In this paper we propose a Bayesian vector autoregressive model with time-varying parameters (BVAR-TVP) to examine the short-term predictability of exchange rates. An important contribution of the paper is the application of the BVAR-TVP model, for the first time, to daily data using information from financial markets. Another contribution is the production of forecasts in real time at the very short horizon of one-trading day-ahead typically used by traders and investors in financial markets. We employ financial criteria and recently developed statistical tests to assess the exchange rate predictability. We find that the BVAR-TVP model outperforms the random walk for all exchange rates. These forecast gains are due primarily to the time-variation of coefficients, and secondly to information from other financial markets. It is shown that international investors could have made statistically significant excess profits if they had followed an inter-day trading strategy based on the buy/sell signals generated by the model’s one-day-ahead exchange rate forecasts, even after allowing for transaction costs and risk factors.

This paper examines the effect that heterogeneous customer orders flows have on exchange rates by using a new, and the largest, proprietary dataset of weekly net order flow segmented by customer type across nine of the most liquid currency pairs. We make several contributions. Firstly, we investigate the extent to which customer order flow can help to explain exchange rate movements over and above the influence of macro-economic variables. Secondly, we address the issue of whether order flows contain (private) information which explain exchange rates changes. Thirdly, we look at the usefulness of order flow in forecasting exchange rate movements at longer horizons than those generally considered in the micro-structure literature. Finally we address the question of whether the out-of-sample exchange rate forecasts generated by order flows can be employed profitably in the foreign exchange markets.

Almost all relevant literature has characterized implied volatility as a biased predictor of realized volatility. In this paper we provide new time series techniques to investigate the validity of this finding in several foreign exchange options markets, including the Euro market. First, we develop a new fractional cointegration test that is shown to be robust to both stationary and non-stationary regions. Second, we employ both intra-day and daily data to measure realized volatility in order to assess the relevance of data frequency in resolving the bias. Third, we use data on implied volatility traded on the market. In contrast to previous studies, we show that the frequency of data used for measuring realized volatility within a fractionally cointegrating framework is important for the results of unbiasedness tests. Significantly, for many popular exchange rates, the use of intra-day rather than daily data affects the emergence of a different bias, as the possibility of a fractionally integrated risk premium admits itself!