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We examine the role of high-frequency traders (HFT) in price discovery. Overall HFT play a positive role in price efficiency by trading in the direction of permanent price changes and in the opposite direction of transitory pricing errors on average days and the highest volatility days. This is done through their marketable orders. In contrast, HFT passive non-marketable orders are adversely selected in terms of the permanent and transitory components as these trades are in the direction opposite to permanent price changes and in the same direction as transitory pricing errors. HFT marketable orders’ informational advantage is sufficient to overcome the bid-ask spread and trading fees to generate positive trading revenues. Non-marketable limit orders also result in positive revenues as the costs associated with adverse selection are smaller than the bid-ask spread and liquidity rebates. HFT predicts price changes in the overall market over short horizons measured in the tens of seconds.

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Standard models of exchange rates, based on macroeconomic variables such as prices, interest rates, output, etc., are thought by many researchers to have failed empirically. We present evidence to the contrary. First, we emphasize the point that “beating a random walk” in forecasting is too strong a criterion for accepting an exchange rate model. Typically models should have low forecasting power of this type. We then propose a number of alternative ways to evaluate models. We examine in-sample fit, but emphasize the importance of the monetary policy rule, and its effects on expectations, in determining exchange rates. Next we present evidence that exchange rates incorporate news about future macroeconomic fundamentals, as the models imply. We demonstrate that the models might well be able to account for observed exchange-rate volatility. We discuss studies that examine the response of exchange rates to announcements of economic data. Then we present estimates of exchange-rate models in which expected present values of fundamentals are calculated from survey forecasts. Finally, we show that outof-sample forecasting power of models can be increased by focusing on panel estimation and long-horizon forecasts.

Abstract—We propose a formal test of the hypothesis that energy prices are predetermined with respect to U.S. macroeconomic aggregates. The test is based on regressing changes in daily energy prices on daily news from U.S. macroeconomic data releases. Using a wide range of macroeconomic news, we find no compelling evidence of feedback at daily or monthly horizons, contradicting the view that energy prices respond instantaneously to macroeconomic news and consistent with the commonly used identifying assumption that there is no feedback from U.S. macroeconomic aggregates to monthly innovations in energy prices. I.

This paper addresses whether macro news arrivals affect currency markets over time. The null from macro exchange-rate theory is that they do not: macro news is impounded in exchange rates instantaneously. We test this by examining the effects of news on subsequent trades by end-user participants (such as hedge funds, mutual funds, and non-financial corporations). News arrivals induce subsequent changes in trading in all of the major end-user segments. These induced changes remain significant for days. Induced trades also have persistent effects on prices. Currency markets are not responding to news instantaneously.

The zero lower bound on nominal interest rates has constrained the Federal Reserve’s setting of the federal funds rate since December 2008. According to many macroeconomic models, this should have greatly reduced the effectiveness of monetary policy and increased the efficacy of fiscal policy. However, standard macroeconomic theory also implies that private-sector decisions depend on the entire path of expected future short-term interest rates, not just the current level of the overnight rate. Thus, interest rates with a year or more to maturity are arguably more relevant for the economy, and it is unclear to what extent the zero bound has constrained those yields. In this paper, we propose a novel approach to measure the effects of the zero lower bound on interest rates of any maturity. We compare the sensitivity of interest rates of various maturities to macroeconomic news during periods when short-term interest rates were very low to that during normal times. We find that yields on Treasury securities with a year or more to maturity were surprisingly responsive to news throughout 2008–10, suggesting that monetary and fiscal policy were likely to have been about as effective as usual during this period. Since mid-2011, the zero lower bound has been a greater constraint. We offer two explanations for these findings: First,

A rapidly growing literature has documented important improvements in volatility measurement and forecasting performance through the use of realized volatilities constructed from high-frequency returns coupled with relatively simple reduced form time series modeling procedures. Building on recent theoretical results from Barndorff-Nielsen and Shephard (2003c) for related bi-power variation measures involving the sum of high-frequency absolute returns, the present paper provides a practical framework for non-parametrically measuring the jump component in the realized volatility measurements. Exploiting these ideas for a decade of high-frequency five-minute returns for the DM/ $ exchange rate, the S&P500 aggregate market index, and the 30-year U.S. Treasury Bond, we find the jump components to be distinctly less persistent than the contribution to the overall return variability originating from the continuous sample path component of the price process. Explicitly including the jump measure as an additional explanatory variable in an easy-to-implement reduced form model for the realized volatilities results in highly significant jump coefficient estimates at the daily, weekly and quarterly forecasts horizons. As such, our results hold promise for improved financial asset allocation, risk management, and derivatives pricing, by separate modeling, forecasting and pricing of the continuous and jump components of the total return variability.

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