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This book presents a foray into the fascinating process of risk management, beginning from classical methods and approaches to understanding risk all the way into cutting-age thinking. Risk management by necessity must lie at the heart of governing our ever more complex digital societies. New phenomena and activities necessitate a new look at how individuals, firms, and states manage the uncertainty they must operate in. Initial chapters provide an introduction to traditional methods and show how they can be built upon to better understand the workings of the modern economy. Later chapters review digital activities and assets like cryptocurrencies showing how such emergent risks can be conceptualized better. Network theory figures prominently and the book demonstrates how it can be used to gauge the risk in the digital sectors of the economy. Predicting the unpredictable black swan events is also discussed in view of a wider adoption of economic simulations. The journey concludes by looking at how individuals perceive risk and make decisions as they operate in a virtual social network. This book interests the academic audience, but it also features insights and novel research results that are relevant for practitioners and policymakers

This book presents a foray into the fascinating process of risk management, beginning from classical methods and approaches to understanding risk all the way into cutting-age thinking. Risk management by necessity must lie at the heart of governing our ever more complex digital societies. New phenomena and activities necessitate a new look at how individuals, firms, and states manage the uncertainty they must operate in. Initial chapters provide an introduction to traditional methods and show how they can be built upon to better understand the workings of the modern economy. Later chapters review digital activities and assets like cryptocurrencies showing how such emergent risks can be conceptualized better. Network theory figures prominently and the book demonstrates how it can be used to gauge the risk in the digital sectors of the economy. Predicting the unpredictable black swan events is also discussed in view of a wider adoption of economic simulations. The journey concludes by looking at how individuals perceive risk and make decisions as they operate in a virtual social network. This book interests the academic audience, but it also features insights and novel research results that are relevant for practitioners and policymakers.

Abstract. With the growing ability of organizations in the public and private sector to collect large volumes of real-time data, the mounting pile of information presents specific challenges for storage, processing, and analysis. Many organizations do need data analysis for the purposes of planning and logistics. Likewise, governments and regulators will need analysis to support policy-making, implementation and controlling. All this leads to the importance of being able to generate large-scale analytics under (sometimes severe) resource constraints. This paper investigates a possible solution – automating analytics with a special focus on forecasting time series. Such approach has the benefit of being able to produce scalable forecasting of thousands of variables with relatively high accuracy for a short period of time and few resources. We first review the literature on time series forecasting with a particular focus on the M, M-2, and M-3 forecasting competition and outline a few major conclusions supported across different empirical studies. The paper then proceeds to explore the typical structure of a time-series variables using Bulgarian GDP growth and show how the ARIMA modeling with a seasonal component can be used to fit economic data of this class. We also review some major approaches to automating forecasting and outline the benefits of selecting the optimal model from a large set of ARIMA alternatives using an information criterion. A possible approach to fit an automated forecasting algorithm on four crucial economic time series from the Bulgarian economy is demonstrated. We use data on GDP growth, inflation, unemployment, and interest rates and fit a large number of possible models. The best ones are selected by taking recourse to the Akaike Information Criterion. The optimal ARIMA models are studied and commented. Forecast accuracy metrics are presented and a few major conclusions and possible model applications are outlined. The paper concludes with directions for further research.Keywords. Automated analytics, Forecasting, Time series, ARIMA, Business forecasting.JEL. C22, C53, E37.

The article shows how some novel machine learning algorithms can be applied to economic problems of discrete binary choice. An examination is made of three typical business tasks – classifying overdraft applications, credit risk management, and marketing segmentation. Both traditional econometric methods (logistic regression and linear discriminant analysis) as well as five more advanced machine learning algorithms (neural networks, k-nearest neighbours, naïve Bayes classifier, random forest, and support vector machine) have been used for modelling these tasks. For all the classification tasks, the random forest algorithm robustly registers improved forecasting accuracy over the more traditional approaches. This underlines the need to supplement the classical econometric toolbox with innovative methods, with the random forest, the support vector machine, and the neural network being prime candidates.

We present an overview of the dynamics of fiscal aggregates in Bulgaria in the period 1998 to 2014. The paper investigates the cyclical dynamics of budget revenue and expenditure and their major components. Using time series of deflated statistical data we calculate the elasticity of individual revenue and expenditure components to GDP growth – both for the overall period and for the post-crisis years. The major conclusion is that the Bulgarian fiscal aggregates are characterized mainly by procyclical dynamics, with the wages and maintenance expenditures being the least sensitive, and investment – the most sensitive to upturns and downturns in growth. These results can feed into fiscal policy and outline the potential to use public investment as an instrument for macroeconomic stabilization in the country.

The main hypotheses for the formation of inflation expectations of economic agents and key results of their research are discussed. Survey data on the inflation expectations of the citizens of the EU-27 member states for the period 01.1998-05.2013 are used to test the extent to which these theoretical hypotheses correspond to the dynamics actually observed. Econometrically, it is argued that the adaptive and rational expectations approaches present an incomplete picture of the process, while the dynamic heterogeneous expectations approach more fully describes observable empirical trends.