Pub Date : 2022-12-01DOI: 10.57017/jorit.v1.2(2).02
I. Raifu, A. E. Ogbonna
The study assessed the hedge or safe-haven property of five cryptocurrencies for stocks of three COVID-19 worst-hit African countries. We address two main concerns bordering on the predictive capacity of African stocks for cryptocurrency returns and the safe-haven property that cryptocurrencies could offer to African stocks. A distributed lag model, with explicitly incorporated salient statistical features, was adopted based on its efficient management of parameter proliferation and estimation biases. We ascertained the model’s in-sample predictability and evaluate its out-of-sample forecasts performance in comparison with the historical average model, using Clark and West statistics. While African stocks significantly predicted cryptocurrency returns, the cryptocurrency-stocks nexus revealed the diversifier and safe-haven property of cryptocurrencies for African stocks in periods of normalcy and crisis/pandemic, respectively. Our predictive model outperformed the historical average model in the out-of-sample. Our results may be sensitive to cryptocurrency-stocks nexus and sample periods but not the out-of-sample forecast horizons
{"title":"Safe-Haven Effectiveness of Cryptocurrency: Evidence from Stock Markets of COVID-19 Worst-Hit\u0000 African Countries","authors":"I. Raifu, A. E. Ogbonna","doi":"10.57017/jorit.v1.2(2).02","DOIUrl":"https://doi.org/10.57017/jorit.v1.2(2).02","url":null,"abstract":"The study assessed the hedge or safe-haven property of five cryptocurrencies for stocks of\u0000 three COVID-19 worst-hit African countries. We address two main concerns bordering on the\u0000 predictive capacity of African stocks for cryptocurrency returns and the safe-haven property\u0000 that cryptocurrencies could offer to African stocks. A distributed lag model, with explicitly\u0000 incorporated salient statistical features, was adopted based on its efficient management of\u0000 parameter proliferation and estimation biases. We ascertained the model’s in-sample\u0000 predictability and evaluate its out-of-sample forecasts performance in comparison with the\u0000 historical average model, using Clark and West statistics. While African stocks significantly\u0000 predicted cryptocurrency returns, the cryptocurrency-stocks nexus revealed the diversifier and\u0000 safe-haven property of cryptocurrencies for African stocks in periods of normalcy and\u0000 crisis/pandemic, respectively. Our predictive model outperformed the historical average model in\u0000 the out-of-sample. Our results may be sensitive to cryptocurrency-stocks nexus and sample\u0000 periods but not the out-of-sample forecast horizons\u0000","PeriodicalId":165708,"journal":{"name":"Journal of Research, Innovation and Technologies (JoRIT)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116935065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}