Hedging downside risk before substantial price corrections is vital for risk management and long-only active equity manager performance. This study proposes a novel methodology for crafting timing signals to hedge sectors’ downside risk. These signals can be integrated into existing strategies simply by purchasing sector index put options. Our methodology generates successful signals for price corrections in 2000 (dot-com bubble) and 2008 (global financial crisis). A key innovation involves utilizing sector correlations. Major price swings within six months are signaled when a sector exhibits high valuation alongside abnormal correlations with others. Utilizing the price-to-earnings ratio for identifying sectors’ high valuations is more beneficial than the bond–stock earnings yield differential. Our signals are also more efficient than those of standard technical analyses.

This is a study of decision problems under two-dimensional risk. We use an existing index of absolute correlation aversion to conveniently classify bivariate preferences, with respect to attitudes toward this risk. This classification seems to be more important than whether decision makers are correlation-averse or correlation-seeking for the study of insurance demand when a loss has a multidimensional impact. On this note, we also re-examine Mossin’s theorem under bivariate preferences, where full insurance is preferred with a fair premium, while less than full coverage is preferred with a proportional premium loading. Furthermore, based on the comparative statics of this two-dimensional insurance model for changes in correlation aversion, we derive testable implications about the classification of bivariate utility functions. For the particular case when the two-dimensional risk can be interpreted as risk on income and health, we identify the form of separable utility functions depending on health status and income that is consistent with household disability insurance decisions.

Immigrants from Latin America’s Indigenous and rural communities in New York City are likely to break the cycle of poverty by improving language proficiency, acculturation, and education. Their well-being has received poor attention in the economic literature, and little is known about the needs, financial welfare, health status, or education among Indigenous-origin migrants from Latin American households. This study used primary data from a non-probabilistic sample of 121 self-identified Indigenous migrants living in New York City (NYC), a demographic cohort presenting challenges in terms of research access. National-level data usually aggregates all Spanish-speaking individuals as Hispanics and fails to acknowledge the presence of these pre-Hispanic groups. Integrating low-skilled Latin American Indigenous migrants into labor markets remains a challenge. We examined the link between the household income of Indigenous migrants from Latin America in NYC and education, acculturation, and discrimination. Using a logistic regression, we substantiated that education retains its prominence as the primary determinant of income for Indigenous migrants, but perceptions of discrimination based on skin color undermined this progress. This study highlights the need for interventions to promote language proficiency, acculturation, and education among Indigenous immigrant communities and implement culturally tailored policies to encourage the upward mobility of this population.

The study empirically examined the tradeoffs between the volatility of the exchange rate target zones and the volatility of nominal interest rate differentials. While the adoption of exchange rate regimes has declined over the past decades, their utilization still endures. A notable example is the European exchange rate mechanism, which now serves as an intermediary stage for new member states on their path to Euro adoption. The revived literature on this topic underscores the need for empirical support for volatility tradeoffs. The longstanding exchange rate mechanism offers a good opportunity for empirically testing these tradeoffs. The results using pooled regression on data drawn primarily from Bloomberg Terminal, covering 1984 to 2022 and including from various countries that participated in the exchange rate mechanism over the course of recent decades, confirm the results of some theoretical studies. Specifically, a policy seeking to narrow the exchange rate fluctuation margins leads to a decrease in the volatility of both the exchange rate and the interest rate differential.

This study identifies several factors that affect the demand for disability insurance (DI), such as wealth, health, financial literacy, and self-reported willingness to take financial risks, using a logit model and a random forest algorithm. While the role of risk aversion in insurance ownership is well established within theoretical insurance models, the empirical literature is still inconclusive. We provide evidence based on the Survey of Consumer Finances that willingness to take financial risks similar to wealth, health, and financial literacy is positively associated with the ownership of private DI. Furthermore, the literature has not explored multivariate risk attitudes in this framework. We thus employ indices of multivariate risk attitudes within a theoretical DI model. Our empirical results on private DI, in conjunction with the theoretical model we present, allow us to test hypotheses regarding multivariate household preferences and reject the hypothesis of correlation-neutral preferences on wealth and health.

For a long-only active equity manager (or investor), the ability to hedge the downside risk before a significant price correction is a valuable skill. The authors proposed constructing a signal that provides timing information for a manager to hedge the downside risk. This systematic signal can help a manager put on a hedge before the price corrections of the dot com bubble in 2000 and the global financial crisis in 2008. The signal is constructed for each GICS sector index in the S&P 500. When a sector’s PE indicates a high valuation and, at the same time, if the high valuation sector’s correlations with some other sectors are out of their historical norm, then these two conditions contribute a timing signal for the significant price swing of the index during the following six months. After observing the timing signal, managers can benefit significantly by hedging the downside risk. The signal can also be interpreted as the beginning of a high volatility regime for a sector.