Journal of Financial Markets最新文献

Arbitrage opportunities in markets for cryptocurrencies are well-documented. In this paper, we confirm that they exist; however, their magnitude decreased greatly from April 2018 onward. Analyzing various trading strategies, we show that it is barely possible to exploit existing price differences since then. We discuss and test several mechanisms that may be responsible for the increased market efficiency and find that informed trading is correlated with a reduction in arbitrage opportunities.

We study competition between European Union’s Central CounterParties (CCPs) on the credit default swap (CDS) market. Using data on market shares, we show that CCPs have a monopoly for single-name CDSs and compete on indices along various dimensions. Using transactions data, we focus on the major dealers who alternatively clear their transactions on the two main CCPs. Estimating their choice of CCP reveals that fees, CCPs’ robustness and activity, dealers’ risk, and market volatility are significant. Dealers’ positions indicate that saving on collateral costs is secondary relative to the benefits of dual membership and quality.

Managed futures funds are predominantly trend-followers. By analyzing positioning data, we provide novel evidence for this claim and estimate signals applied by these funds. We write trend-followers aggregate position as a weighted sum of past daily returns and use a generalized ridge regression for regularization and parameter estimation. This procedure prevents overfitting but remains flexible enough to capture various patterns. For the 23 commodities considered, trend-following can explain speculators’ position changes with an average $R^2$ of more than 40%. Finally, we document that producers act as contrarians in a way that closely mirrors the behavior of momentum traders.

We enrich the literature by extracting ambiguity from the options market. Our results show that options market ambiguity contains information regarding future market excess returns, both in the U.S. market and international markets, and the predictive power of options market ambiguity is adjusted by the level of market fear indicated by the implied variance. The findings also show that the discount rate is a critical channel for the forecasting ability of options market ambiguity. The linkages between options market ambiguity and the bond and CDS spreads provide additional evidence for the relationship between ambiguity and the discount rate.

Firms with lower profitability have lower expected returns because such firms perform better than expected when market volatility increases. The better-than-expected performance arises because unprofitable firms are distressed and volatile, their equity resembles a call option on the assets, and call options value increases with volatility, all else fixed. Consistent with this hypothesis, the profitability anomaly and its exposure to aggregate volatility risk are stronger for distressed and volatile firms; for such firms, aggregate volatility risk explains roughly half of the profitability anomaly, while in single sorts on profitability about 70% of the anomaly is explained.

The long-run underperformance of initial public offerings (IPOs) suggests that aftermarket prices are overvalued. According to the theory of heterogeneous beliefs and short-sale constraints, the aftermarket price of IPOs is overvalued; in addition, their performance deteriorates when the mean level of optimism and degree of divergence of investors’ opinions increase. I examine this phenomenon by estimating the mean and divergence of investor opinion distribution by focusing on Japanese auction-method IPOs. According to the results, both optimism and divergence cause the overvaluation of IPO’s first-day market price; however, only the mean level of optimism is statistically significant in explaining post-IPO underperformance.

We construct a model of debt maturity structure and show how a firm trades off between the costs of market liquidity risk and rollover risk. We show that an exogenous shock that directly increases one type of liquidity risk would induce the firm to alter its debt maturity structure and partially offset the impact of the shock by raising its exposure to the other type of risk (i.e., spillover effects exist). We also show that the spillover from market liquidity risk (rollover risk) to rollover risk (market liquidity risk) is more (less) pronounced during recessions or in competitive markets.

We analyze the relation between ETF ownership and firm-specific information in corporate bond returns. ETF ownership appears to weaken bond price informativeness by altering the flow of firm-specific information to bonds. Bonds with low (high) ETF ownership are sensitive (insensitive) to the same information as equity. Using earnings announcements, we show that bonds with low ETF ownership react to earnings news, but bonds with high levels do not. Moreover, we find a positive association between investment-grade bond return comovement with the market and ETF ownership, implying that return variation is less attributable to firm-level information as ETF ownership increases.

With machine learning tools, we document that firm fundamentals have explanatory power on the shape of the option implied volatility (IV) curve that is both economically and statistically significant. We also find that, after accounting for fundamentals, the associated IV process can generate overreaction in the long-term IV with respect to change in the short-term IV, and can allow a positive profit from at-the-money straddle writing, explaining puzzling patterns in the literature. We also provide a simple model linking the IV to firm fundamentals, which permits realistic IV curves and is consistent with the empirical findings.

I provide a new monthly cross-sectional measure of stock market tail risk, SCSTR, defined as the average of the daily cross-sectional tail risk, rather than the tail risk of the pooled daily returns within a month. Through simulations, I find that SCSTR better captures monthly tail risk rather than merely the tail risk on specific days within a month. In an extended period from 1964 until 2018, this difference is important in generating strong in- and out-of-sample predictability and performs better than the historical risk premium and other commonly-used predictors for short- and long-term horizons.