The information content of risk reversals in emerging market currencies/O conteudo informacional dos 'risk reversals' em moedas de paises emergentes.
| Autor | Filho, Adonias Evaristo da Costa |
| Cargo | Ensayo |
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Introduction
Is it possible to predict future returns of emerging market currencies based on information embedded in options prices? This paper investigates whether risk reversals (RR)--a measure of the skewness of exchange rate expected distribution--contain information about future returns of emerging market currencies.
Risk reversals consist in the difference between the implied volatilities of out-of-the-money call and put, of a given maturity and delta. With these parameters fixed, it implies a long position in a call and a short position in a put. Thus, by showing if the call or put has a higher cost in terms of implied volatility, it reveals the expectation of the market regarding the distribution of the exchange rate underlying the option. Risk reversals are often described as the slope of the volatility smile curve, which plots, for a given maturity, the implied volatility associated with each delta for calls and puts.
Information embedded in options prices receives considerable attention of both practitioners and authorities, since they convey forward looking information that may be useful for central banks and market participants in general.
The predictability of foreign currencies through risk reversals have been tested for major currencies. Kurbanov (2010) examined risk reversals for the EURUSD exchange rate, using weekly data. Using univariate methods, he found a positive contemporaneous relationship between changes in risk reversals and EURUSD returns. In terms of predictability, he found a negative association between past RR changes and exchange rate returns, but with limited explanatory power, casting doubt on the usefulness of risk reversals for forecasting exchange rate returns.
Along the same lines, Dunis and Lequeux (2001) analyzed the information content of risk reversals for the pairs USDJPY, USDCHF, GBPUSD and AUDUSD, using daily data and multivariate time series methods, also finding little evidence for the usefulness of risk reversals in assessing the future evolution of exchange rates.
Brunnermeier et al. (2009) investigated crash risk of currencies, i.e., sudden and abrupt depreciations of the target currency of the investment. They found that the interest rate differentials are negative associated with realized skewness, interpreting this result as a measure of crash risk. On the other hand, carry returns are positively associated with risk reversals, meaning that investors buy insurance against crash risk. Galati, Heath and McGuire (2007) argued that risk reversals capture directional uncertainty about the exchange rate, and associate this with carry trade risks. Gagnon and Chaboud (2007) documented that risk reversals increase after episodes of presumed unwinding of carry trades, in which the Yen is sharply appreciated, meaning that investors are frightened by losses, but they also found that there is no evidence of purchase of protection through risk reversal when carry trade positions are building up.
Farhi et al. (2015) developed a model to estimate a time series of the compensation for global disaster risk exposure based on exchange rate data of major currencies. The disaster risk exposure measure obtained in the model showed a strong correlation with risk-reversals. As in Brunnermeier et al. (2009), they associated risk reversals with the level of interest rate differentials, meaning that expected carry trade returns compensate investors for the risk of a sharp depreciation of the target currency.
Carr and Wu (2007) investigated exchange rate dynamics of the pairs USDJPY and GBPUSD, developing a model that delivers stochastic volatility and skewness, influenced by the time-varying feature of risk reversals found in the data. As in the afore-mentioned studies, they also documented a positive and strong correlation between currency returns and risk reversals, but the correlation between butterfly spreads and currency returns is much lower. Finally, De Bock and Carvalho Filho (2015) found that risk reversals are positively correlated with currency weakness during risk-off episodes, appearing significantly in regressions that explain the drivers of currency movements during the periods analyzed.
Despite the evidence for major currencies, at present no research has focused on the empirical content of risk reversals in emerging market currencies. The main contribution of this paper is to provide such evidence. From a theoretical point of view, this paper is related to the ongoing research on disasters and its implications for potentially solving asset pricing problems (Barro,2006; Barro and UrsUa, 2008; Tsai and Watcher,2015). Disasters are usually modeled as a shock that induces negative skewness in the distribution of output. As mentioned previously, as the price of skewness, risk reversals capture the expected skewness in the context of foreign exchange (FX) markets.
This paper is organized as follows. Apart from this introduction, section 2 presents the data used and how some variables were constructed. Section 3 pursues with analysis of the relationship between currency returns and risk reversals, mainly on the forecast ability power from one variable to the other. Section 4 examines the relationship between global risk aversion, carry trade returns, risk reversals and interest rate differentials in VAR models estimated for each country. Section 5 constructs an indicator of crash risk sentiment in emerging market currencies and shows that it is highly correlated with the VIX. Section 6 continues with the analysis of the relationship between global risk aversion, carry trade returns, risk reversals and interest rate differentials, but with a PVAR model. Finally, section 7 concludes. Appendices present unit root tests, figures of the carry trade returns series used and robustness evidence for the findings.
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Data and Descriptive Analysis
I used daily data, spanning from November 10, 2010 to October 28, 2015, for the following pairs: Brazilian Real (USDBRL, BRL), Chilean Peso (USDCLP, CLP), Indian Rupee (USDINR, INR), Indonesia Ruapiah (USDIDR, IDR), Mexican Peso (US-DMXN, MXN), Polish Zloty (USDPLN, PLN), South African Rand (USDZAR, ZAR), Korean Won (USDKRW, KRW), Israeli Shekkel (USDILS, ILS) and Turkish Lira (US-DTRY, TRY).
I also collected some data on interest rate differentials for the currencies employed. I used 3-month interest rates. For Brazil, it is the implied NDF rate, for Chile is the CLF GVT Zero Yield, for India is the 3- month TBill yield, for Indonesia, Poland, Turkey and Israel I used the interbank rate, for Mexico is the 90-day CETES rate, for South Korea is the 90-day NCD rate, and finally, for South Africa, it was the JIBAR 3-month rate. In order to construct the interest rate differential series, I subtracted each interest rate series from the 3-month US T-Bill rate. All data come from Datastream, and interest rate series used reflect availability in this program. Even though information about risk reversals for some of these currency pairs is available before the sample start date, I chose to begin the sample on the same date for all of them, for the sake of uniformity of the analysis.
Brunnermeier et al. (2009) used 25 delta 1-month risk reversals. De Bock and Carvalho Filho (2015) used 25 delta at the 3-month maturity. Carr and Wu (2007) presented data for 10 and 25 delta risk reversals for many maturities. Kurbanov (2010) employed 1-month 10 and 25 delta risk reversals and butterflies. Lastly, Dunis and Luqueux (2001) analyzed 1-month 25 delta risk reversals. Aligned with the related literature, I have chosen to use 25 delta 3-month risk reversals. Table 1 reports a strong positive correlation across deltas and maturities, revealing a strong co-movement between risk reversals of a given currency pair. Figure 2 shows the evolution of risk reversals and exchange rates in the sample period.
I also constructed carry trade return series (variables [z.sub.t], as specified on equation 2 below) for the currency pairs used in the analysis, which encompasses both the currency returns and interest rate carry. Following Brunnermeier et al. (2008), the return series have been constructed using the following formulas:
[s.sub.t] = log (nominal exchange rate) (1)
[z.sub.t+1] = ([i.sub.t] - [i.sup.*.sub.t]) - [DELTA][s.sub.t+1] (2)
Where [z.sub.t], the carry trade return, is the sum of FX and interest rate returns, and [DELTA][s.sub.t+1] is the currency return. A positive value for the currency returns means a depreciation of the domestic (emerging market) currencies relative to the dollar. For the interest rate differential, I used the series constructed and described above, considering a year with 52 weeks (1) therefore, I have daily data on total returns, exchange rate returns, 25-delta 3-month risk reversals and 3-month interest rate differentials. In order to provide additional evidence to the empirical analysis, I used in the estimations daily total return data obtained from Bloomberg as well. For each currency, I had 1296 observations, from November 2010 to October 2015. The exception is the Chilean peso, for which the interest rate data begin in June 2011, and therefore I used a shorter sample in the analysis.
Figure 2 plots, for each currency pair, currency levels and risk reversals over the sample. Figure 3 presents interest differential data, while figures 9 and 10 in the Appendix show the carry trade return series used, respectively the variables [z.sub.t] constructed and the daily return series from Bloomberg.
Figure 1 shows that, with the exception of the INR, there is a positive correlation between the average level of risk reversals and average interest rate differentials over the sample period, implying that overall higher interest rate differentials lead to higher risks, or investors in high yielding currencies are exposed to crash risks--sudden or abrupt depreciations of the currencies in which investments were made- and to...
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