Cross Economic Determinants of Implied Volatility Smile Dynamics: Three Major European Currency Options

Published date01 November 2016
Date01 November 2016
Cross Economic Determinants of
Implied Volatility Smile Dynamics:
Three Major European Currency
Qian Han, Jufang Liang and Boqiang Wu
The Wang Yanan Institute for Studies in Economics (WISE), National Key Lab of Econometrics,
Xiamen University, Xiamen, China, P.R.
This paper examines the contemporaneous and leadlag relationships between
economic variables and implied volatility smiles for three major European
currency options. We nd that cross economic determinants are at least as
important as own economic variables in explaining the dynamics of implied
volatility smiles. Out-of-sample tests also suggest that cross economic variables
are important in predicting an economys currency option smile. These ndings
suggest that the price impact from cross economic determinants may help ll the
gap between the theoretical and the practical implied volatility skews.
Keywords: implied volatility smile, economic determinants, currency options
JEL classification: G13
1. Introduction
The relationship between asset returns and the real economy has been widely studied for
different nancial markets. Fama (1990) and Schwert (1990) nd that real stock returns
contain rich information about future production growth rates. Estrella and Mishkin
The authors thank Robert I. Webb (the discussant) and other participants in the 3rd
International Conference on Deri vatives and Risk Management for thei r valuable
discussions and comments. We also thank the anonymous referee and the Editor for their
very constructive comments and suggestions. Hans research is supported by the National
Science Foundation of China (project no. 71471153). Liangs research is supported by
Overseas Visit Project of Xiamen University Graduate School. Part of his work was
completed during his visit to the University of North Carolina at Charlotte. All errors, if
any, are those of the authors.
European Financial Management, Vol. 22, No. 5, 2016, 817852
doi: 10.1111/eufm.12072
© 2015 John Wiley & Sons, Ltd.
(1998) examine the out-of-sample performance of several nancial variables, including
stock prices, in predicting US recessions. Stock and Watson (1999, 2003) show that
asset prices can be used to forecast ination and output. Hong and Yogo (2012) argue that
open interest of commodity futures is more informative about future economic activity
than futures prices. Welch and Goyal (2008) and Rapach et al. (2013) investigate the out-
of-sample predictability of real economic variables on equity premium. Beber and
Brandt (2006) examine the effect of macroeconomic news announcements on the
moments of risk-neutral state price densities extracted from bond options markets.
Ludvigson and Ng (2009) nd signicant predictive power of real activity for bond risk
This paper studies the cross asseteconomy relationship, i.e., how asset prices in one
country or region may be affected by or contain predictive information about economic
variables in another country or region.
Specically, we examine the role of own, and in
particular cross, economic determinants on three major European currency option
impliedvolatilitysmile (IVS) dynamics. These options are over-the-counter (OTC)
options for euro (EUR), British pound (GBP) and Swiss franc (CHF), all quoted against
US dollars (USD). We address three questions. Which own and cross economic variables
are relevant to explain the contemporaneous dynamics of the three currency option IVS
dynamics? Are there any Granger causality relationships between each of these
economic variables and the currency option IVS dynamics? Are cross economic
variables important in the out-of-sample predictions of the currency option IVS
To answer these questions, we rst use the option price quotes, i.e., at-the-money
(ATM) volatility, risk reversal (RR) and butteryspread(By), to capture the
shape of the IVS. The ATM corresponds to the level of the smile curve, RR describes
the asymmetry of the curve and By proxies the curvature of the curve. We further
propose that RR measures net speculative demand (NSD) and By is a measure
of the gross hedging pressure (GHP) of the corresponding currency. The
economic determinants we consider are largely taken from the literature and can
be divided into three groups: variables from the foreign exchange market, from the
stock market and from the bond market. We use ordinary least squares (OLS)
regressions to test the contemporaneous correlations between curve characteristics
and own or cross economic variables, and then apply a multivariate autoregres-
sive regression (VAR) analysis to examine their Granger-causality relationships.
Impulse response and variance decomposition analyses are conducted as robustness
We nd that cross economic variables are at least as, sometimes more, important
determinants of currency IVS dynamics than their own economic variables.
Contemporaneously, historical volatilities of currencies are positively associated with
ATM volatilities of other currencies. A negative spot rate return in one currency market
is accompanied by an increase in the ATM volatilities of other currencies. The
The economic variables studied in this paper are not strictly real economic activity proxies.
Rather, they are variables from the nancial markets. However, the literature of empirical
option pricing has termed them economic determinantsto differentiate these determinants
from the underlying asset characteristics. We use this term to keep consistency with this line
of research.
© 2015 John Wiley & Sons, Ltd.
818 Qian Han, Jufang Liang and Boqiang Wu
Switzerland stock market characteristics are closely associated with the RR and By for
the pound and euro. And default risk in the euro bond market seems highly associated
with the implied volatilities of the Swiss franc and the pound.
The importance of cross economic variables is also shown in the Granger-causality
tests. For example, the historic volatility of the stock index in the euro area has predictive
power for the buttery spread of the pound. The pound spot return Granger-causes the
risk reversal of all three options. The risk reversals of the euro and pound both positively
Granger-cause the spot rate trends of the other two currencies. Default spread does not
play any role in the Granger causality tests for buttery spread and risk reversal but is
very important for ATM volatility. The ATM volatilities of the Swiss franc and the euro
Granger-cause the default spreads for all three economies. Both the impulse response and
variance decomposition analyses conrm our ndings. In particular, the variance
decomposition analysis, which explicitly quanties the magnitudes of contributions to
the forecast error variance of the smile dynamics of both own and cross economic
variables, nds a more signicant role played by cross economic variables. Because
previous studies claim that in-sample analyses may be unreliable due to over-tting
concerns, we also conduct out-of-sample tests to examine the predictive power of cross
economic variables. The results suggest that cross economic variables are at least as
important in predicting an economys currency option smile dynamics out-of-sample as
its own economic variables.
Why is a countrys currency option IVS dynamics signicantly determined by another
countrys economic variables? The unprecedented speed of economic and nancial
globalisation over the past few decades seems to be a plausible reason. Economic
interaction among different countries and regions has soared, and nancial markets
around the world have seen increased return correlations, volatility spillover and risk
contagion. Rizova (2010) shows that, in a two-country, Lucas-tree model with
information diffusion, returns in one country can predict returns in a trading partner
country. European economic integration is probably the most successful regional
integration so far in history and European Union countries trade heavily with each other
due to historical and geographical bonds among union members. Table 1 lists the top
trading partners for the three nations under consideration. For the UK, the European
Union dominates other countries and regions in both imports and exports, while
Switzerland is ranked 3rd in exports and 5th in imports. The EU and Switzerland are each
others major trading partners with over 50% (74%) of exports (imports) of Switzerland
going to (coming from) the EU. In theory, these close trade ties will facilitate information
diffusion across the three economies, leading to Granger-causal relationships between
their asset prices.
In addition, macroeconomists tend to agree that currency value is a good economic
indicator. If this is the case, then the progress of economic integration should strengthen
relationships between currency returns, consequently increasing the correlation between
currency option prices. In fact, as shown in Figure 1, the correlation between USD/EUR
and USD/CHF returns is as high as 72% (USD/EUR and USD/GBP is 65% while USD/
CHF and USD/GBP is 42%).
An alternative explanation is that, as pointed out in Rapach et al. (2013), information
diffuses from the market with the most investor attention to other markets. The three
currencies in this study are all among the most actively traded and most inuential
currencies in the global foreign exchange market. Indeed, the euro is the major
alternative reserve currency after the US dollar, the Swiss franc is commonly perceived
© 2015 John Wiley & Sons, Ltd.
Cross Economic Determinants of Implied Volatility Smile Dynamics 819

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