Currency Networks, Bilateral Exchange Rate Volatility and the Role of the US Dollar

We investigate monthly bilateral exchange rate volatility for a large sample of currency pairs over the period 1999-2006. Pegs (particularly to the US dollar) and managed floats tend to have lower volatility than independent floats. A deeper investigation shows that the peg effect operates almost entirely through currency networks (i.e. where two currencies are pegged to the same anchor currency), and the lower volatility of US dollar pegs reflects the size of the US dollar network. Managed floats show clear evidence of tracking the US dollar, further increasing the effective size of the US dollar network. Inflation undermines the currency-stabilizing effect of peg networks. Currencies in smaller peg networks have higher unweighted but not trade-weighted exchange rate volatility, which is consistent with anchors being chosen to minimize trade-weighted volatility. The size of the effective US dollar network revealed here is a plausible explanation of the rarity of basket pegs. Volatility also reflects a range of structural factors such as country size, level of development, population density, inflation differentials and business cycle asymmetry.exchange rate volatility; currency peg; inflation

Measuring exchange rate flexibility by regression methods

A new and easily implemented regression method is proposed for generating an index of exchange rate flexibility, whilst simultaneously identifying anchors of pegged currencies. The method can distinguish floats from pegs, including those with occasional devaluations. An annual index is calculated that can be compared with other regime classification schemes, or used directly in empirical research as a measure of exchange rate flexibility. Different categories in the IMF’s de facto classification, and also in the Reinhart-Rogoff classification, are associated with significantly different average values of the index. Further analysis of managed floats shows that they have a strong tendency to track the US dollar

Net foreign assets and real exchange rates revisited

Theory suggests a significant positive relationship in long-run equilibrium between the net foreign assets (NFA) of a country and its real exchange rate. Empirical tests have ignored two issues: the large variation in cross-country trade/GDP ratios, which is likely to induce substantial cross-country differences in coefficients when net foreign assets are scaled by GDP, and the reverse causality associated with valuation effects. A real exchange rate appreciation reduces the absolute value of NFA denominated in foreign currency relative to domestic GDP, because of the sizeable component of non-tradable goods in domestic GDP. This endogeneity biases the test results. New tests are implemented that address these issues. The valuation effect bias is found to be significant. The new tests nevertheless still support the existence of a long-run positive relationship between NFA and real exchange rates

Exchange Rate Flexibility: How Should We Measure It?

This paper first examines some recent exchange rate classification schemes. There is little evidence of a trend towards greater agreement between schemes. There is a probability of between 16 and 28 percent that a peg in one classification scheme is coded as a float in a different scheme, or vice versa. This probability is much smaller for the tightest forms of peg and the most volatile floats. Continuous indices of exchange rate flexibility are analysed and shown to have significant potential, despite the lack of interest in them shown in previous research

Global Trends in the Choice of Exchange Rate Regime

The raw data suggest that the global trend towards greater exchange rate flexibility that was evident before 1990 has since stopped. An optimum currency area (OCA) model of exchange rate regime choice is estimated. Four different schemes for classifying exchange rate regime are investigated. Trends in the explanatory variables made little difference to the trend towards greater flexibility before 1990 but have worked against it since, largely because of the reduction in inflation. Underlying preferences are still shifting gradually in the direction of greater flexibility

Class-Incremental Grouping Network for Continual Audio-Visual Learning

Continual learning is a challenging problem in which models need to be trained on non-stationary data across sequential tasks for class-incremental learning. While previous methods have focused on using either regularization or rehearsal-based frameworks to alleviate catastrophic forgetting in image classification, they are limited to a single modality and cannot learn compact class-aware cross-modal representations for continual audio-visual learning. To address this gap, we propose a novel class-incremental grouping network (CIGN) that can learn category-wise semantic features to achieve continual audio-visual learning. Our CIGN leverages learnable audio-visual class tokens and audio-visual grouping to continually aggregate class-aware features. Additionally, it utilizes class tokens distillation and continual grouping to prevent forgetting parameters learned from previous tasks, thereby improving the model's ability to capture discriminative audio-visual categories. We conduct extensive experiments on VGGSound-Instruments, VGGSound-100, and VGG-Sound Sources benchmarks. Our experimental results demonstrate that the CIGN achieves state-of-the-art audio-visual class-incremental learning performance. Code is available at https://github.com/stoneMo/CIGN.Comment: ICCV 2023. arXiv admin note: text overlap with arXiv:2303.1705