The Dynamic Relation Between Economic Policy Uncertainty and Stock Market Volatility in Two Countries: United States and S. Korea

The financial industry has become one of the most data-driven industries and the recent technological developments in big data, financial services, consumer-data management, social network services, and filtering data quality has made better interpretation of analyzed data possible in the finance industry.Financial data consist of a huge volume of quotes, market data, historical trade data, and time-sequenced transaction data used to model market and customer behavior.Many papers found that the stock market Volatility and Economic Policy Uncertainty (EPU) are closely correlated.This study aims to find (1) the EPU index generated by big data can trace the volatility of the stock market and (2) the long-run equilibrium and shot-run relation between EPUs and Volatility index in the two countries are existed.The study has utilized the Vector Error Correction Model (VECM) to seek the dynamic relation between EPU and stock market volatility of the U.S. and S. Korea.The main findings of the study are (1) In most cases, the long-run equilibrium relation between EPU index and Volatility index exists, (2) a positive bi-directional relation between US_EPU and VIX and a positive uni-directional relation between KOR_EPU and VKOSPI, US_EPU and VKOSPI, KOR_EPU and VIX were found in the short-run, and finally (3) the EPU index in both countries affects the volatility of the stock market significantly.The paper concludes that the EPU index in different countries that have co-movements in stock price can provide useful information to investors in the stock and derivatives markets. RESEARCH BACKGROUNDThis paper utilized time series analysis that is one of the technics of traditional analytics and applied the important idea of data analysis derived from the current big data analytics.Lohr (2013) indicates that big data has been using since the 1990s and it represents the information assets characterized by such a high volume, velocity, and variety to require specific technology and analytical methods for its transformation into value.Kaplan and Haenlein (2018) define big data as data sets that are characterized by huge amounts of frequently updated data in various formats such as numeric, textual, images, and videos.Big data has attracted increasing attention