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.

We propose a novel pricing factor for currency returns motivated by the market microstructure literature. Our factor aggregates order flow data to provide a measure of buying and selling pressure related to conventional currency trading strategies. It successfully prices the cross-section of currency returns sorted on the basis of forward discount and momentum. The association between our factor and currency returns differs according to the customer segment of the foreign exchange market. In particular, it appears that financial customers are risk-takers in the market, while nonfinancial customers serve as liquidity providers.

We develop a restriction that precludes implausibly high reward-for-risk in incomplete international economies to consider a theoretical problem that characterizes a lower bound on the covariance between stochastic discount factors (SDFs) subject to correct pricing. The problem is analytically solvable and synthesizes domestic and foreign SDFs into spanned and unspanned components. Our novelty is that exchange rate growth need not equal the ratio of SDFs and that the SDF correlations are plausibly lowered. Exploiting the realities of cross-country correlations of macroeconomic quantities, namely, consumption, wealth, dividend growths, and asset returns, our empirical investigation refutes the specification of complete markets. Received September 19, 2016; editorial decision August 31, 2017 by Editor Andrew Karolyi. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

This paper estimates a reduced-form model to assess the insolvency risk of General Insurance (GI) firms in the UK. In comparison to earlier studies, it uses a much larger sample including 30 years of data for 515 firms, and also considers a much wider set of possible determinants of insolvency risk. The empirical results suggest that macroeconomic and firm-specific factors both play important roles. Other key findings are the following: insolvency risk varies across firms depending on their business lines; there is default clustering in the GI industry; different reinsurance levels also affect the insolvency risk of insurance firms. The implications of these findings for regulators of GI firms under the newly launched Solvency II are discussed.