Modelling Risk under Volatile Conditions: Tail Index Estimation and Validation

Authors

  • Vladimir Djakovic University of Novi Sad, Faculty of Technical Sciences, Serbia
  • Jelena Ivetić University of Novi Sad, Faculty of Technical Sciences, Serbia
  • Goran Andjelic Educons University, Faculty of Business Economics, Serbia

DOI:

https://doi.org/10.5755/j01.ee.32.4.29192

Keywords:

Modelling Risk, Value at Risk, Volatile Conditions, Tail Index Estimation, Investment Validation

Abstract

The subject of the research is to analyse and evaluate methods of investment risk modelling in dynamic, changing market circumstances, with a special focus on the assessment success of the expected effects of investment activities in ’extreme’ return points. In that sense, different Value at Risk models were used: the Historical Simulation (HS VaR), the Delta Normal VaR (D VaR) and the Extreme Value Theory model (EVT). The research objective is to test the performance of these models in specific, volatile, market circumstances, in terms of estimating the maximum possible losses from these activities. The basic hypothesis of the research is that it is possible to successfully anticipate the maximum possible losses from the investment activities in the extreme points of the return function by applying different methods of investment risk modelling in volatile market circumstances. The analysed financial data comprise daily stock returns of the BELEX15 (Serbia), BUX (Hungary), CROBEX (Croatia) and SBITOP (Slovenia) stock exchange indices in the period 2012-2019, which is relatively long time period suitable for the sound analyses. The main findings of the research point to the superior application adequacy of the Extreme Value Theory model (EVT) for successful risk modelling, i.e. for making optimal investment decisions. The research results represent innovated, concrete knowledge in the field of understanding the behaviour of the return function in its extremes, and consequently are of great importance to both the academic and professional public in the process of generating decisions on investment activities in volatile market conditions.

Author Biographies

Vladimir Djakovic, University of Novi Sad, Faculty of Technical Sciences, Serbia

Vladimir Djakovic was born at Novi Sad, Serbia. He did his Ph.D. thesis in Engineering Management-Investment Management at the University of Novi Sad, Faculty of Technical Sciences. Associate Professor Djakovic’s field of interest includes the following: investment management, financial management, risk management, and portfolio management. His research focuses on investment optimization processes using contemporary risk management investment tools. Particular emphasis in his research is placed on developing countries and the possibilities of developing various multidisciplinary engineering models in the subject field. He has taught courses at all levels (B.Sc., M.Sc., and Ph.D.)

Jelena Ivetić, University of Novi Sad, Faculty of Technical Sciences, Serbia

Jelena Ivetić is an associate professor at the Chair for Mathematics, Faculty of Technical Sciences, University of Novi Sad. Her research interests are in the domain of applied statistics and probability, and theoretical computer science. She has taught several mathematics courses to engineering students at all study levels.

Goran Andjelic, Educons University, Faculty of Business Economics, Serbia

Goran Andjelic was born at Novi Sad, Serbia. He did his Ph.D. thesis in Investment Management at the Faculty of Technical Sciences, University of Novi Sad. Full Professor Andjelic has a broad field of research interests in areas of Finance, Financial Management, Investments and Risk Management. He is the author and co-author of significant numbers of scientific articles and a participant in conferences. His diverse academic and practical experience has allowed him to work in finance, management, banking, investment sector, and public sector. He has taught courses at all levels (B.Sc., M.Sc., and Ph.D.).

Additional Files

Published

2021-10-28

Issue

Section

Articles