Investment Environment Problems Analysis and Evaluation: An Ex Post Empirical Analysis and Performance Implications
Keywords:Investment Environment, Value-at-Risk, Risk Assessment, Developing Countries, Investment
The research subject is the investment environment problem analysis and the evaluation of the developing countries, namely, the Republic of Serbia, Croatia, Slovenia, and Hungary. The research problem is to determine performance and adequacy of risk estimation models with special attention to the investment environment specificities of the markets in the developing countries. The analysis was carried out by testing and implementation of the Value-at-Risk models, i.e. the historical simulation (HS VaR), the delta-normal VaR (D VaR) and the extreme value theory model (EVT), with the confidence level of 95 % for 100, 200 and 300 days, in the period from 2012 to 2016. The research objective is to test the validity of VaR models and performance evaluation regarding determination of the maximum possible loss. The basic hypothesis of the research is that there is a relation between the successful application of the historical simulation (HS VaR), the delta-normal VaR (D VaR) and the extreme value theory model (EVT) and the conditions and opportunities of the investment environment of the developing countries. The research results provide concrete knowledge of the conditions and circumstances of the investment environment in the observed markets, with a simultaneous performance assessment of the tested VaR models. The main result of the study is that regarding investment activities in the markets of developing countries and number of failures of various VaR models, the investment policymakers cannot rely on the analysis of historical trends and on one of the basic postulates of portfolio analysis ‘History Repeats Itself’. Recommendation for further research and for the local societies benefit is to emphasize the necessity of stable investment environment, thus enabling adequate capital allocation and risk estimation, while using the wide variety of approaches to Value-at-Risk modeling, especially for longer-horizon risk prediction.