This paper explains exchange rate dynamics by linking financial customers’ foreign exchange order flow with their dynamic portfolio reallocation. For any currency pair in a particular period, one currency has higher assets return than the other and can be considered the high-return-currency (HRC). Financial institutions attempt to hold more HRC assets when they become more risk-loving or the relative return of the assets is expected to increase. Such a portfolio reallocation generates buy order toward the HRC and the currency appreciates. As the HRC changes over time, the direction that the relative return and risk appetite affect the exchange rate varies in different regimes.

We study the production effects of one of the largest bank branch expansion programs in history, implemented by the government of India during the 1980s. Combining policy-driven variation with newly-digitized data on bank lending and crop prices at the district-year level, we do not find evidence for a significant shift in agricultural output and inputs on average. Greater bank presence does promote resilience to climate risk, however, by attenuating the effect of lagged rainfall shocks on output. This effect operates via changes in the incidence of cropping during the dry winter season, which makes use of costly irrigation resources.

We find a strong and robust positive relationship between individual downside variance premia (DVP)–the difference between risk-neutral and physical expected downside variances–and future corporate bond returns. The spread portfolio that longs the high DVP bond portfolio and shorts the low DVP bond portfolio earns a statistically significant excess return of 0.37% (0.42%) per month in value- (equal-)weighted returns. The alpha estimates from various factor models remain statistically significant and economically substantial. The predictive power of the downside variance premium is stronger in noninvestment-grade (long-maturity) corporate bonds than in investment-grade (short-maturity) bonds. We show that the downside variance premium positively relates to the likelihood of future default and cash flow uncertainty and negatively relates to future cash flows.

This paper develops a new methodology for constructing a real estate price index that utilizes all transaction price information, encompassing both single-sales and repeat-sales. The method is less susceptible to specification error than standard hedonic methods and is not subject to the sample selection bias involved in indexes that rely only on repeat sales. The methodology employs a model design that uses a sale pairing process based on the individual building level, rather than the individual house level as is used in the repeat-sales method. The approach extends ideas from repeat-sales methodology in a way that accommodates much wider datasets. In an empirical analysis of the methodology, we fit the model to the private residential property market in Singapore between Q1 1995 and Q2 2014, covering several periods of major price fluctuation and changes in government macroprudential policy. The index is found to perform much better in out-of-sample prediction exercises than either the S&P/Case-Shiller index or the index based on standard hedonic methods. In a further empirical application, the recursive dating method of Phillips et al. (2015a,b) is used to detect explosive behavior in the Singapore real estate market. Explosive behavior in the new index is found to arise two quarters earlier than in the other indices.

Unlike traditional asset categories (e.g., industry classifications) that are generally defined clearly, some groups of stocks are tied to certain loosely defined “concepts” (e.g., e-commerce). When investors find it difficult to analyze ambiguous concept-oriented information, information diffuses slowly, creating “concept momentum”. Based on unique concept data in the Chinese stock market, this study constructs a concept-momentum strategy that involves buying stocks from past winning concepts and selling stocks from past losing concepts, which can generate pronounced abnormal returns. Neither risk factors, firm-level momentum, nor industry-level momentum can explain concept momentum. Furthermore, we find that both the underreaction and cross-stock lead-lag effect channels can cause slow information diffusion and drive concept momentum. Moreover, the concept momentum effect is stronger for relatively ambiguous concepts, for concepts that attract less investor attention, and following high-sentiment periods.