Abstract
Abstract Understanding the effects of people's interactions on social media on economic fluctuations is essential for analyzing economic dynamics and making predictions. ‘Time-varying’ and ‘time-scale dependent’ volatilities between tweets sent from Turkey containing the terms "economic crisis", "inflation", "unemployment", "economic recession", "#dolar" (also their lagged series), and TL/USD FX rate was examined with dynamic conditional correlation (DCC) GARCH model. 7.402.035 Tweet data were used for the study, and their count was averaged between the dates 01.10.2020 and 11.03.2022, and a time series of 15, 30 and 60 minutes was obtained. These series of tweets were compared with the USD/TL FX rate data for the same periods. The results show that examining -delayed relationships of up to 10 lags- 6th and 10th lag of 60 min frequency Twitter data have high level of conditional correlations with TL/USD FX rate. However, except for these series 12 of that is not dynamic but a CC process and for 105 series are statistically not significant to explain CC and DCC relationship. JEL classification numbers: G12, G17, G41. Keywords: Twitter narratives, DCC-GARCH, USD/TL FX rate, narrative economics.
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