Financial development and economic growth in an oil-rich economy: The case of Saudi Arabia

© 2014 Elsevier B.V.We investigate the effect of financial development on economic growth in the context of Saudi Arabia, an oil-rich economy. In doing so, we distinguish between the effects of financial development on the oil and non-oil sectors of the economy. Using the Autoregressive Distributed Lag (ARDL) Bounds test technique, we find that financial development has a positive impact on the growth of the non-oil sector. In contrast, its impact on the oil-sector growth and total GDP growth is either negative or insignificant. This suggests that the relationship between financial development and growth may be fundamentally different in resource-dominated economies

Financial Regulation and Transparency of Information: first steps on new land

This article examines the relationship between the level of regulation and transparency of financial institutions from 37 countries and the impacts of the subprime crisis on the stock market, through a regulation and transparency index. Furthermore, with the objective of detecting reasons for the success of some emerging economies in avoiding the crisis, empirical evidence for the presence of market discipline in the Brazilian banking industry is shown. The results are that a higher degree of regulation and transparency is related to higher returns and lower volatility in the stock market during the subprime crisis. Moreover, one of the main reasons for the apparent success of the Brazilian case in facing the crisis is the combination of a strong regulation of the financial system and the presence of market discipline.

Shadow banking activity and entrusted loans in a DSGE model of China

This paper examines how the risky lending activities of the state‐owned enterprises (SOEs) affect the effectiveness of monetary and fiscal policy in China with a shadow banking sector. We develop a dynamic stochastic general equilibrium (DSGE) macroeconomic model with two production sectors, where the SOEs have access to low cost funds from the commercial banks (also mainly state‐owned) and on‐lend to the private sector in the form of entrusted loans. The Bayesian estimation results show that higher restrictions on bank credit push SOEs to engage in more shadow banking in this form which dampens the effectiveness of contractionary monetary policy. Expansionary fiscal policy increases output, but crowds out private investment, which can further drain the financial market and exert a detrimental effect on the Chinese economy

The Determinants of Trade Balance and Adjustment to the Crisis in Indonesia

This paper investigates the effects of real exchange rate depreciation and supply side shocks on exports and imports. Indonesia provides an interesting case study of the subject because this country experienced a large depreciation, banking sector collapse, and socio-political turbulence during the Asian crisis episode. The results suggest that trade balance will improve following devaluation through an increase in exports and a collapse in imports. Because the elasticity of imports with respect to the real exchange rate is greater than that of exports, improvement in trade balance would be mainly come from import compression. It is also found that export performance could have been far better if Indonesia did not suffer from banking problems and socio-political turbulence.Real Exchange Rate, Export, Import, Indonesia.

Trade Liberalization, Financial Sector Reforms and Growth

This paper empirically investigates the impact of trade and financial liberalization on economic growth in Pakistan using annual observations over the period 1961-2005. The analysis is based on the bound testing approach of cointegration advanced by Pesaran et al (2001). The empirical findings suggest that both trade and financial policies play an important role in enhancing growth in Pakistan in the long-run. However, the short-run response of real deposit rate and trade policy variable is very low, suggesting further acceleration of reform process. The feedback coefficient suggests a very slow rate of adjustment towards long-run equilibrium. The estimated short-run dynamics are stable as indicated by CUSUMQ test.Financial Sector Reforms; Trade Liberalization; Growth; Pakistan

Data Science for Finance: Targeted Learning from (Big) Data to Economic Stability and Financial Risk Management

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Statistics and EconometricsThe modelling, measurement, and management of systemic financial stability remains a critical issue in most countries. Policymakers, regulators, and managers depend on complex models for financial stability and risk management. The models are compelled to be robust, realistic, and consistent with all relevant available data. This requires great data disclosure, which is deemed to have the highest quality standards. However, stressed situations, financial crises, and pandemics are the source of many new risks with new requirements such as new data sources and different models. This dissertation aims to show the data quality challenges of high-risk situations such as pandemics or economic crisis and it try to theorize the new machine learning models for predictive and longitudes time series models. In the first study (Chapter Two) we analyzed and compared the quality of official datasets available for COVID-19 as a best practice for a recent high-risk situation with dramatic effects on financial stability. We used comparative statistical analysis to evaluate the accuracy of data collection by a national (Chinese Center for Disease Control and Prevention) and two international (World Health Organization; European Centre for Disease Prevention and Control) organizations based on the value of systematic measurement errors. We combined excel files, text mining techniques, and manual data entries to extract the COVID-19 data from official reports and to generate an accurate profile for comparisons. The findings show noticeable and increasing measurement errors in the three datasets as the pandemic outbreak expanded and more countries contributed data for the official repositories, raising data comparability concerns and pointing to the need for better coordination and harmonized statistical methods. The study offers a COVID-19 combined dataset and dashboard with minimum systematic measurement errors and valuable insights into the potential problems in using databanks without carefully examining the metadata and additional documentation that describe the overall context of data. In the second study (Chapter Three) we discussed credit risk as the most significant source of risk in banking as one of the most important sectors of financial institutions. We proposed a new machine learning approach for online credit scoring which is enough conservative and robust for unstable and high-risk situations. This Chapter is aimed at the case of credit scoring in risk management and presents a novel method to be used for the default prediction of high-risk branches or customers. This study uses the Kruskal-Wallis non-parametric statistic to form a conservative credit-scoring model and to study its impact on modeling performance on the benefit of the credit provider. The findings show that the new credit scoring methodology represents a reasonable coefficient of determination and a very low false-negative rate. It is computationally less expensive with high accuracy with around 18% improvement in Recall/Sensitivity. Because of the recent perspective of continued credit/behavior scoring, our study suggests using this credit score for non-traditional data sources for online loan providers to allow them to study and reveal changes in client behavior over time and choose the reliable unbanked customers, based on their application data. This is the first study that develops an online non-parametric credit scoring system, which can reselect effective features automatically for continued credit evaluation and weigh them out by their level of contribution with a good diagnostic ability. In the third study (Chapter Four) we focus on the financial stability challenges faced by insurance companies and pension schemes when managing systematic (undiversifiable) mortality and longevity risk. For this purpose, we first developed a new ensemble learning strategy for panel time-series forecasting and studied its applications to tracking respiratory disease excess mortality during the COVID-19 pandemic. The layered learning approach is a solution related to ensemble learning to address a given predictive task by different predictive models when direct mapping from inputs to outputs is not accurate. We adopt a layered learning approach to an ensemble learning strategy to solve the predictive tasks with improved predictive performance and take advantage of multiple learning processes into an ensemble model. In this proposed strategy, the appropriate holdout for each model is specified individually. Additionally, the models in the ensemble are selected by a proposed selection approach to be combined dynamically based on their predictive performance. It provides a high-performance ensemble model to automatically cope with the different kinds of time series for each panel member. For the experimental section, we studied more than twelve thousand observations in a portfolio of 61-time series (countries) of reported respiratory disease deaths with monthly sampling frequency to show the amount of improvement in predictive performance. We then compare each country’s forecasts of respiratory disease deaths generated by our model with the corresponding COVID-19 deaths in 2020. The results of this large set of experiments show that the accuracy of the ensemble model is improved noticeably by using different holdouts for different contributed time series methods based on the proposed model selection method. These improved time series models provide us proper forecasting of respiratory disease deaths for each country, exhibiting high correlation (0.94) with Covid-19 deaths in 2020. In the fourth study (Chapter Five) we used the new ensemble learning approach for time series modeling, discussed in the previous Chapter, accompany by K-means clustering for forecasting life tables in COVID-19 times. Stochastic mortality modeling plays a critical role in public pension design, population and public health projections, and in the design, pricing, and risk management of life insurance contracts and longevity-linked securities. There is no general method to forecast the mortality rate applicable to all situations especially for unusual years such as the COVID-19 pandemic. In this Chapter, we investigate the feasibility of using an ensemble of traditional and machine learning time series methods to empower forecasts of age-specific mortality rates for groups of countries that share common longevity trends. We use Generalized Age-Period-Cohort stochastic mortality models to capture age and period effects, apply K-means clustering to time series to group countries following common longevity trends, and use ensemble learning to forecast life expectancy and annuity prices by age and sex. To calibrate models, we use data for 14 European countries from 1960 to 2018. The results show that the ensemble method presents the best robust results overall with minimum RMSE in the presence of structural changes in the shape of time series at the time of COVID-19. In this dissertation’s conclusions (Chapter Six), we provide more detailed insights about the overall contributions of this dissertation on the financial stability and risk management by data science, opportunities, limitations, and avenues for future research about the application of data science in finance and economy

Export destinations and learning-by-exporting : Evidence from Belgium

This paper evaluates the causal effects of exports to different destination countries using a comprehensive dataset on Belgian manufacturing firms from 1998 to 2005. Initial evidence suggests that, before export market entry, exporters to more developed economies have superior productivity levels than non-exporters and firms exporting to less developed countries. Moreover, they seem to experience higher productivity growth rates in the post-entry period, suggesting learning-by-exporting effects. However, applying matching methodology to formally evaluate the causal effects of export market entry on productivity reveals no such impact. Thus, the productivity advantage of firms exporting to developed countries appears to be driven solely by self-selection.Learning-by-exporting, export destinations, productivity