Using a new approach relying on news wire reports, we estimate the proportion of secret interventions (i.e., unreported official interventions) in the foreign exchange markets that have been conducted by the three major central banks since 1985. We therefore revisit the estimation of conditional probabilities of secret operations and compute them by both central bank and operation type. The proportion of secret interventions is found to be lower for concerted operations and to display a great deal of variability over time as well as across the three major central banks. Our analysis reveals that the Bank of Japan has recently adopted an intervention policy more based on secret operations.
Financial series occasionally exhibit large changes. To deal with those events, we assume that the observed return series consists of a conditionally Gaussian ARMA-GARCH (or -GJR) model contaminated by an additive jump component. In this framework, we propose a new test for additive jumps. The test is based on standardised returns, where the first two conditional moments of the non-contaminated observations are estimated in a robust way. Simulation results indicate that the test has very good finite sample properties, i.e. correct size and high proportion of correct jump detection. We apply our test on daily returns and detect less than 1% of jumps for the three exchange rates and between 1 and 3% of jumps for about 50 large capitalization stocks from the NYSE. Once jumps have been filtered out, all series are found to be conditionally Gaussian. We also find that simple GARCH-type models estimated on filtered returns deliver better out-of-sample forecasts of the conditional variance than GARCH and GAS models estimated on raw data.
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