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This study investigates the effect of the day of the week on the volatility of cryptocurrencies. Thus, we reveal investors' perceptions of the day of the week.

The EGARCH model consists of the day of the week for 2019–2022 and the volatility of 11 cryptocurrencies.

Empirical results show that the weekend harms cryptocurrency volatility. Also, there was positive cryptocurrency volatility at the beginning of the week. Our findings show that weekdays and weekends significantly impact cryptocurrency volatility. Besides, cryptocurrency investors are sensitive to market movements, disclosures, and regulations during the week. Holiday mode and cognitive shortcuts may cause cryptocurrency traders to remain passive on weekends.

This study has some limitations. We include 11 cryptocurrencies in the analysis by limiting cryptocurrencies according to market capitalizations. Further studies may analyze a larger sample. In addition, further studies may examine the moderator and mediator effects of other financial instruments.

The empirical results have research, social and practical conclusions from different aspects. Our analysis may contribute to determining trading strategies, risk management, market efficiency, regulatory oversight, and investment decisions in the cryptocurrency market.

The calendar effect in financial markets has extensive literature. However, cryptocurrencies' weekday and weekend effect needs to be adequately analyzed. Besides, studies analyzing cryptocurrency volatility are limited. We contribute to the literature by investigating the impact of days of the week on cryptocurrency volatility with a large sample and current data.

This study investigates the effect of unproven energy reserve news on the volatility of energy firms' stocks. Thus, investors' perception of unproven energy reserves is revealed. Additionally, the study aims to determine whether the effect of the news changes according to time and volatility level.

The general autoregressive conditional heteroskedasticity (GARCH) and exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models consist of the energy reserve exploration news in Turkey for the period 2009–2022 and the volatility of 14 energy stocks.

The results indicate energy exploration news's negative and significant effect on volatility. According to empirical results, energy stock volatility is most affected in the first ten days. Besides, the results show that the significant models of energy reserve news in low-volatility stocks are proportionally higher than in high-volatility stocks.

Only unproved reserve news is included in the analysis, as sufficient confirmed reserves could not be reached during the sampling period. Further studies can compare proven and unproved reserve news effects. Additionally, a similar analysis can be conducted between Turkey and another country with a similar socio-economic character to examine different investor behaviors.

This research includes indications on managing investors' reactions to unproven energy reserve news.

This study contributes to the literature by analyzing unproven reserves. Contrary to previous studies, examining stock volatility also makes the study unique.