G10 Currencies Research Articles

Limits to arbitrage and the term structure of CIP violations

We investigate the existence of a term structure in cross-currency swap bases, a measure for CIP violations, to identify limits to arbitrage in foreign exchange swap markets. Based on estimates from a multivariate model of USD cross-currency bases for G10 currencies that caters for a number of known intermediary constraints as well as linkages between currency pairs our findings highlight the importance of two-tiered arbitrage, risk aversion, regulation, and policy in explaining this term structure of CIP violations.

Extreme downside risk connectedness and portfolio hedging among the G10 currencies

This study investigates the frequency connectedness among foreign exchange markets of G10 countries, focusing on tail risk and its implications for portfolio management. To do so, we use a novel framework that combines the Conditional Autoregressive Value-at-Risk (CAViaR) model with the novel time-varying frequency and quantile connectedness method developed by Chatziantoniou et al. (2022) based on Baruník and Křehlík (2018) and Ando et al. (2018) approach. A key value of this paper to the literature is the provision of fresh empirical evidence on the extreme downside linkages among the markets examined. From the average connectedness measures, the top shock transmitters within the network were EUR, NOK, AUD, SEK, and NZD, while the main shock receivers emerged to be JPY and CHF, followed by CAD and GBP. We note that events in the major funding markets (the Eurozone, Japan) have a higher impact on the participants in these same markets than in relatively small markets (New Zealand, Norway). From the dynamic connectedness results, the magnitude of connectedness for the entire sample period increased during the COVID-19 era, compared to the magnitude before the COVID-19 outbreak. The cumulative spillover also reveals that USDNOK is the vastest net transmitter of spillovers to other markets, including SEK, CHF, and AUD. However, the EUR is the largest net beneficiary followed by JPY and CAD. Findings from the time-varying extreme downside analysis suggest that throughout the period, SEK and NOK are the other currencies' strongest and most frequent net spillover shock emitters for the short-, medium-, and long-term dynamics. Currency portfolio implications are discussed.

Does exchange rate misalignments lead changes in macroeconomic fundamentals?

This article aimed at verifying the reliability of equilibrium exchange rate (EER) concepts by exploring if exchange rate misalignments are leading to changes in macroeconomic fundamentals. The authors focused on G10 currencies and the three most popular EER models which the IMF regularly uses within the External Balance Assessment: Purchasing Power Parity, Behavioural EER and Fundamental EER. In methodological terms, the study applied the cross-sectional predictability approach developed by Sarno and Schmeling (2014), which ranks countries on the basis of the scale of EER misalignment to check if this ranking helps to predict key macro variables. The results indicate that EER misalignment contains cross-sectional predictive power and allows forecasting real effective exchange rates, short-term interest rates and current account balance differences across countries.

Housing Cycles and Exchange Rates

This paper documents that the ratio of residential-to-nonresidential investment is a strong in-sample and out-of-sample predictor for the dollar up to 12 quarters. The predictability is robust to a battery of additional checks and holds for other G10 currencies. We explain the predictability in an analytical model with time-varying housing preference, productivity, and volatility. In the model, the U.S. housing investment share is higher during periods with higher growth and lower uncertainty, corresponding to lower future nontradable prices, dollar index, and excess returns. We find strong empirical support for the channel. Alternative explanations, including the business and financial cycle, find less empirical support. This paper was accepted by David Sraer, finance. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4932 .

Beyond the dollar: A global perspective on exchange rate dynamics via currency factors

This paper investigates the dynamics of currency exchange rates from a global perspective, by expanding on the concept of currency factors. Through the analysis of G10 currencies, we disclose the interconnections and patterns driving exchange rates, shedding light on the central role of the euro and Swiss franc in shock propagation. Furthermore, we observe that pound and yen factors have acted as net absorbers of spillovers from other currencies. Our findings suggest that currency factors provide valuable insights for international diversification, risk sharing strategies, and real-time indicators of foreign exchange market uncertainty.

Exchange rate spillover, carry trades, and the COVID-19 pandemic

Although it is widely accepted that exchange rates are connected, what drives these connections remains an unsettled question. We examine the interconnections and spillovers of G10 currencies over the period from January 1, 2018 to June 17, 2021. We find that the Euro and Australian dollar serve as risk transmitters whereas the Japanese yen operates as a risk recipient. During the COVID-19 pandemic period, countries with higher infection cases experience currency depreciation and transmit more currency risk to others. In response to this crisis, the Fed adopted the large-scale asset purchase program that weakened the USD and increased the demand for high-yield currencies through the portfolio rebalancing channel. The appreciation of high-yield currencies further attracts carry trades and enhances their risk transmission to low-yield currencies. Furthermore, we provide evidence to show that the COVID-19 infection cases, the Fed's policy, and carry trades are crucial determinants of exchange rate spillovers.

Climate Change Vulnerability and Currency Returns

Using measures of physical risk from climate change, we develop a methodology to allocate currency pairs according to a country’s vulnerability and construct portfolios with decreasing vulnerability to physical risk. We show that non-G10 currencies are more vulnerable to physical risk, have become less vulnerable over time, and that the vulnerability measure is correlated with higher losses from natural disasters. Portfolios exposed to currencies with decreasing vulnerability have exhibited positive abnormal returns, with the abnormal return coming from currencies that have relatively high levels of vulnerability. These results exist in non-G10 currencies, while no relation is found within G10 currencies.

Maximally predictable currency portfolios

We investigate the predictability of the G10 currencies with respect to lagged currency returns from the perspective of a U.S. investor, using the maximally predictable portfolio (MPP) approach of Lo and MacKinlay (1997). We show that, out-of-sample, the MPP yields a higher Sharpe ratio, higher cumulative return and lower maximum drawdown than both a naïve equal-weighted portfolio of the currencies and an equal-weighted portfolio of momentum trading strategies, and that a mean–variance investor would be willing to pay a performance fee to switch from the naïve and momentum portfolios to the MPP. The MPP has performed particularly well since the 2008 financial crisis, in contrast with the momentum portfolio, the value of which declined significantly over this period. Our results are robust to the estimation window length, the type and level of portfolio weight constraints and transaction costs.

Connectedness between G10 currencies: Searching for the causal structure

AbstractThis paper presents a new approach for modelling the connectedness between asset returns. We adapt the measure of Diebold and Yilmaz, which is based on the forecast error variance decomposition of a VAR model. However, their connectedness measure hinges on critical assumptions with regard to the variance–covariance matrix of the error terms. We propose to use a more agnostic empirical approach, based on a machine learning algorithm, to identify the contemporaneous structure. In a Monte Carlo study, we compare the different connectedness measures and discuss their advantages and disadvantages. In an empirical application we analyse the connectedness between the G10 currencies. Our results suggest that the US dollar as well as the Norwegian krone are the most independent currencies in our sample. By contrast, the Swiss franc and New Zealand dollar have a negligible impact on other currencies. Moreover, a cluster analysis suggests that the currencies can be divided into three groups, which we classify as: commodity currencies, European currencies and safe haven/carry trade financing currencies.

Hedging local currency risk with precious metals

Precious metals are popular instruments for hedging local currency risk. Most precious metals are priced in US dollars, which is a single-currency numéraire. The numéraire of a precious metal can easily be changed, which allows investors to choose their own local currency as the precious metal’s numéraire and subsequently use it to hedge their own local currency risk. In this paper, we decompose the standard hedge ratio into two parts, namely, a precious-metal hedge ratio and a local-currency hedge ratio. We consider three main precious metals, namely, gold, silver and platinum, from the beginning of 1990 to the end of 2019 to hedge local currency risk for individual G10 currencies. Over the full sample, we find that all standard hedge ratios are negative for all combinations of currencies and precious metals. However, the negativity is driven by the precious metal’s numéraire, rather than the precious metal.

Exchange Rate Sensitivity and the Net Foreign Asset Composition

AbstractMany currencies, especially from countries with negative net foreign assets, depreciate during financial turbulence. Using a panel of 26 currencies for the period April 2002 to December 2019, I show that the net foreign asset composition is related to the exchange rate sensitivity to global financial market uncertainty changes. Net foreign debt is associated with a higher sensitivity, whereas net equity and FDI are not. Ownership matters too, as this association is stronger for private net liabilities. In emerging markets, this vulnerability arises from net other investments, while G10 currencies are more sensitive the more private net portfolio debt the countries have.

Currency Factors

We examine the ability of existing and new factor models to explain the comovements of G10 currency changes, measured using “currency baskets.” A clustering technique reveals a clear two-block structure in currency comovements, with the first block containing mostly the dollar currencies and the other the European currencies. A factor model incorporating this “clustering” factor and two additional factors, a commodity currency factor and a “world” factor based on trading volumes, fits currency basket correlations much better than extant factors, such as value and carry, do. In particular, it explains on average about 60% of currency variation and generates a root mean squared error relative to sample correlations of only 0.11. The model also fits comovements in emerging market currencies well. Economically, the correlations between currency baskets underlying the factor structure are inversely related to the physical distances between countries. This paper was accepted by Kay Giesecke, finance.

Climate Change Vulnerability and Currency Returns

Using measures of physical risk from climate change, we develop a methodology to allocate currency pairs according to a country’s vulnerability to climate change and construct portfolios with decreasing vulnerability to physical risk. We show that non-G10 currencies are more vulnerable to physical risk, have become less vulnerable over time, and that the vulnerability measure is correlated with higher losses from natural disasters. Portfolios exposed to currencies with decreasing vulnerability have exhibited positive abnormal returns, with the abnormal return coming from currencies that have relatively high levels of vulnerability. These results exist in non-G10 currencies, while no relation to returns exist within G10 currencies.

Currency Risk and Capital Accumulation

The Lucas Paradox states that there are large and persistent differences in capital-output ratios across countries, suggesting capital is not flowing to countries where it is relatively scarce. In the data, capital-output ratios vary a lot cross-sectionally even within developed countries, and they are negatively correlated with currency risk premia and risk-free rates. To rationalize these patterns, I build a quantitative multi-country model of capital accumulation with external habit and heterogeneous exposures to a global productivity shock. I show that currency risk in this model generates cross-country variations in risk-free rates and capital-output ratios that are consistent with the data. I estimate the model using GDP data from countries issuing the G10 currencies and find two main results: (1) The heterogenous loadings that I extract from GDP data alone are highly correlated with capital-output ratios; and (2) when I feed the estimated loadings to the model, model-generated capital-output ratios account for roughly 55% of the cross-country variation in the data. I conclude that variation in currency risk and therefore currency risk premia have significant effects on the real economy.

Maximally Predictable Currency Portfolios

We investigate the predictability of the G10 currencies with respect to lagged currency returns from the perspective of a U.S. investor, using the maximally predictable portfolio (MPP) approach of Lo and MacKinlay (1997). Out-of-sample, the MPP yields a higher Sharpe ratio, higher cumulative return and lower maximum drawdown than both an equal-weighted portfolio of the currencies and an equal-weighted portfolio of momentum trading strategies. The MPP has performed particularly well since the 2008 financial crisis, in contrast with the momentum portfolio, the value of which declined significantly over this period. Our results are robust to the estimation window length, the type and level of portfolio weight constraints, and transaction costs.

From CIP-deviations to a market for risk premia: A dynamic investigation of cross-currency basis swaps

The persistent deviations from the covered interest rate parity (CIP) since 2007 indicate that specific frictions continue to exist, which prevent them from being arbitraged away. In this study, we investigate the cross-currency basis swap market. We put forward the argument that the risk premium expressed via the CIP-deviation constitutes a unique market that determines its own equilibrium price after receiving feedback from various sources, including contagion within the market itself. We investigate contagion using a TVP-VAR framework of analysis that measures the extent of connectedness across the bases on all G10 currencies against the US dollar between 2007 and 2017. Our main findings indicate that connectedness is event-dependent. Furthermore, we provide evidence that net-transmitting bases are typically associated with banking sectors with significant overseas operations (e.g. EUR and JPY). Safe-haven currencies (e.g. CHF and JPY) tend to assume a net-receiving role during periods of stress. On a pairwise level, results confirm that during tranquil times in international financial markets, connectedness subdues and even reaches negligible levels – particularly for stable banking systems (e.g. CAD) or without significant US dollar funding gaps (e.g. AUD).

Short-Term Trading Strategy on G10 Currencies

This paper presents research on a profitable trading strategy for G10 currencies. We will devise trading strategies by considering realistic trading scenarios, analyze the performance of such strategies on out of sample data, identify the risks of these trading strategies, explain why the trading strategy works, and summarize and draw conclusions. We will use technical indicators like Moving average (MA), Exponentially-weighted moving average, Ichimoku, Relative strength index, stochastic oscillators, Williams %R, Commodity Channel Index, and Bollinger bands. We will also use fundamental indicators like interest rate differentials from one currency to another. We will build a machine learning based model as it would be better equipped to build dynamic trading rules to capture profitable trading opportunities. We used an ensemble of machine learning algorithms - logistic regression with four principal components, and a voter classifier with random forest, extremely randomized trees, logistic regression with 4 principal components from the features, and support vector machine with 4 principal components from the features, giving 27% and 27% annualized returns, and 1.47 and 1.60 Sharpe ratio respectively.

Time-Varying Risk Aversion and the Profitability of Carry Trades: Evidence from the Cross-Quantilogram

This paper examines the predictive power of time-varying risk aversion over payoffs to the carry trade strategy via the cross-quantilogram methodology. Our analysis yields significant evidence of directional predictability from risk aversion to daily carry trade returns tracked by the Deutsche Bank G10 Currency Future Harvest Total Return Index. The predictive power of risk aversion is found to be stronger during periods of moderate to high risk aversion and largely concentrated on extreme fluctuations in carry trade returns. While large crashes in carry trade returns are associated with significant rises in investors’ risk aversion, we also found that booms in carry trade returns can be predicted at high quantiles of risk aversion. The results highlight the predictive role of extreme investor sentiment in currency markets and regime specific patterns in carry trade returns that can be captured via quantile-based predictive models.

Arbitrage detection using max plus product iteration on foreign exchange rate graphs

We propose a novel graph-theoretic method to detect k-currency arbitrage in spot foreign exchange (FX) markets and discuss and compare the runtime performance of this method against the permutation search approach. This technique is applied to a minute level bid/ask quote dataset consisting of rates constructed from all G10 currency pairs. We validate this approach through an example while also demonstrating its runtime efficiency, especially in the case of spot markets consisting of a large number of currency pairs. Finally, several potential extensions including trading applications are discussed.

Does the U.S. economic policy uncertainty connect financial markets? Evidence from oil and commodity currencies

We provide novel insight to the emerging literature on the role of U.S. monetary policy as a driver of a global financial cycle by examining the possible causal effect of U.S. economic policy uncertainty on the connectedness of crude oil and currency markets, using a sample of commodity currencies from advanced and emerging nations. A battery of linear and nonlinear Granger-based causality tests indicate the presence of a causal relationship between economic policy uncertainty and the connectedness of oil and currency markets, particularly at low frequencies and more significantly after the outburst of the global financial crisis. While crude oil generally serves as a net transmitter of shocks to currencies across all frequency bands, the spillover effects from oil are largely concentrated towards the G10 currencies of Australian and New Zealand dollar that are often used as investment currencies in global carry trade strategies. Overall, our findings suggest the presence of a significant pass-through effect of economic policy uncertainty via oil prices, spilling over to the currency market, in line with the emerging evidence that the monetary policy by the U.S. Fed serves as a major driver of a global financial cycle that describes patterns in global capital flows, credit activity and asset prices across financial markets.