Davide La Cara

This paper establishes a novel link between exchange rates and equity markets by introducing a new equity signal that captures the direction of international capital flows: the winners’ spread, defined as the cross-country differential in the performance of value-weighted winner stocks. I find a strong positive correlation between this signal and FX returns, suggesting that investors time international allocations based on relative winners’ performance, influencing currency appreciations. This relationship is indeed supported by cross-border equity flows, which move in line with the winners’ spread. Finally, trading strategies based on this signal generate significant excess returns (up to 4% annually and robust to transaction costs) unexplained by traditional risk factors, ruling out risk-based explanations and boosting a demand pressure hypothesis. These findings provide new insights into the equity–FX nexus beyond market-wide differentials.

The increasing concern surrounding geopolitical risk underscores its growing impact on contemporary markets. Leveraging the novel GPR index developed by Caldara and Iacoviello (2022), this paper constructs a new tradable factor based on historical betas, demonstrating its role as a priced state variable with a sizable monthly premium of 0.69% in the US equity market. Notably, this factor is among the few of its kind based on information orthogonal to economic indicators and explicitly linked to geopolitics. This cross-sectional factor analysis supports the main time-series findings, which are examined using an event-driven IV and a novel application of wavelet decomposition. While the former addresses inference concerns, the latter offers a dynamic lens into the frequency-domain behavior of geopolitical risk. It reveals that its effect on equity indices, such as NASDAQ, is negative and transitory. A behavioral interpretation grounded in recency, salience, and availability biases is also provided.

With reference to S&P 500 daily returns, we report evidence of an in-sample predictive accuracy breakdown for realized variance by GARCH models in correspondence to the March 2020 Covid-19 outbreak. However, a variety of macroeconomic risk, political and social media sentiment uncertainty factors, and crucially a few variables capturing the evolution of the Covid-19 pandemics, successfully predict the direction and size of GARCH forecast errors between November 2019 and June 2020. Predictors related to diagnosed cases, their rate of growth, and the progression of the curve of deceased, infected people in the United States are featured prominently. We test a number of “augmented” GARCH models to include the most precisely estimated exogenous variables and find that they offer precise forecasts in samples that include the Covid-19 outbreak. In genuine out-of-sample tests, augmenting GARCH with Covid-19 related exogenous variables increases the percentage of days in which the direction of change in realized variance is correctly predicted.