A Comparative Analysis Of The Informational Efficiency Of The Fixed Income Market In Seven European Countries

Aurelio Fernandez Bariviera

Universitat Rovira i Virgili – Department of Business

M. Belén Guercio

National University of the South – Instituto de Investigaciones Económicas y Sociales del Sur (IIESS)

Lisana B. Martinez

National University of the South – Instituto de Investigaciones Económicas y Sociales del Sur (IIESS)

April 12, 2012

Economics Letters, Vol. 116, No. 3, 2012


This letter investigates the time-varying behavior of long memory in sovereign and corporate bond indices of seven European Union countries from July 1998 to November 2011. We compute the Hurst exponent and detect that the current financial crisis affects more the informational efficiency of the corporate bond market than the sovereign bond market.

A Comparative Analysis Of The Informational Efficiency Of The Fixed Income Market In Seven European Countries – Introduction

Over a century ago Bachelier (1900) developed the first mathematical model of security prices, applying the arithmetic Brownian motion model to French bonds. The formalization of the Efficient Market Hypothesis (EMH) remained latent until the development of Samuelson (1965) and the definition and classification by Fama (1970). Briefly, the EMH requires that returns of financial assets follow a memoryless stochastic process with respect to the underlying information set.

The weak form of informational efficiency excludes the possibility of finding, systematically, profitable trading strategies. As a corollary, returns time series cannot exhibit predictable memory content. However, there are several studies that find long memory in financial time series, using different methods. For example, Barkoulas et al. (2000) and Blasco and Santamar ´?a (1996) find long memory in the Greek Stock Exchange and Spanish Stock Exchange respectively. Cheung and Lai (1995) find empirical evidence of long memory in 5 out of 18 developed stock markets and Barkoulas and Baum (1996) do not find strong convincing evidence against the random walk model in US stock returns. In spite of the fact that fixed income instruments are very important in the composition of investment portfolios and in firm and government financing, they have been less studied than stocks. Carbone et al. (2004) find long memory in German stock and sovereign bondmarkets and McCarthy et al. (2009) find long memory in yields of corporate bonds and in the spread of returns of corporate bonds and treasury bonds. Another issue in the literature is the time varying behavior of the market efficiency. The reasons for the varying memory remains a puzzle. In this aspect Ito and Sugiyama (2009) find that inefficiency varies through time in the US stock market. Bariviera (2011) finds that time varying long-range dependence in the Thai Stock Market is weakly influenced by the liquidity level and market size. Cajueiro et al. (2009) find that financial market liberalization increases the informational efficiency in the Greek Stock Market. Kim et al. (2011) find that return predictability is altered by political and economic crises but not during market crashes.

The aim of this letter is to analyze the evolution of the long memory in corporate and sovereign bonds indices of seven EU countries. This letter contributes to the literature in several aspects. First, it finds evidence of the influence of the financial crisis on the informational efficiency of the fixed income market. Second, it shows the different behavior of the sovereign and corporate bond markets since the inception of the euro. Third, it expands the empirical literature on the long-term dependence in fixed income markets.

The letter is organized as follows. Section 2 presents the data and methodology. Section 3 shows the results. Finally Section 4 presents the main conclusion of our analysis.

2. Data and methodology

We used daily data for sovereign bond indices (WBGI, Citigroup) and corporate bond indices (Euro Aggregate Corporate, Barclays) of seven European Union countries: Austria, Belgium, France, Germany, Italy, Netherlands and Spain. All data used in this paper was retrieved from DataStream. The period under examination is from 31/07/1998 to 04/11/2011, except for government bond indices for Germany and Spain which begins on 03/09/1998, for a total of 3460 observations. The period under analysis is relevant because it includes a major financial turmoil.

The Hurst exponent H characterizes the scaling behavior of the range of cumulative departures of a time series from its mean. Since the seminal paper of Hurst (1951), several methods (both parametric and non-parametric) have been developed to calculate the Hurst exponent. For a survey on the different methods for estimating long range dependences see Taqqu et al. (1995), Montanari et al. (1999) and Serinaldi (2010). We use the Detrended Fluctuation Analysis (DFA) method developed by Peng et al. (1994) because, as highlighted by Grau-Carles (2000), it avoids the spurious detection of long-range dependence due to nonstationary data. The algorithm is described in detail in Peng et al. (1995).

Departing from daily returns1, and following Cajueiro and Tabak (2004), we estimate the Hurst exponent using a four year sliding window (1024 datapoints). This rolling sample approach works as follows: we compute the Hurst exponent for the first 1024 returns, then we discard the first return and add the following return of the time series, and continue this way until the end of data. Thus, each H estimate is calculated from data samples of the same size. The last H estimate covers the period from 04/12/2007 until 04/11/2011. We obtained an average of 2430 Hurst estimates. In Figure 1 we can observe the evolution through time of the Hurst exponents of sovereign and corporate indices.

European Countries

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