Identifying Regime Shifts in South African Exchange Rates

Abstract

Abstract:Linear time series models are not able to capture the behaviour of many financial time series, as in the cases of inflation rates, exchange rates and stock prices data. To overcome this problem, nonlinear time series models are typically designed to capture these nonlinear features in the data. (3) In this paper, we use portmanteau test and likelihood ratio test (LR) test to detect nonlinear feature and to justify the use of 2-regime Markov switching autoregressive model (MS-AR) in South Africa exchange rate between 1995 and 2013. For model selection criteria (AIC and SBC) were used and for identifying best model error matrix such as MEA and MSE were used. The study compared the in-sample fitting between linear model and Markov switching model. From the error matrix (MEA and MSE) values, it is found that the MS -AR

Several explorations on how to construct an early warning system for local government debt risk in China

“…Regarding the contagion risk, Bianchi et al [38] take the network structure perspective and use the standard eigenvector centrality to model contagion in financial market; Anagnostou et al [39] incorporate contagion in portfolio credit risk model by using network theory; Berloco et al [40] use the network model to capture firms' fragility to shocks. Regarding the dynamic risk, Jutasompakorn et al [41] identify the banking crisis dates via the MS-AR model; Xaba et al [42] explore the performance of MS-AR model to forecast the quarterly exchange rate of South Africa; Makatjane and Kagiso [43] realize a dynamic early warning of the Johannesburg stock exchange all-share index through a two regime MS-AR model. Referring to the above practices, this paper will apply the network model and MS-AR model to construct a comprehensive EWS local government debt risk in China.…”