propose a forecasting framework based on the ensemble empirical mode decomposition, and hybrid models including autoregressive integrated moving average, support vector regression, and the genetic algorithm, to predict the primary energy consumption of an economy.

Schneider et al. propose a dispersion trading strategy based on a statistical stock selection process, which determines appropriate subset weights by exploiting a principal component analysis to specify the individual index explanatory power of each stock.

D´ıaz et al. study efficiency scores of pharmaceutical firms based on non-parametric data envelopment analysis techniques.

Isohat¨ al¨ a et al. study the impact of financing constraints on the relationship between net worth ¨ and investment.

Yan et al. propose a framework of a financial early warning system for listed companies in China, combining the unconstrained distributed lag model and widely used financial distress prediction models such as the logistic model and the support vector machine.

Lamothe-Fernandez et al. compare deep learning methodologies for forecasting Bitcoin price ´ and introduce a new prediction model.

Nikolova et al. provide a methodology, based on the Hurst exponent, to calculate the probability of volatility clusters and apply it to different assets including stocks, indexes, forex, and cryptocurrencies.

Garc´ıa Mirantes et al. analyze how many factors (including short and long-term components) should be considered when modeling the risk-management of energy derivatives.

Salmeron G ´ omez et al. analyze the detection of near-multicollinearity in a multiple linear ´ regression from auxiliary centered and non-centered regressions.

Naimy et al. analyze the volatility dynamics in the financial markets of the United States, Russia, and China during their intervention in the Syrian war.

Cruz Rambaud et al. introduce a novel concept of an abstract derivative with applications in intertemporal choice when trying to characterize moderately and strongly decreasing impatience.

Salmeron G ´ omez et al. extends the concept of variance inflation factor to be applied in a raise ´ regression in a model that presents collinearity.

Cruz Rambaud et al. study the relationship between the Time Trade-Off, Expected Utility, and Discounted Utility models.

Ramos-Requena et al. introduce different models to calculate the amount of money to be allocated in each stock in a pairs trading strategy.

Druica et al. study the relationship between academic dishonesty and dishonest and fraudulent ˘ behavior, such as tax evasion, social insurance fraud, and piracy.

#### **J.E. Trinidad-Segovia, Miguel Angel S´ ´ anchez-Granero** *Editors*
