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Statistical-Econometric Methods For Risk Diversification

Author

Listed:
  • CONSTANTIN ANGHELACHE

    (BUCHAREST UNIVERSITY OF ECONOMIC STUDIES / ARTIFEX UNIVERSITY OF BUCHAREST)

  • MADALINA-GABRIELA ANGHEL

    (ARTIFEX UNIVERSITY OF BUCHAREST)

  • STEFAN VIRGIL IACOB

    (ARTIFEX UNIVERSITY OF BUCHAREST)

Abstract

The risks appear, they can be known, measures can be taken to reduce the influence, the effects but they cannot be completely eradicated. Therefore, in the article we proposed a study as complex as possible to identify those statistical-econometric methods that underlie the study and analysis of risks. After all, in the article we talk about a diversification scheme of the activity precisely so that its effect is to reduce the risks. The statistical-econometric methods used are part of the methodology we used in this analysis, along with other statistical methods, such as grouping, data processing and interpretation, index method, analysis of the evolution of risk dynamics, effects produced in previous periods but also the causes that determine the occurrence of risks and which, if not met with precise measures, can lead to an increase in the losses suffered by the national economy. A correct statement is that all agents are concerned with identifying risks using a study either empirically, based on data, graphical representations, data series, or by using some statistical-econometric methods and models that result in the calculation of parameters in on the basis of which the possible losses can be extended so that measures can be taken throughout the course of the economic phenomenon. Also as a method we used stochastic analysis in risk analysis, precisely so that, on a statistical- mathematical basis, we can identify these risks, quantify their evolutionary perspectives and, finally, be able to take some measures to limit those risks.

Suggested Citation

  • Constantin Anghelache & Madalina-Gabriela Anghel & Stefan Virgil Iacob, 2021. "Statistical-Econometric Methods For Risk Diversification," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 5, pages 157-163, October.
    • Handle: RePEc:cbu:jrnlec:y:2021:v:5:p:157-163
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    References listed on IDEAS

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    1. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    2. Constantin Anghelache & Gyorgy Bodo, 2018. "General Methods of Management the Credit Risk," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 8(1), pages 143-152, January.
    3. Martin Martens & Jason Zein, 2004. "Predicting financial volatility: High‐frequency time‐series forecasts vis‐à‐vis implied volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 24(11), pages 1005-1028, November.
    4. Adrian Blundell-Wignall & Paul Atkinson, 2010. "Thinking beyond Basel III: Necessary Solutions for Capital and Liquidity," OECD Journal: Financial Market Trends, OECD Publishing, vol. 2010(1), pages 9-33.
    5. Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2008. "Designing Realized Kernels to Measure the ex post Variation of Equity Prices in the Presence of Noise," Econometrica, Econometric Society, vol. 76(6), pages 1481-1536, November.
    6. Eva Cipovova & Gabriela Dlaskova, 2016. "Comparison of Different Methods of Credit Risk Management of the Commercial Bank to Accelerate Lending Activities for SME Segment," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 17-26.
    7. Constantin ANGHELACHE & Gabriela Victoria ANGHELACHE & Madalina – Gabriela Anghel & Georgiana NITA, 2016. "General Notions on Banking Risks," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 64(5), pages 7-10, May.
    8. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
    9. Aurelian DIACONU & Alexandru BADIU & Doina AVRAM & Doina BUREA & Marius POPOVICI, 2017. "Operational Risk Management," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 65(5), pages 221-229, May.
    10. Alexandru Manole & Madalina Anghel & Emilia Stanciu & Alexandru Badiu, 2016. "Analysis models for the financial risk," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 64(9), pages 73-80, September.
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