earticle

영어

The new coronavirus(COVID-19) has shocked economies around the world, and the stock market is also facing unprecedented conditions due to the effects of COVID-19. The impact of COVID-19 on the global economy is clearly different from typical cyclical fluctuations in the traditional economic development process and economic losses from the COVID-19 pandemic will also surpass the extent of endogenous and extreme events that have occurred in the past. Assessing and understanding the economic impact of COVID-19 has become an important issue. China is the first country to respond to COVID-19, and has made great efforts to boost shrinking production and consumption. However, since the level of the virus affects different industries, it is necessary to analyze the movement of the stock market at the industrial level. The Chinese stock market was a closed market where foreigners were not allowed to invest, but it has grown rapidly as an investment destination that investors around the world pay attention to following the government's stance of opening and reforming the capital market. The leading stock indexes, the Shanghai Composite Index and the Shenzhen Component Index, represent the entire flow of the Chinese stock market, with trading centered on large-cap stocks centered on traditional industries in the Shanghai market and new industry-oriented stocks such as IT and bio in the Shenzhen market. This paper tries to estimate the dynamic linear latent factor model (DLLFM) with jump in order to find jump risk, heteroscedasticity and time varying correlations in Shenzhen Stock Markets. In addition, the impact of disaster risk on volatility by industry was also analyzed by including the occurrence and diffusion of COVID-19 in the sample period. The motivation for the study began with the view that the economic crisis caused by shot down in China, where COVID-19 first occurred, could be quantitatively assessed from an industry-specific perspective on how it spreaded to the stock market. In particular, this study has utilized DLLFM because that model can measure the coefficients of time varying correlations among those various industries of Shenzhen Stock Market. Using six major Industrial Stock Index such as Manufacturing, IT, Finance, Transportation, Whole sale & Retail, Construction from 1/5/2015 to 8/31/2020, this study finds the evidence of common factor and time-varying correlations in addition to the industry-specific idiosyncratic risk. According to the main estimated results of this paper, jump risk of common factor comes every 7.82 trading days in Shenzhen stock markets and about 87 percent of the common factor of the Shenzen stock markets can be explained by Shanghai market risk, which is China stock market risk. Also, some part of unobserved common factor of the Shenzen stock markets may be explained by foreign exchange market risk of China. And the portion of foreign exchange market risk may be increasing as the Chinese government announces the new method of calculating standard value of yuan exchange rate in August 2015. So far as concerning the time varying correlations among those indices, the levels of correlations seem to be comparatively high, but those levels are going down transiently after the occurrence of COVID-19. In this study, we are trying to investigate the Shenzen stock markets where president Xi Jinping has visited couple of times as the center for bio, information, communication, venture markets for the future of Chinese economy to cope with US-China trade dispute. We have been very much interested in finding the real nature of the latent common factor of the Shenzen stock markets. Conclusively, the findings are, some of them Chinese systemic risk, some of them foreign exchange risk. All of these suggest that the use of multivariate models such as dynamic linear latent model seems to be essential to comprehensively analyze those industry-specific indices in the course of the transition of disaster risks such as COVID-19. Also unobserved common jump and heteroscedasticity seem to be those main characteristics of chinese financial time series. According to the main results of this paper, two pillars of the Chinese stock markets, Shanghai and Shenzhen, may look quite similar, but seem to be different from the view point of the portfolio or index investments. Somehow it would be better that investors should pay attention to those characteristics of the latent common factor of the Shenzen stock markets when it comes to the risk management strategies. It seems that the similarity of two stock markets from the stand point of the nature of risk, mainly comes from the financial regulations of Chinese government.

한국어

본 연구에서는 중국 선전주식시장을 대상으로 비관측 공통요인을 추정하고, COVID-19 확산에 따른 업종별 상관관계의 변화여부를 실증분석 하였다. 이를 위해 선전시장의 업종별 지수를 포괄적으로 분석할 수 있는 다변량 모형으로서 DLLFM(Dynamic Linear Latent Factor Model)을 도입하였다. 본 연구의 분석기간은 2015년 1월 5일부터 2020년 8월 31일까지이며, 분석대상은 선전시장 6개 업종(제조, IT, 금융, 운송, 도･소매, 건설)의 일별 지수이다. 실증분석결과, 첫째 One Factor 잠재적 요인을 추정할 수 있는 모형의 특성을 활용하여 이분산성과 점프 리스크의 특성으로 설명될 수 있는 공통요인의 존재를 확인하였다. 둘째 시장의 지배적 흐름이 존재하는 가운데에서도 개별업종의 경제상황을 반영한 고유의 변동성 양상이 부분적으로 포착되었다. 셋째 재난리스크에 가장 민감하게 반응할 것으로 예상되는 건설업을 중심으로 상관계수를 추정한 결과, 전반적으로 높은 수준의 상관관계를 보였지만 COVID-19 발생 이후에 일시적으로 하락하는 현상이 확인되었다. 넷째 선전시장 공통요인의 실체는 약 87%가 중국전체주식시장의 시스템 리스크이고 위안/달러 환율의 위험도 상당부분 반영되어 있으며, 특히 외환시장의 위험 구성비율은 최근 들어 점점 증가추세에 있는 것으로 판단된다.