{'fraction_non_alphanumeric': 0.01509705248023005, 'fraction_numerical': 0.0, 'mean_word_length': 5.597156398104265, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A STUDY ON THE RETURN VOLATILITY OF THE COFFEE BEANS PRICE USING ARCH MODELS

In the case of primary commodities, price volatility would arise mainly due to disturbances in supply, whereas for industrial raw materials, it would be the result of disturbances in demand. In the analysis of commodity markets can be seen that information, hedging, speculation and physical availability are factors that can influence their volatility. Moreover, increased volatility in commodity markets can justify the use of information-based processes for modeling the pattern of return volatility of these commodities. Since the relevance of autoregressive conditional heteroscedasticity (ARCH) family models in the solution of problems in economic and financial areas due to their applicability and interpretation (the relation between return and volatility) have been provided, the aim of this work was to compare the Bayesian estimates for the parameters of ARCH processes with normal and Student’s t distributions for the conditional distribution of the return series of coffee beans price. In addition, informative prior distributions were suggested and posterior summaries were obtained by Monte Carlo Markov Chain simulation methods. Results showed that the proposed Bayesian approach provides satisfactory estimates and that the ARCH process with Student’s t distribution adjusts better to the data.