This paper examines changes in market quality resulting from the smaller tick size of the interbank foreign exchange market. Coupled with the lower tick size, the special composition of traders and their order placement strategies created a suitable environment for high-frequency traders (HFT’s) to implement sub-penny jumping strategy to front-run human traders. We show that the spread declined following the introduction of decimal pip pricing. However, benefits of spread reduction were mostly absorbed by the HFT’s. Market depths were also significantly reduced with the occupation of the top of the order book by HFT’s. This new environment changed the market maker-market taker composition between different traders and altered price impacts of the order flows.

From the market microstructure perspective, technical analysis can be profitable when informed traders make systematic mistakes or when uninformed traders have predictable impacts on price. However, chartists face a considerable degree of trading uncertainty because technical indicators such as moving averages are essentially imperfect filters with a nonzero phase shift. Consequently, technical trading may result in erroneous trading recommendations and substantial losses. This paper presents an uncertainty reduction approach based on fuzzy logic that addresses two problems related to the uncertainty embedded in technical trading strategies: market timing and order size. The results of our high-frequency exercises show that ‘fuzzy technical indicators’ dominate standard moving average technical indicators and filter rules for the Euro-US dollar (EUR-USD) exchange rates, especially on high-volatility days.

Volatility clustering, with autocorrelations of the hyperbolic decay rate, is unquestionably one of the most important stylized facts of financial time series. This paper presents a market microstructure model that is able to generate volatility clustering with hyperbolically decaying autocorrelations via traders with multiple trading frequencies, using Bayesian information updates in an incomplete market. The model illustrates that signal extraction, which is induced by multiple trading frequencies, can increase the persistence of the volatility of returns. Furthermore, we show that the volatility of the underlying time series of returns varies greatly with the number of traders in the market.

Counterparty risk is an important determinant of corporate credit spreads. However, there are only a few techniques available to isolate it from other factors. In this paper we describe a model of financial networks that is suitable for the construction of proxies for counterparty risk. Using data on North American supplier–customer network of public companies, we find that, for each supplier, counterparties’ leverage and option implied volatilities are significant determinants of corporate credit spreads in the period after the 2008–2009 U.S. recession. Our findings are robust after controlling for several idiosyncratic, industry, and market factors.

We consider a financial network where banks are heterogeneous in scale and each bank has only local knowledge regarding the network. Each bank must make counterparty and portfolio decisions while anticipating uncertainty regarding the network structure. Such network uncertainty is an important consideration in banks’ risk management practice, which aims to minimize the effect of exogenous liquidity shocks and hedge against possible fire-sale in asset markets. We show that network uncertainty gives rise to an endogenous core-periphery structure which is optimal in mitigating financial contagion yet concentrates systemic risk at the core of big banks.