There are many ways one can lose money when investing, and exchange rates are one of the potential risk factors. Luckily, there are several ways to minimize this type of loss in your portfolio. Systematic FX hedging that uses currency factor strategies is a way of protecting an existing or anticipated position from an unwanted move in an exchange rate. It does not eliminate the risk of loss completely but helps to manage currency exposure better.

Some see Bitcoin (BTC) as a payment method of the future; others see it as a speculative asset class. Despite the speculative activity connected with Bitcoin, after all, it is a currency that is different from fiat currencies like the US Dollar or Euro. If you hold fiat currency, there is an opportunity to earn a risk-free rate. But is there the same opportunity also in Bitcoin? And what are the Bitcoin’s risk-free and market rates? These are the questions we had in Quantpedia, and we invite you to join us in our thought experiment that tries to answer them …

Authors: Vojtko, Padysak

Title: What is the Bitcoin’s Risk-Free Interest Rate?

What is the impact of volatility (and changes in volatility) on popular Currency Momentum and Currency Carry strategies? That’s the topic of recent academic study written by Duc Hong Hoang, which decomposes foreign exchange volatility into two components, namely, secular (long-term) and transitory or mean-reverting (short-term) components. Long term component captures business cycle effects, while short term volatility usually represents funding tightness or shocks. Carry trade strategy is linked (and therefore partially predictable) to long-run volatility while momentum reacts mainly to short-run risks.

Author: Hoang

Title: Long Run and Short Run Risk Premium in Currency Market

A new research paper related to multiple currency strategies:

#5 – FX Carry Trade
#8 – Currency Momentum Factor
#9 – Currency Value Factor – PPP Strategy

Authors: Baku, Fortes, Herve, Lezmi, Malongo, Roncalli, Xu

Title: Factor Investing in Currency Markets: Does it Make Sense?

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3415700

Abstract:

The concept of factor investing emerged at the end of the 2000s and has completely changed the landscape of equity investing. Today, institutional investors structure their strategic asset allocation around five risk factors: size, value, low beta, momentum and quality. This approach has been extended to multi-asset portfolios and is known as the alternative risk premia model. This framework recognizes that the construction of diversified portfolios cannot only be reduced to the allocation policy between asset classes, such as stocks and bonds. Indeed, diversification is multifaceted and must also consider alternative risk factors. More recently, factor investing has gained popularity in the fixed income universe, even though the use of risk factors is an old topic for modeling the yield curve and pricing interest rate contingent claims. Factor investing is now implemented for managing portfolios of corporate bonds or emerging bonds.

In this paper, we focus on currency markets. The dynamics of foreign exchange rates are generally explained by several theoretical economic models that are commonly presented as competing approaches. In our opinion, they are more complementary and they can be the backbone of a Fama-French-Carhart risk factor model for currencies. In particular, we show that these risk factors
may explain a significant part of time-series and cross-section returns in foreign exchange markets. Therefore, this result helps us to better understand the management of forex portfolios. To illustrate this point, we provide some applications concerning basket hedging, overlay management and the construction of alpha strategies.

Notable quotations from the academic research paper:

"In this paper, we propose analyzing foreign exchange rates using three main risk factors: carry, value and momentum. The choice of these market risk factors is driven by the economic models of foreign exchange rates. For instance, the carry risk factor is based on the uncovered interest rate parity, the value risk factor is derived from equilibrium models of the real exchange rate, and the momentum risk factor benefits from the importance of technical analysis, trading behavior and overreaction/underreaction patterns. Moreover, analyzing an asset using these three dimensions helps to better characterize the financial patterns that impact an asset: its income, its price and its trend dynamics. Indeed, carry is associated with the yield of the asset, value measures the fair price or the fundamental risk and momentum summarizes the recent price movements.

By using carry, value and momentum risk factors, we are equipped to study the cross-section and time-series of currency returns. In the case of stocks and bonds, academics present their results at the portfolio level because of the large universe of these asset classes. Since the number of currencies is limited, we can show the results at the security level.

For each currency, we can then estimate the sensitivity with respect to each risk factor, the importance of common risk factors, when specific risk does matter, etc. We can also connect statistical figures with monetary policies and regimes, illustrating the high interconnectedness of market risk factors and economic risk factors. The primary goal of building an APT model for currencies is to have a framework for analyzing and comparing the behavior of currency returns. This is the main objective of this paper, and a more appropriate title would have been "Factor Analysis of Currency Returns". By choosing the title "Factor Investing in Currency Markets", we emphasize that our risk factor framework can also help to manage currency portfolios as security analysis always comes before investment decisions.

This paper is organized as follows. Section Two is dedicated to the economics of foreign exchange rates. We first introduce the concept of real exchange rate, which is central for understanding the different theories of exchange rate determination. Then, we focus on interest rate and purchasing power parities. Studying monetary models and identifying the statistical properties of currency returns also helps to define the market risk factors, which are presented in Section Three. These risk factors are built using the same approach in terms of portfolio composition and rebalancing. Section Four presents the cross-section and time-series analysis of each currency. We can then estimate a time-varying APT-based model in order to understand the dynamics of currency markets. The results of this dynamic model can be used to manage a currency portfolio. This is why Section Five considers hedging and
overlay management."

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What are the main insights?

– future alpha of a high interest rate currency carry portfolio increases in a trough in a business cycle and in a state of high market uncertainty

1/

Authors: Burnside, Cerrato, Zhang

Title: Foreign Exchange Order Flow as a Risk Factor

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3275356

Abstract:

This paper proposes a set of novel pricing factors for currency returns that are motivated by microstructure models. In so doing, we bring two strands of the exchange rate literature, namely market-microstructure and risk-based models, closer together. Our novel factors use order flow data to provide direct measures of buying and selling pressure related to carry trading and momentum strategies. We find that they appear to be good proxies for currency crash risk. Additionally, we show that the association between our order-flow factors and currency returns differs according to the customer segment of the foreign exchange market. In particular, it appears that financial customers are risk takers in the market, while non-financial customers serve as liquidity providers.

2/

Authors: Hsu, Taylor, Wang

Title: The Profitability of Carry Trades: Reality or Illusion?

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3158101

Abstract:

We carry out a large-scale investigation of the profitability of carry trades, using foreign exchange data for 48 countries spanning a period from 1983 to 2016 and employing a stepwise test to counter data-snooping bias. We find that, while we can confirm previous findings that the carry trade is profitable over this long period when a specific carry-trade strategy is selected based on the whole data set, even after controlling for data snooping, when we split the sample into sub-periods, the best carry-trade strategy in one sub-period is generally not profitable in the next sub-period. This finding holds true even when we include learning strategies and stop-loss strategies. Our findings thus highlight the instability of carry trades over long periods and their limitation in the sense that it is hard to predict their performance based on several years of data and therefore to choose a profitable carry-trade strategy ex ante.

3/

Author: Sakemoto

Title: Currency Carry Trades and the Conditional Factor Model

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3210768

Abstract:

This study employs a conditional factor model in order to investigate the time-varying profitability of currency carry trades. To that end, I estimate conditional alphas and betas on the popular dollar and carry factors through the use of a nonparametric approach. The empirical results illustrate that the alphas and betas vary over time. Furthermore, I find that the alpha of a high interest rate currency portfolio increases in a trough in a business cycle and in a state of high market uncertainty. However, the beta on the dollar factor decreases in these market conditions, suggesting that investors reduce the foreign currency risk exposure.

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Related to all major factor strategies (trend, momentum, value, carry, seasonality and low beta/volatility):

Authors: Baltussen, Swinkels, van Vliet

Title: Global Factor Premiums

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3325720

Abstract:

We examine 24 global factor premiums across the main asset classes via replication and new-sample evidence spanning more than 200 years of data. Replication yields ambiguous evidence within a unified testing framework with methods that account for p-hacking. The new-sample evidence reveals that the large majority of global factors are strongly present under conservative p-hacking perspectives, with limited out-of-sample decay of the premiums. Further, utilizing our deep sample, we find global factor premiums to be not driven by market, downside, or macroeconomic risks. These results reveal strong global factor premiums that present a challenge to asset pricing theories.

Notable quotations from the academic research paper:

"In this paper we study global factors premiums over a long and wide sample spanning the recent 217 years across equity index (but not single securities), bond, currency, and commodity markets.

The first objective of this study is to robustly and rigorously examine these global factor premiums from the perspective of ‘p-hacking’.

We take as our starting point the main global return factors published in the Journal of Finance and the Journal of Financial Economics during the period 2012-2018: time-series momentum (henceforth ‘trend’), cross-sectional momentum (henceforth ‘momentum’), value, carry, return seasonality and betting-against-beta (henceforth ‘BAB’). We examine these global factors in four major asset classes: equity indices, government bonds, commodities and currencies, hence resulting in a total of 24 global return factors.4

We work from the idea that these published factor premiums could be influenced by p-hacking and that an extended sample period is useful for falsification or verification tests. Figure 1, Panel A summarizes the main results of these studies.

Shown are the reported Sharpe ratio’s in previous publications, as well as the 5% significance cutoff in the grey-colored dashed line. In general, the studies show evidence on the global factor premiums, with 14 of the 22 factors (return seasonality is not tested in bonds and currencies) displaying significant Sharpe ratio’s at the conventional 5% significance level.

Further, most of the studies have differences in, amongst others, testing methodologies, investment universes and sample periods, choices that introduce degrees of freedom to the researcher. To mitigate the impact of such degrees of freedom, we reexamine the global return factors using uniform choices on testing methodology and investment universe over their average sample period (1981-2011). Figure 1, Panel B shows the results of this replicating exercise. We find that Sharpe ratios are marginally lower, with 12 of the 24 factor premiums being significant at the conventional 5% level.

The second objective of this study is to provide rigorous new sample evidence on the global return factors. To this end, we construct a deep, largely uncovered historical global database on the global return factors in the four major asset classes. This data consists of pre-sample data spanning the period 1800- 1980, supplemented with post-sample data from 2012-2016, such that we have an extensive new sample to conduct further analyses. If the global return factors were unintentionally the result of p-hacking, we would expect them to disappear for this new sample period.

Our new sample findings reveal consistent and ubiquitous evidence for the large majority of global return factors. Figure 1, Panel C summarizes our main findings by depicting the historical Sharpe ratio’s in the new sample period. In terms of economic significance, the Sharpe ratios are substantial, with an average of 0.41. Remarkably, in contrast to most out-of-sample studies we see very limited ‘out-of-sample’ decay of factor premiums.

In terms of statistical significance and p-hacking perspectives, 19 of the 24 t-values are above 3.0,19 Bayesian p-values are below 5%, and the break-even prior odds generally need to be above 9,999 to have less than 5% probability that the null hypothesis is true."

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