{"title":"Risk vs Upside uncertainty: application of quantile regression in investment analysis","authors":"Seema Rehman, J. A. Khilji, S. Sharif","doi":"10.1080/17520843.2021.1952639","DOIUrl":null,"url":null,"abstract":"ABSTRACT This paper examines the implications for risk taking in an emerging stock market, namely, Pakistan Stock Exchange (PSX), using tools that specifically account for the asymmetries. We perform sectoral level price data analysis to infer how investors behaved during various states of stock market such as bullish, bearish, stable etc. Using monthly data over 2005–2020, we estimate the Capital Asset Pricing Model (CAPM) using quantile regression framework, which is robust to distributional assumptions and can estimate the elasticities across the risk spectrum. The empirical findings suggest that the elasticities, namely, betas, are significant across quantiles. It implies that the risk-return relationship behaves differently across the market states and that the investors and policymakers, therefore, should calibrate their decisions accordingly.","PeriodicalId":42943,"journal":{"name":"Macroeconomics and Finance in Emerging Market Economies","volume":"16 1","pages":"264 - 284"},"PeriodicalIF":1.1000,"publicationDate":"2021-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/17520843.2021.1952639","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Macroeconomics and Finance in Emerging Market Economies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/17520843.2021.1952639","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 1
Abstract
ABSTRACT This paper examines the implications for risk taking in an emerging stock market, namely, Pakistan Stock Exchange (PSX), using tools that specifically account for the asymmetries. We perform sectoral level price data analysis to infer how investors behaved during various states of stock market such as bullish, bearish, stable etc. Using monthly data over 2005–2020, we estimate the Capital Asset Pricing Model (CAPM) using quantile regression framework, which is robust to distributional assumptions and can estimate the elasticities across the risk spectrum. The empirical findings suggest that the elasticities, namely, betas, are significant across quantiles. It implies that the risk-return relationship behaves differently across the market states and that the investors and policymakers, therefore, should calibrate their decisions accordingly.