{"id":42105,"date":"2025-09-26T15:03:33","date_gmt":"2025-09-26T13:03:33","guid":{"rendered":"https:\/\/quantpedia.com\/?p=42105"},"modified":"2025-10-28T22:52:45","modified_gmt":"2025-10-28T21:52:45","slug":"cross-sectional-and-dollar-components-of-currency-risk-premia","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/cross-sectional-and-dollar-components-of-currency-risk-premia\/","title":{"rendered":"Cross-Sectional and Dollar Components of Currency Risk Premia"},"content":{"rendered":"

Currency strategies often appear simple on the surface \u2013 go long high-yielding currencies, short low-yielding ones<\/a>, or take a position on the U.S. dollar. But these trades actually mix two distinct components: a Dollar component, which bets on broad movements of the U.S. dollar against all others, and a Cross-Sectional (CS) component, which exploits relative differences<\/em> across countries. The question is, which of these components really drives currency risk premia? A new paper by Vahid Rostamkhani tackles this long-standing question by decomposing the predictive power of eleven macroeconomic fundamentals\u2014such as interest rates, inflation, unemployment, and fiscal variables\u2014into these two components across almost a century of data (1926-2023). This approach directly tests whether it is more rewarding to time the dollar itself or to focus on cross-country fundamental spreads.<\/strong><\/p>\n\n\n\n

For practitioners running currency carry, value, or macro-fundamental strategies, this distinction is critical. A strategy dominated by the Dollar component is effectively a bet on the global financial cycle and the dollar\u2019s safe-haven status\u2014exposed to regime shifts in U.S. monetary policy and risk-off episodes. In contrast, CS-driven strategies isolate relative country risk premia and may offer better diversification. Rostamkhani\u2019s results show that cross-sectional predictability is consistently stronger<\/strong>, delivering higher and more robust risk-adjusted returns (Sharpe ratios) than strategies that attempt to time the broad dollar.<\/p>\n\n\n\n

To handle the \u201cfactor zoo\u201d of 22 Dollar and CS signals, the paper applies a Bayesian Model-Averaged Stochastic Discount Factor (BMA-SDF)<\/strong> framework. The analysis finds that currency pricing is dense, not sparse<\/strong>: no single macro factor dominates, but many provide noisy pieces of valuable information about underlying risks. By optimally aggregating them, the BMA-SDF achieves much better out-of-sample pricing power than traditional two-factor models. For portfolio managers, this suggests that instead of seeking a single perfect macro predictor, combining a broad set of relative-fundamental signals\u2014and emphasizing the cross-sectional side\u2014may capture more of the available currency risk premium.<\/p>\n\n\n\n

Key Findings<\/strong><\/h3>\n