Taking into account the rate of convergence in CLT under Risk evaluation on financial markets

Artikel

This paper examines 'fat tails puzzle' in the financial markets. Ignoring the rate of convergence in Central Limit Theorem (CLT) provides the 'fat tail' uncertainty. In this paper, we provide a review of the empirical results obtained 'fat tails puzzle' using innovative method of Yuri Gabovich based on the rate of convergence in CLT to the normal distribution, which is called G-bounds. Constructed G-bounds evaluate risk in the financial markets more carefully than models based on Gaussian distributions. This statement was tested on the 24 financial markets exploring their stock indexes. Besides, this has tested Weak-Form Market Efficiency for investigated markets. As a result, we found out the negative correlation between the weak effectiveness of the stock market and the thickness of the left tail of the profitability density function. Therefore, the closer the risk of losses on the stock market to the corresponding risk of loss for a normal distribution, the higher the probability that the market is weak effective. For non-effective markets, the probability of large losses is much higher than for a weak effective.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Cogent Economics & Finance ; ISSN: 2332-2039 ; Volume: 5 ; Year: 2017 ; Issue: 1 ; Pages: 1-11 ; Abingdon: Taylor & Francis

Classification and Topics

• Classification: Wirtschaft ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Model Evaluation, Validation, and Selection ; Financial Econometrics ; Information and Market Efficiency; Event Studies; Insider Trading
• Subject: fat tails ; non-gaussianity ; risk evaluation ; G-bounds ; CLT ; The convergence to the normal distribution ; weak-form market efficiency (WFE)

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Kazaryan, Levon ; Kantorovich, Gregory
• Event: Veröffentlichung
• (who): Taylor & Francis
• (where): Abingdon
• (when): 2017

Further information

• DOI: doi:10.1080/23322039.2017.1302870
• Handle: http://hdl.handle.net/10419/194661
• Last update: 10.03.2025, 11:46 AM CET