In 2010 a new financial risk measure was discovered, namely Financial Turbulence. Although it has been studied by other papers, its statistical distribution study is still missing. Knowing a financial phenomenon distribution is of importance when performing risk and portfolio management, especially when estimating parametric Value-at-Risk with Copulas and performing Monte Carlo simulations. Therefore, this paper explores the S&P 500 Fi-nancial Turbulence to determine its best distribution fit by making use of various residual measures and goodness-of-fit tests. Additionally, it makes use of in-sampling and out-sampling in the period between 01/11/2012 and 01/11/2022. The results of this research indicate that the Generalised Hyperbolic distribution describes the S&P 500 Financial Turbulence the best.
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