We focus on the fundamental role of security analysts as information intermediaries using recent advances in the realized variance literature. We construct a signal-to-noise volatility ratio to examine the heterogeneity in the efficiency contributions of analysts' recommendations while controlling for the noise contained in price data. We find that only analysts' revisions with greater efficiency contributions generate significant stock price reactions in the directions expected by the analysts. Furthermore, these revisions increase the degree of informed trading in the options market and reduce the uncertainty related to the covered firms.

We investigate intraday return dynamics in currency markets around FOMC announcements. Using comprehensive high-frequency exchange rate data, we reveal that post-FOMC announcement returns are significantly low, cancelling out approximately 65% of positive pre-FOMC announcement drifts. These post-announcement reversals mainly result from uncertainty resolution and are mostly realized between 12 and 24 hours after FOMC announcements. This return behavior is significantly related to the negative jump volatilities driven by FOMC announcements. Our findings suggest that our signed jump volatility measures capture informational shocks and uncertainty resolutions and tend to be high under illiquid market conditions. (JEL G14, G15)

This article examines how realized variances predict cryptocurrency returns in the cross section using intraday data. We find that cryptocurrencies with higher variances exhibit lower returns in subsequent weeks. Decomposing total variances into signed jump and jump-robust variances reveals that the negative predictability is attributable to positive jump and jump-robust variances. The negative pricing effect is more pronounced for smaller cryptocurrencies with lower prices, less liquidity, more retail trading activities, and more positive sentiment. Our results suggest that cryptocurrency markets are unique because retail investors and preferences for lottery-like payoffs play important roles in the partial variance effects.

I study how realized idiosyncratic jumps play a role in pricing individual stocks. I find that stocks with high variances associated with positive idiosyncratic jumps tend to have low subsequent returns. To explain the negative premium, I show that positive idiosyncratic jump variances are important predictors for future skewness. Thus, my finding is consistent with investors’ preference for unusually large gains over short horizons. I demonstrate the economic significance of my results by highlighting the superior performance of a strategy based on variances associated with positive idiosyncratic jumps compared to strategies based on other variance measures.

This paper investigates how jump risks are priced in currency markets. We find that currencies whose changes are more sensitive to negative market jumps provide significantly higher expected returns. The positive risk premium constitutes compensation for the extreme losses during periods of market turmoil. Using the empirical findings, we propose a jump modified carry trade strategy, which has approximately two-percentage-point (per annum) higher returns than the regular carry trade strategy. These findings result from the fact that negative jump betas are significantly related to the riskiness of currencies and business conditions.

We investigate the predictability of jumps in currency markets and show the implications for carry trades. Formulating new currency jump analyses, we propose a general method to estimate the determinants of jump sizes and intensities at various frequencies. We employ a large panel of high-frequency data and identify significant predictive relationships between currency jumps and national characteristics. In addition, we find the patterns of intraday jumps (i.e., multiple currency jump clustering and time-of-day effects). Macroeconomic information releases in the United States, particularly FOMC announcements, lead to currency jumps. Using these jump predictors, investors can construct jump-robust carry trades to mitigate left-tail risks.

Bradley et al. (BCLO, 2014) find evidence that the time stamps reported in I/B/E/S for analysts' recommendations are systematically delayed giving the appearance that recommendations are uninformative. We review the findings of BCLO and extend their analyses along three dimensions. First, we show that time stamp delays are less likely to be associated with all-star analysts, affiliated analysts, and analysts from high reputation banks, but are more likely from independent analysts. Second, we show that recommendations from all-star analysts, analysts working for high reputation banks, and analysts who issued a previous influential recommendation are more likely to be influential. Finally, we examine post-recommendation drift following influential revisions and find post-revision returns of 18(-44) basis points for upgrades (downgrades) over the 2.5 hours following the revision.

This paper examines the relationship between realized daily skewness and future stock returns and investigates the impact of information releases on that relationship. We find that there exists a negative relationship between realized daily skewness and subsequent stock returns when there is no high-impact information release, but that the relationship becomes positive if the realized skewness is associated with such releases. We show that the profitability of a zero-investment portfolio can be enhanced by incorporating this positive relationship in the presence of high-impact information into investment strategies. As the positive relationship mainly results from riskier stocks with volume increase in the presence of information releases, we offer an explanation for this finding based on the divergence of investors’ opinion. JEL classification codes: G14, G17

We demonstrate that time stamps reported in I/B/E/S for analysts' recommendations released during trading hours are systematically delayed. Using newswire-reported time stamps, we find 30-minute returns of 1.83% (-2.10%) for upgrades (downgrades), but for this subset of recommendations we find corresponding returns of -0.07% (-0.09%) using I/B/E/S-reported time stamps. We also examine the information content of recommendations relative to management guidance and earnings announcements. Our evidence suggests that analysts' recommendations are the most important information disclosure channel examined.

This article investigates the predictability of jump arrivals in U.S. stock markets. Using a new test that identifies jump predictors up to the intraday level, I find that jumps are likely to occur shortly after macroeconomic information releases, such as the Federal Reserve announcements, nonfarm payroll reports, and jobless claims, as well as market index jumps. I also find firm-specific jump predictors related to earnings releases, analyst recommendations, past stock jumps, and dividend dates. Evidence suggests that distinguishing systematic jumps from idiosyncratic jumps is possible using the characteristics of jump predictors. Finally, I present a short-term jump size clustering. (JEL G10, C14)

Recent asset-pricing models incorporate jump risk through Lévy processes in addition to diffusive risk. This paper studies how to detect stochastic arrivals of small and big Lévy jumps with new nonparametric tests. The tests allow for robust analysis of their separate characteristics and facilitate better estimation of return dynamics. Empirical evidence of both small and big jumps based on these tests suggests that models for individual equities and overall market indices require incorporating Lévy-type jumps. The evidence of small jumps also helps explain why jumps in the market index are uncorrelated with jumps in its component equities.

Asset prices observed in financial markets combine equilibrium prices and market microstructure noise. In this paper, we study how to tell apart large shifts in equilibrium prices from noise using high frequency data. We propose a new nonparametric test which allows us to asymptotically remove the noise from observable price data and to discover jumps in fundamental asset values. We provide its asymptotic distribution to decide when such jumps occur. In finite samples, our test offers reasonable power for distinguishing between noise and jumps. Empirical evidence indicates that it is necessary to incorporate the presence of jumps in equilibrium prices.

This article introduces a new nonparametric test to detect jump arrival times and realized jump sizes in asset prices up to the intra-day level. We demonstrate that the likelihood of misclassification of jumps becomes negligible when we use high-frequency returns. Using our test, we examine jump dynamics and their distributions in the U.S. equity markets. The results show that individual stock jumps are associated with prescheduled earnings announcements and other company-specific news events. Additionally, S&P 500 Index jumps are associated with general market news announcements. This suggests different pricing models for individual equity options versus index options.

Griffin (2002) shows that Fama-French stock market factors are local, rather than global. In an integrated world financial market, however, local factors may be interrelated across countries. In this paper, we investigate the international linkages among local, country-specific stock market factors in order to better understand the structure of increasingly integrated world financial markets. In particular, the focus of our study is on (i) the cointegrating relationship and equilibrium dynamics among local factors and (ii) the pattern of international transmission of local factor innovations. In addressing these issues, we use daily returns to Fama-French three factors plus the momentum factor for each of the six major markets in our sample, during the period January 2000 - December 2005, and utilize the 'factor indices' constructed from the cumulative factor returns. The key findings of the paper are as follows. First, local factor indices are internationally cointegrated for each factor class, supporting the view that international stock markets are integrated at the factor level. This means that although stock market factors may be local, rather than global, these local factors are globally bound to each other through the long run equilibrium relationship. Furthermore, industrial outputs of our six sample economies also form a cointegrative system, implying that the cointegrating relations among local stock market factors may reflect, at least in part, international integration of the real economies. Second, following a system-wide shock to the cointegrating relations, the market and size factors revert to the equilibrium state much faster than the value and momentum factors, implying that international markets are more strongly integrated for the former than for the latter. Interestingly, the momentum factors initially 'overshoot' following a shock, drifting farther away from the equilibrium, before they begin to adjust toward the equilibrium, whereas other factors immediately embark on the adjustment processes. Third, the U.S. plays the dominant role in the market factor system, with its innovations eliciting clear global responses. However, the U.K. emerges as the most influential market in both the size and momentum factor systems. No single market plays a leadership role in the value factor system.

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We introduce a new nonparametric jump test for continuous-time asset pricing models. It distinguishes actual arrivals and different sizes of jumps. Asymptotic distribution of the test statistics is provided, and we demonstrate that it is desirable to use high-frequency data. We explore dynamic jump intensity structure through the test, and find empirical evidence of jump clustering in foreign currency exchange markets. Its implication for financial risk management, in particular value at risk, is also discussed.