Investment banks that develop new corporate securities systematically lead the new underwriting market despite being imitated early by equally competitive rivals. We study how innovators and imitators set underwriting fees in order to identify empirically the source of this advantage. Using data of innovative securities since 1985, we do find that innovators set systematically higher fees than imitators. This premium decreases as more issues occur, and faster for later generation products. Imitation is also quicker for later generations. This evidence supports the hypothesis that the innovator has superior skills in structuring any given issue of the new security

This paper examines the impact of foreign bank entry on China’s banking performance. An innovation in this study is that it constructs a spatially disaggregated measure of foreign bank presence, employing location data of foreign bank branches. Using this measure, the study examines the relationship between foreign bank presence and domestic banking performance in China. The results show that the spatially disaggregated measure not only improves the assessment of the impact of foreign bank presence but also resolves unexplained discrepancies found in existing empirical studies contingent on aggregate measures. Importantly, the study provides strong empirical evidence that foreign bank entry is supportive of a more competitive and efficient banking industry in China

The liquidity effect is the negative relationship between the supply of federal funds and the overnight federal funds rate. Deviations of the federal funds rate from its target can be interpreted as demand innovations for federal funds. Permanent adjustments to demand are modeled as an unobserved component and estimated using the Kalman filter to identify liquidity effects. The demand-based approach for identifying the liquidity effect contrasts previous work which concentrates on errors forecasting the supply of federal funds. This paper finds a liquidity effect several times larger than that from previous studies, indicating the market for federal funds is less liquid than previously thought. The effect of a $1 billion increase in open market operations over a 1-week period is a decrease of the federal funds rate by about 12 basis points

Historically, attempts to solve the liquidity puzzle focus on narrowly defined monetary aggregates, such as non-borrowed reserves, the monetary base, or M1. Many of these efforts fail to find a short-term negative correlation between interest rates and monetary policy innovations. More recent research uses sophisticated macroeconomic and econometric modeling. However, little research has investigated the role measurement error plays in the liquidity puzzle, since in nearly every case, work investigating the liquidity puzzle has used one of the official monetary aggregates, which have been shown to exhibit significant measurement error. In this paper, we examine the role that measurement error plays in the liquidity puzzle by (i) providing a theoretical framework explaining how the official simple-sum methodology can lead to a liquidity puzzle, and (ii) testing for the liquidity effect by estimating an unrestricted VAR

We use fractionally-integrated time-series models to investigate the joint dynamics of equity trading volume and volatility. Bollerslev and Jubinski (1999) show that volume and volatility have a similar degree of fractional integration, and they argue that this evidence supports a long-run view of the mixture-of-distributions hypothesis. We examine this issue using more precise volatility estimates obtained using high-frequency returns (i.e., realized volatilities). Our results indicate that volume and volatility both display long memory, but we can reject the hypothesis that the two series share a common order of fractional integration for a fifth of the firms in our sample. Moreover, we find a strong correlation between the innovations to volume and volatility, which suggests that trading volume can be used to obtain more precise estimates of daily volatility for cases in which high-frequency returns are unavailable

Leveraged ETFs are a recent and very successful financial innovation. They provide daily returns that are in a multiple or a negative multiple of the daily returns on a market benchmark. In this paper, we examine the characteristics, trading statistics, pricing efficiency and tracking errors of a sample of leveraged ETFs. We find that these ETFs are traded mainly by retail traders with very short holding periods. Price deviations (from NAV) are small on average, but large premiums and discounts are prone to occur. More interestingly, the behavior of premiums is different between bull (i.e., those with a positive multiple) and bear ETFs (i.e., those with a negative multiple). Our findings are consistent with the argument that the end-of-day rebalancing of the funds’ exposures increases market volatility at the close of a trading day. As for tracking errors, they are small for holding periods of up to a week, but become increasingly larger for longer horizons