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Jumps and diffusive changes in stock prices are different ways in which information is reflected in the prices. We use nonparametric methods to decompose returns on individual stocks into jumps and diffusive components. Contrary to the conventional assumption that jump intensity is positively related to diffusive variance, we find abundant evidence that realized jump intensity and diffusive variance are uncorrelated or negatively related for a majority of stocks. The jump-diffusion beta is found to positively contribute to the implied volatility smile of options on individual stocks. We also document a counter-cyclical pattern of realized jump sizes, which challenges the i.i.d. jump size assumption commonly seen in the literature. The findings provide useful guidance on modeling option prices.

Background: The pneumonia associated with the novel coronavirus (Severe Acute Respiratory Syndrome Coronavirus 2, SARS-CoV-2) is breaking out in Wuhan, China W

The third-party platform channel has been widely used in addition to the traditional retail channel to sell products. In practice, some third-party platforms provide financing services to small businesses that sell products on them. However, few studies addressed the capital constraint problem faced by a manufacturer who sells products through both retailers and third-party platforms, especially when considering the third-party platform’s lending service behavior. This research establishes a model where a capital-constrained manufacturer sells products through a retailer and a third-party platform and may pursue a financing strategy by borrowing from the third-party platform (3PF), the retailer (RF), or the bank (BF). We investigate the impact of the third-party platform’s or retailer’s dual role—lending provider and channel participant—on dual-channel operational management and study the manufacturer’s financing strategy choices by comparing profits under different financing strategies. The results of our analysis show that for the manufacturer, the 3PF strategy is always better than the BF strategy. Furthermore, the manufacturer is more likely to prefer the RF strategy to the 3PF strategy as the channel competition increases or as the revenue sharing rate or unit production cost decreases. We also find that the retailer’s retail price increases as the revenue sharing rate increases if there is no capital constraint, but it decreases under the BF and 3PF strategies. This indicates that the manufacturer’s financing behavior has a significant impact on the retailer’s retail price decision. We extend our model by considering random demand and find that these findings continue to hold when the potential demand equals its expected value.

We present an analytical framework for the forward-looking measurement of extreme market risk. In contrast to standard techniques relying on past return data, we propose to extract Value-at-Risk and Expected Shortfall under the physical measure from current option prices. Our empirical evidence suggests that the resulting estimates accurately capture the tail risk of the S&P 500 and that they quickly react to changing market conditions. Compared to dynamic tail risk forecasts driven by past returns, our forward-looking estimates are relatively higher during good times and lower during adverse economic conditions, which could reduce the amplification effects of conventional dynamic risk management policies.

We examine whether and how payout policy affects credit risk using evidence from the credit default swap (CDS) market. CDS spreads increase substantially in response to announcements of dividend cuts, especially during recessions and among firms experiencing financial distress. CDS spreads also react more strongly to permanent and less anticipated dividend cuts. The size of CDS reaction is more pronounced for financial firms, which are inherently more opaque. In contrast, CDS spreads react weakly to dividend raises and share repurchases. The results show that the information effect of dividend changes dominates the wealth transfer effect.

We investigate the effect of information uncertainty on the macroeconomic announcement premium of the market return, as well as the effect of fundamentals risk. We show that the premium is significant only during low information uncertainty periods, opposite to the case of fundamentals risk. Both effects are also manifest in the reduction of fundamentals risk on announcement days. These effects extend to market factor premiums on various sets of portfolios and have a connection with business cycles. The findings are consistent with the notion that information uncertainty hampers the effectiveness of learning.

This paper investigates the interaction between housing prices and housing rentals. Standard economic models treat housing prices as the present discounted value of future rentals with the latter treated as exogenous. Casual observation, however, suggests that changes in rental prices often follow housing price changes. Economic theory also supports the view that rental prices may not be exogenous. Extending the user-cost model of house price determination, we propose that expected returns on alternative investments contribute positively to the rental adjustment process. We estimate an empirical model for Hong Kong house prices and show that a 1% change in the gap between rental yields and equilibrium whole economy capital returns, the return gap, implies a 0.30% change in real rents. One policy implication is that price changes in the housing market can impact the rental adjustment process by changing the return gap. Consequently, variation in the price-to-rent ratio, which is often used to measure the divergence of housing prices from their equilibrium level can underestimate the size of any housing market 'bubble'.

The decline of average stock return volatility in the 2001-2006 period provides a good opportunity to test various theories on why the average return volatility increased in the pre-2000 period. This paper compares fundamentals-based theories with trading-based theories. While both fundamentals-based theories and trading-based theories explain the upward trend in the average volatility in US stocks from 1976 to 2000, only the fundamentals-based theories explain the volatility pattern for 2001-2006. Much of the variation in the stock return volatilities can be explained by the variation in the volatilities in earnings and, to a lesser degree, by proxies for growth options, but not by trading-related variables.

This paper tests the asymptotic arbitrage pricing theory (APT) with the factors extracted from individual stocks. It is shown that the asymptotic APT fails if and only if the number of unbounded eigenvalues of the second-moment matrix of excess returns exceeds that of the variance matrix of excess returns by one. A test is developed using this result on the eigenvectors extracted by the Connor-Korajczyk (CK) method. The test statistic is shown to be related to the maximum Sharpe ratio among portfolios of all individual stocks. The empirical evidence, supplemented by simulation results, lends support to the implication of the asymptotic arbitrage pricing theory that the unconditional pricing error is small.

We investigate why individual stocks become more volatile over the 1976–2000 period, during which quarterly accounting data are available at the firm level. On average, corporate earnings have deteriorated and their volatilities have increased over the sample period. This is more evident for newly listed stocks than for existing stocks. The stock return volatility is negatively related to the return‐on‐equity and positively related to the volatility of the return‐on‐equity in cross‐sections. The upward trend in average stock return volatility is fully accounted for by the downward trend in the return‐on‐equity and the upward trend in the volatility of the return‐on‐equity.