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Exchange Rate Regime Analysis for the Indian Rupee Achim Zeileis
| Content Provider | Semantic Scholar |
|---|---|
| Author | Shah, Ajay Patnaik, Ila |
| Copyright Year | 2009 |
| Abstract | We investigate the Indian exchange rate regime starting from 1993 when trading in the Indian rupee began up to the end of 2007. This reproduces the analysis from Zeileis, Shah, and Patnaik (2008) which includes a more detailed discussion. 1 Analysis Exchange rate regime analysis is based on a linear regression model for cross-currency returns. A large data set derived from exchange rates available online from the US Federal Reserve at http://www.federalreserve.gov/releases/h10/Hist/ is provided in the FXRatesCHF data set in fxregime. > library("fxregime") > data("FXRatesCHF", package = "fxregime") It is a“zoo” series containing 25 daily time series from 1971-01-04 to 2009-08-14. The columns correspond to the prices for various currencies (in ISO 4217 format) with respect to CHF as the unit currency. India is an expanding economy with a currency that has been receiving increased interest over the last years. India is in the process of evolving away from a closed economy towards a greater integration with the world on both the current account and the capital account. This has brought considerable stress upon the pegged exchange rate regime. Therefore, we try to track the evolution of the INR exchange rate regime since trading in the INR began in about 1993 up to the end of 2007. The currency basket employed consists of the most important floating currencies (USD, JPY, EUR, GBP). Because EUR can only be used for the time after its introduction as official euro-zone currency in 1999, we employ the exchange rates of the German mark (DEM, the most important currency in the EUR zone) adjusted to EUR rates. The combined returns are denoted DUR below in FXRatesCHF: > inr inr_lm inr_efp plot(inr_efp, aggregate = FALSE, ylim = c(-1.85, 1.85)) Its visualization in Figure ̃1 shows that there is significant instability because two processes (intercept and variance) exceed their 5% level boundaries. More formally, the corresponding double maximum can be performed by > sctest(inr_efp) M-fluctuation test data: inr_efp f(efp) = 1.7242, p-value = 0.03099 This p ̃value is not very small because there seem to be several changes in various parameters. A more suitable test in such a situation would be the Nyblom-Hansen test > sctest(inr_efp, functional = meanL2BB) M-fluctuation test data: inr_efp f(efp) = 3.1147, p-value = 0.005 However, the multivariate fluctuation process is interesting as a visualization of the changes in the different parameters. The process for the variance σ2 has the most distinctive shape revealing at least four different regimes: at first, a variance that is lower than the overall average (and hence a decreasing process), then a much larger variance (up to the boundary crossing), a much smaller variance again and finally a period where the variance is roughly the full-sample average. Other interesting processes are the intercept and maybe the USD and DUR. The latter two are not significant but have some peaks revealing a decrease and increase, respectively, in the corresponding coefficients. To capture this exploratory assessment in a formal way, a dating procedure is conducted for 1, . . . , 10 breaks and a minimal segment size of 20 observations. > inr_reg |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://cran.r-project.org/web/packages/fxregime/vignettes/INR.pdf |
| Alternate Webpage(s) | http://cran.at.r-project.org/web/packages/fxregime/vignettes/INR.pdf |
| Alternate Webpage(s) | http://cran.cnr.berkeley.edu/web/packages/fxregime/vignettes/INR.pdf |
| Alternate Webpage(s) | http://cran.md.tsukuba.ac.jp/web/packages/fxregime/vignettes/INR.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
