000 02261nam a2200361 i 4500
001 CR9781108893671
003 UkCbUP
005 20240909171424.0
006 m|||||o||d||||||||
007 cr||||||||||||
008 191220s2020||||enk o ||1 0|eng|d
020 _a9781108893671 (ebook)
020 _z9781108810135 (paperback)
040 _aUkCbUP
_beng
_erda
_cUkCbUP
043 _as------
050 4 _aHG3930
_b.S67 2020
082 0 4 _a332.4
_223
100 1 _aSosa, Luis Molinas,
_eauthor.
245 1 0 _aExchange rates in South America's emerging markets /
_cLuis Molinas Sosa, Caio Vigo Pereira.
264 1 _aCambridge :
_bCambridge University Press,
_c2020.
300 _a1 online resource (67 pages) :
_bdigital, PDF file(s).
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
490 0 _aCambridge elements. Elements in the economics of emerging markets, 2631-8598
500 _aTitle from publisher's bibliographic system (viewed on 07 Jul 2020).
520 _aSince Meese and Rogoff (1983) results showed that no model could outperform a random walk in predicting exchange rates. Many papers have tried to find a forecasting methodology that could beat the random walk, at least for certain forecasting periods. This Element compares the Purchasing Power Parity, the Uncovered Interest Rate, the Sticky Price, the Bayesian Model Averaging, and the Bayesian Vector Autoregression models to the random walk benchmark in forecasting exchange rates between most South American currencies and the US Dollar, and between the Paraguayan Guarani and the Brazilian Real and the Argentinian Peso. Forecasts are evaluated under the criteria of Root Mean Square Error, Direction of Change, and the Diebold-Mariano statistic. The results indicate that the two Bayesian models have greater forecasting power and that there is little evidence in favor of using the other three fundamentals models, except Purchasing Power Parity at longer forecasting horizons.
650 0 _aForeign exchange rates
_zSouth America.
700 1 _aPereira, Caio Vigo,
_eauthor.
776 0 8 _iPrint version:
_z9781108810135
856 4 0 _uhttps://doi.org/10.1017/9781108893671
942 _2ddc
_cEB
999 _c8893
_d8893