Volatility Clustering of Fine Wine Prices assuming Different Distributions
Cynthia Royal Tori, PhD; Scott Leander Tori, PhD

Abstract
This study estimates fine wine market volatility using the generalized autoregressive conditional heteroscedastic (GARCH)and Exponential-GARCH (1, 1) models assuming three different residual distributions, and compare the results to identify a best fit model. This study examines volatility using the daily value of the Liv-ex Fine Wine 50 Index from February 26, 2010 through September 29, 2017. The results suggest that the EGARCH models assuming a student’s t distribution or generalized error distribution are better models for the Liv-ex returns than the GARCH models. While the magnitude of the response to innovations may be equal for positive and negative innovations, the volatility associated with positive innovations is greater than negative innovations. Further the EGARCH model suggests the persistence of the volatility is 70 trading days.

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