Abstract This study estimates a Phillips curve with time-varying parameters and stochastic volatility to examine the evolution of inflation persistence across different monetary regimes over the period 1870–2020. Using long-run macroeconomic data for the United Kingdom and the United States, we document substantial variation in inflation persistence over time in both countries. By combining these time-varying estimates with a novel, data-driven measure of regime credibility, we provide empirical evidence that credibility plays a key role in shaping inflation dynamics. In particular, periods characterized by higher monetary policy credibility are associated with significantly lower inflation persistence. Lending support to the credibility hypothesis, these results highlight the importance of the monetary regime in driving inflation outcomes. Complementing our quantitative analysis with historical narrative evidence, we conclude that credible conduct of monetary policy is essential for achieving and maintaining long-term price stability.
Abstract This paper contributes to the debate on geography as a driver of information diffusion and price discovery by examining the tail-dependence structure of nine African stock markets linked by free trade areas and economic unions. Using asymptotic-dependence measures derived from multivariate extreme value theory, we show that African equity markets exhibit stronger dependence during negative return exceedances than during positive return exceedances, indicating greater connectedness in crash states. The dependence structure is shaped by market-specific roles. Mauritius emerges as the dominant regional centre of negative tail dependence, displaying elevated crash dependence with all other markets, while South Africa, despite being the dominant trading partner and the most globally integrated market in the region, follows a distinct crash-risk profile. Cross-sectional regressions show that, conditional on these roles, bilateral trade contributes to negative tail-dependence propagation, while spatial distance indicates that crash dependence is organized through a wider geographic structure. Positive return exceedances display a more diffuse structure. Overall, the evidence supports a state-dependent, crash-specific information-diffusion mechanism in African equity markets.
Abstract This study provides a comprehensive assessment of the asymmetric and nonlinear impacts that major sources of economic and financial uncertainty exert on exchange rates in 23 emerging and frontier market currencies. Through a flexible modeling approach, we uncover critical threshold effects and complex transmission mechanisms underlying the uncertainty-exchange rate nexus. Our results indicate that traditional linear models substantially understate these dynamics. We make three key contributions. First, we model nonlinearities across three distinct data transformations (level, logarithmic, and return series of exchange rates), offering a nuanced understanding of exchange rate behavior at different scales – capturing long-term trends, short-term fluctuations, and volatility dynamics. Second, we introduce a novel data-driven calibration of B-spline generalized additive models (GAMs), which optimizes the number and placement of knots based on information criteria and cross-validation. This approach yields a parsimonious yet highly adaptive fit that quantifies the extent of nonlinearity with unprecedented granularity. Third, using an extensive daily dataset from 2011 to 2023 that spans multiple global crises, we deliver generalizable insights into the pervasive asymmetries and nonlinearities governing emerging-market forex movements. The evidence highlights that uncertainty stemming from equity markets, commodities, and interest rates has markedly nonlinear effects on currencies – effects that intensify beyond certain uncertainty thresholds. Our findings guide policymakers in fortifying resilience against disruptive capital flows and exchange rate pressures, and they advance the literature on uncertainty and exchange rates by revealing hidden nonlinear patterns that linear models would overlook.
Abstract This paper proposes a novel Bayesian decision-theoretic framework for ranking and selecting simple macroeconomic policy rules in linear rational expectations models under structural parameter uncertainty. Unlike standard approaches that rely on point estimates, expected welfare losses, or worst-case scenarios, our framework utilizes stochastic dominance (SD) orderings of finite or infinite degree to account for the entire posterior distribution of welfare losses. This approach allows a policymaker – without a predetermined attitude toward random loss – to identify efficient sets of policy rules that are robust to all monotonic transformations of the underlying loss distributions. We introduce the Optimal Policy Feedback Coefficient and Minimized Welfare Loss functions to provide a feasible numerical algorithm for identifying SD-optimal rules. We apply this framework to the debate on price-level targeting versus inflation targeting within a richly specified DSGE model of the U.S. economy featuring financial frictions on both sides of the bank’s balance sheet. Our empirical results demonstrate that while the Taylor rule may achieve lower welfare losses than price-level rules, the result is highly dependent on the specific source of parameter uncertainty. Furthermore, our framework yields distinct policy recommendations compared to the standard Bayesian robust approach, suggesting more aggressive policy responses to supply, demand, and financial shocks.
Abstract Noncausal time series models have attracted attention in financial econometrics because they can reproduce bubbles in cryptocurrency and commodity markets. We investigate the pricing and hedging of European options under the noncausal Cauchy autoregressive model of order one (NCAR(1)), whose calendar-time conditional dynamics satisfy the martingale property. We derive closed-form expressions for the conditional cumulative distribution function and the expected payoff of a European call option. When the current asset price is large, the conditional dynamics admit a limiting binomial approximation, with upward and downward movements corresponding to an expansion and a collapse of the bubble, respectively. This result suggests a natural binomial hedging strategy, which we compare with a quadratic hedging strategy and delta hedging. Numerical experiments illustrate the accuracy of the binomial approximation and the relative performance of the three hedging methods.
