We study the economic sources of stock–bond return comovements and their time variation using a dynamic factor model. We identify the economic factors employing a semistruc-tural regime-switching model for state variables such as interest rates, inflation, the output gap, and cash flow growth. We also view risk aversion, uncertainty about inflation and output, and liquidity proxies as additional potential factors. We find that macroeconomic fundamentals contribute little to explaining stock and bond return correlations but that other factors, especially liquidity proxies, play a more important role. The macro factors are still important in fitting bond return volatility, whereas the “variance premium ” is criti-cal in explaining stock return volatility. However, the factor model primarily fails in fitting covariances. (JEL G11, G12, G14, E43, E44) Stock and bond returns in the United States display an average correlation of about 19 % during the post-1968 period. Shiller and Beltratti (1992) un-derestimate the empirical correlation using a present value with constant dis-count rates, whereas Bekaert, Engstrom, and Grenadier (2005) overestimate it in a consumption-based asset pricing model with stochastic risk aversion. Yet,

We study the impact that algorithmic trading, computers directly interfacing with trading platforms, has had on price discovery and volatility in the foreign exchange market, using high frequency data representing a majority of global interdealer trading in …ve major currency pairs from 2006 to 2007. Our dataset contains precise observations of the fraction and the direction of the computer-generated trades each minute. As such, it allows us to analyze the possible links between algorithmic trading and market volatility, to identify whose trades have a more permanent impact on prices, and to study how correlated algorithmic trades are. We …nd that non-algorithmic order ‡ow accounts for most of the (long-run) variance in exchange rate returns, i.e. non-algorithmic traders are better “informed”. We also …nd that there is, in some cases, an over-reaction of the price to algorithmic order ‡ow. Thee is some evidence that algorithmic trades tend to be correlated, suggesting that the algorithmic strategies used in the market may not be as diverse as those used by non-algorithmic traders.

Abstract not found

Stochastic volatility (SV) models are used heavily within the fields of financial economics and mathematical finance to capture the impact of time-varying volatility on financial markets and decision making. The development of the subject has been highly multidisciplinary, with results drawn from financial economics, probability theory and econometrics blending to produce methods that

Current practice largely follows restrictive approaches to market risk measurement, such as historical simulation or RiskMetrics. In contrast, we propose flexible methods that exploit recent developments in financial econometrics and are likely to produce more accurate risk assessments, treating both portfoliolevel and asset-level analysis. Asset-level analysis is particularly challenging because the demands of real-world risk management in financial institutions – in particular, real-time risk tracking in very high-dimensional situations – impose strict limits on model complexity. Hence we stress powerful yet parsimonious models that are easily estimated. In addition, we emphasize the need for deeper understanding of the links between market risk and macroeconomic fundamentals, focusing primarily on links among equity return volatilities, real growth, and real growth volatilities. Throughout, we strive not only to deepen our scientific understanding of market risk, but also cross-fertilize the academic and practitioner communities, promoting improved market risk measurement

This paper presents preliminary findings and is being distributed to economists and other interested readers solely to stimulate discussion and elicit comments. The views expressed in the paper are those of the authors and are not necessarily reflective of views at the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

We propose a fundamentals-based econometric model for the weekly changes in the euro-dollar rate with the distinctive feature of mixing economic variables quoted at di¤erent frequen-cies. The model obtains good in-sample …t and, more importantly, encouraging out-of-sample forecasting results at horizons ranging from one-week to one-month. Speci…cally, we obtain statistically signi…cant improvements upon the hard-to-beat random walk model using tradi-tional statistical measures of forecasting error at all horizons. Moreover, our model obtains a great improvement when we use the direction of change metric, which has more economic relevance than other loss measures. With this measure, our model performs much better at all forecasting horizons than a naive model that predicts the exchange rate as an equal chance to go up or down, with statistically signi…cant improvements.

In this paper, we compare four months of Reuters EFX high frequency indicative data with D2000-1 inter-dealer transaction data for DEM/USD and GBP/USD. Contrary to previous studies, we find, using various information measures, that the matched tick-by-tick indicative data bear no qualitative difference from the transaction data, and have higher information content. Expanding the system to include order flow, due to its growing importance in exchange rate theory, we find that indicative data has a similar impact on order flow as transaction data. However, order flow has no impact on either price. We would like to thank Martin Evans for making available to us the transaction data and an anonymous referee for useful comments. We would also like to thank the Emerging Markets Group, Cass Business School, in London for financial support. 1

Abstract Investors with limited attention have an incentive to focus on summary statistics rather than individual pieces of information. We use this observation to form a test of the impact of limited attention on the aggregate stock market. We examine the market response to a macro economic release that is purely a summary statistic, the U.S. Leading Economic Index (LEI). Consistent with the limited attention hypothesis, we show that the LEI announcement has an impact on aggregate stock returns, return volatility, and trading volume. Furthermore, we …nd that the response to the LEI is higher for stocks which inattentive investors are more likely to trade.

This paper examines the ability of government bond fund managers to time the market, based on their holdings of Treasury securities during the period 1997-2006. We find that, on average, government bond fund managers exhibit significant and positive timing ability at the one-month horizon, under a holdings-based timing measure. In particular, fund managers specializing in Treasury securities are more likely to better time the market than general government bond fund managers. We also find that more successful market timers tend to have relatively higher Morningstar ratings, larger fund flows, lower expense ratios, higher Sharpe ratios, and higher concentrations of holdings of Treasury securities.