An application of a random level shifts model to the volatility of Peruvian stock and exchange rate returns. Issue 1 (2nd January 2016)

• Record Type: Journal Article
• Authors: Ojeda Cunya, Junior A.; Rodríguez, Gabriel
• Is Part Of: Macroeconomics and finance in emerging market economies. Volume 9:Issue 1(2016)
• Page Start: 34
• Page End: 55
• Publication Date: 2016-01-02
• Subjects: returns -- volatility -- long memory -- random level shifts model -- Kalman filter -- ARFIMA models -- GARCH models -- CGARCH models -- forecasting; C22; Developing countries -- Economic policy -- Periodicals; Monetary policy -- Developing countries -- Periodicals; Finance -- Developing countries -- Periodicals; 330.91724
• DOI: 10.1080/17520843.2015.1088880
• Languages: English
• ISSNs: 1752-0843

Abstract

Abstract : The literature has shown that the volatility of stock and forex rate market returns shows the characteristic of long memory. Another fact that is shown in the literature is that this feature may be spurious and volatility actually consists of a short memory process contaminated with random level shifts (RLS). In this paper, we follow recent econometric approaches estimating an RLS model to the logarithm of the absolute value of stock and forex returns. The model consists of the sum of a short-term memory component and a component of level shifts. The second component is specified as the cumulative sum of a process that is zero with probability '1-alpha' and is a random variable with probability 'alpha'. The results show that there are level shifts that are rare, but once they are taken into account, the characteristic or property of long memory disappears. Also, the presence of General Autoregressive Conditional Heteroscedasticity (GARCH) effects is eliminated when included or deducted level shifts. An exercise of out-of-sample forecasting shows that the RLS model has better performance than traditional models for modelling long memory such as the models ARFIMA ( p, d, q ).