Finance Research Letters

This dataset contains data used in the paper titled "National Culture and the Demand for Physical Money During the First Year of the COVID-19 Pandemic." In it I searched if "national culture" variables had significant impacts on the money demand during the first year of the COVID-19 pandemic. The main file of this dataset is the "20220923_final_dataset.xlsx" spreadsheet. Sheet "trimed_dataset_final" contains all the data used in the final regressions presented in the paper. In sheet "full_dataset_active", partial data on countries that did not make it into final analyses due to data gaps is also presented. Sheets "CiC", "GDP", "CPI'", "IR", "SI," and "NC" hold all the data on dependent and independent variables I have managed to collect. The origin of each data point is presented in the "data_sources_table." It should be noted that all data in this spreadsheet underwent transformation as described in the paper. However, all raw data is also included in this dataset (folder 'raw_data'). Data on CiC were primarily gathered from the IMF International Financial Statistics database (henceforth: IMF IFS; database: FASMBC_XDC), but for some countries the data were supplemented with the data from National Central Banks’. Data on NC was taken from the Hofstede Insights consultancy (https://www.hofstede-insights.com/). Data on GDP was primarily obtained from the IMF IFS (database: NGDP_XDC). Data gaps in this area were filled, if possible, directly from the National Statistical Offices’ (NSOs’) datasets. Because the study is based on monthly data, all quarterly data obtained was temporally disaggregated using the Denton-Cholette method (without an indicator series). Data on CPI was obtained primarily from Bank for International Settlements (BIS) datasets (https://www.bis.org/statistics/cp.htm) and IMF IFS databases (database: PCPI_IX) [BIS data had precedence]. Data gaps in this area were filled, if possible, directly from the National Statistical Offices’ datasets. Data on IR was obtained primarily from Bank for International Settlements (BIS) datasets (https://www.bis.org/statistics/cbpol.htm) and IMF IFS databases (databases: FPOLM_PA, FIMM_PA, and FIDR_PA) [BIS data had precedence]. Data gaps in this area were filled, if possible, directly from the National Central Banks’ datasets. Furthermore, data on IR were compiled using hierarchy suggested by the monetary transmission mechanism's: 'central bank policy rates' were given precedence; if those were unavailable,'money market rates' were used; and if those were also unavailable, data on 'deposit rates' were collected. Data on SI was taken directly from the COVID-19 Government Response Tracker (https://www.bsg.ox.ac.uk/research/research-projects/covid-19-government-response-tracker or https://github.com/OxCGRT/covid-policy-tracker/tree/master/data).

TPU and stock RV

This is the research data used in the article titled "The impact of COVID-19 on the degree of dependence and structure of risk-return relationship: A quantile regression approach"

The sheet "Indices" include all the data, whereas C stands for case, D stands for death. Any variable such as C43560 can be understood as follows: First letter,C, is the variable, the numbers follow stand for date.

This dataset is used for the empirical analysis of our study.

P2P data

The data used in this paper are collected from bitcoincharts.com over the period from January 2015 to September 2018. The variables contained in the data are daily high price, low price, closing price, average price, trading volume, and market capitalization. Exchanges are selected by data availability. We also use daily market capitalization data from coinmarketcap.com, and daily foreign exchange rate data from the Bank of Korea.

High-frequency data of tweets hashtag Bitcoin and corresponding Bitcoin price data (5-minute Intervals)

Data includes: 1/The stock indices of Japan, Korea, Singapore, Taiwan, Hong Kong, China, and Vietnam. 2/ FDI inflows to Vietnam 3/ Net FDI inflow over GDP

Data Explanation 1. rare_disaster_data1.txt monthly data for the number of news articles 1st column: time 2nd column: The number of news articles covering North Korea’s military provocations 3rd column: The number of news articles covering North Korea’s nuclear bomb tests 4th column: The number of news articles covering North Korea’s missile tests 5th column: The nominal exchange rate between South Korea and the US 6th column: CPI for South Korea 7th column: CPI for the US 2. rare_disaster_data2_5m_2.txt - rare_disaster_data2_5m_6.txt 5 minute high-frequency data for the exchange rate 2nd – 6th nuclear tests in North Korea 1st column: date 2nd column: time 3rd column: The nominal exchange rate between South Korea and the US 3. rare_disaster_data3_daily_1.txt daily data for the exchange rate 1st nuclear test in North Korea 1st column: date 2nd column: The nominal exchange rate between South Korea and the US