Asset Volatility - .Asset volatility is a primitive variable in structural models of credit spreads

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Transcript of Asset Volatility - .Asset volatility is a primitive variable in structural models of credit spreads

  • Asset Volatility

    Maria Correia

    London Business School

    Johnny Kang

    AQR Capital Management LLC

    Scott Richardson

    London Business School

    February 13, 2014


    Asset volatility is a primitive variable in structural models of credit spreads. We

    evaluate alternative measures of asset volatility using information from (i) historical

    security returns (both equity and credit), (ii) implied volatilities extracted from equity

    options, and (iii) financial statements. For a large sample of US firms, we find that

    combining information from all three sources improves the explanatory power of

    corporate bankruptcy models and cross-sectional variation in credit spreads. Market

    based (accounting) measures of asset volatility appear to reflect systematic

    (idiosyncratic) sources of volatility and combining both sources of information

    generates a superior measure of total asset volatility that is relevant for understanding

    credit spreads.

    JEL classification: G12; G14; M41

    Key words: credit spreads, volatility, bankruptcy, default.

    We are grateful to Arne Staal and Philippe Vannerem at Barclays Capital for providing us access to the

    Barclays Capital bond dataset. We also thank Cliff Asness, Max Bruche, John Campbell, John Hand,

    Jens Hilscher, Ronen Israel, Anya Kleymenova, Sonia Konstantinidi, Caroline Pflueger, Tjomme

    Rusticus and seminar participants at Cass Business School, London Business School, and University of

    Porto for helpful discussion and comments. Richardson has an ongoing consulting relationship with

    AQR Capital, which invests in, among other strategies, securities studied in this paper. The views and

    opinions expressed herein are those of the authors and do not necessarily reflect the views of AQR

    Capital Management, LLC (AQR) its affiliates, or its employees. This information does not

    constitute an offer or solicitation of an offer, or any advice or recommendation, by AQR, to purchase

    any securities or other financial instruments, and may not be construed as such.

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    1. Introduction

    Our objective is to compare and contrast alternative measures of asset

    volatility in their ability to explain security prices. A seminal paper by Merton (1974)

    developed structural models as a benchmark to describe credit spreads. In these

    models asset volatility is arguably the most important primitive variable for

    determining distance to default and, consequently, spreads. In credit markets it is

    total (i.e., both systematic and idiosyncratic) asset volatility that is relevant for

    security prices (Campbell and Taksler, 2003). Thus, credit markets are a natural

    setting to evaluate the relative importance of measures of asset volatility for security


    We examine three primary sources of information to measure asset volatility.

    First, we extract information from secondary equity and credit markets to measure

    equity volatility, debt volatility and their correlations. We derive several measures of

    historical asset volatility ranging from simplistic deleveraging of historical equity

    volatility to a complete measure that uses historical return volatilities and historical

    return correlations (see e.g., Schaefer and Strebulaev, 2008). Second, we extract

    information from equity option markets. Specifically, we use the implied volatility

    for at the money put and call options. Third, we extract information from firm

    financial statements about the volatility of firms unlevered profitability.

    We find that combining information about asset volatility from market based

    (historical and forward looking) and accounting based (historical) information

    improves estimates of corporate bankruptcy.

    Using a large sample of firms with liquid corporate bond data, we find that a

    one standard deviation change in our market (accounting) based component measures

    of asset volatility translates to an increase of 5.5 (2.5) percent in the conditional

  • 2

    probability of bankruptcy. We further find that market based and accounting based

    estimates of asset volatility improve explanatory power of cross-sectional credit

    spread regression models. Importantly, we show that regressing credit spreads onto a

    constrained non-linear function of asset volatility and leverage the credit spread

    implied by the Merton model performs significantly better than regressing onto

    these variables linearly and independently. In constrained regression analysis where

    we incorporate component measures of asset volatility into theoretically justified

    implied spreads, we find that all component measures of asset volatility are relevant.

    Specifically, historical market, forward looking market, and historical accounting

    component measures of asset volatility account for 24.4%, 23.9%, and 19.0% of the

    cross-sectional variation in credit spreads respectively. We also find some evidence

    that the relative importance of accounting based measures of asset volatility is greatest

    for high yield corporate bonds relative to investment grade bonds.

    Measures of fundamental volatility have recently been examined in the context

    of equity option markets (see e.g., Goodman, Neamtiu and Zhang, 2012 and Sridharan,

    2012). A limitation of these analyses is that fundamental measures of volatility are

    related to short dated (less than 90 day) straddle returns. Financial statements are

    released at a quarterly frequency which makes them a slow moving source of

    information about volatility. The credit instruments we examine (corporate bonds and

    CDS) have considerably longer duration, thus making credit spreads a more natural

    setting to examine the relative importance of market and accounting based component

    measures of asset volatility.

    Prior studies have documented an association between idiosyncratic equity

    (Campbell and Taksler, 2003) and bond (Gemmil and Keswani, 2011) volatility and

    credit spreads at an aggregate and issuer level. Taking these findings into account and

  • 3

    to help better understand the relative importance of market based and accounting

    based component measures of asset volatility we explore the mapping of these

    respective measures to systematic and idiosyncratic volatility. We find that average

    within industry pairwise correlations of market measures of returns (both equity and

    credit market returns) are significantly larger than within industry pairwise

    correlations of changes in seasonally adjusted accounting rates of return. This

    suggests that market based measures of asset volatility are more likely to reflect

    systematic sources of volatility. To further explore this possibility, we form factor

    mimicking portfolios based on market and accounting based measures of asset

    volatility. We find that the market based asset volatility factor mimicking portfolio

    has a significantly higher beta with respect to aggregate asset returns. Together this

    evidence suggests that combining measures of asset volatility from market based and

    accounting based measures yields a superior measure of asset volatility due to a

    combination of systematic and idiosyncratic measures of asset volatility. This finding

    is consistent with the two different sources of firm-level return volatility identified by

    (Vuolteenaho 2002), cash flow news, which are largely firm-specific, and expected

    return news, with a stronger systematic component. As discussed earlier, in the

    context of credit derivatives total asset volatility is the relevant measure, and not just

    systematic volatility.

    The rest of the paper is structured as follows. Section 2 describes our sample

    selection and research design. Section 3 presents our empirical analysis and

    robustness tests, and section 4 concludes.

  • 4

    2. Sample and research design

    2.1 Secondary credit market data

    Our analysis is based on a comprehensive panel of US corporate bond data,

    which includes all the constituents of (i) Barclays U.S. Corporate Investment Grade

    Index, and (ii) Barclays U.S. High Yield Index. The data includes monthly returns and

    bond characteristics from September 1988 to February 2013. We exclude financial

    firms, with SIC codes between 6000 and 6999. Table 1, Panel A shows the industry

    composition of the resulting sample, using Barclays Capitals industry definitions.

    Approximately 35% of the sample firms are consumer products firms. Capital Goods

    firms and Basic Industry make up for another 20% of the sample.

    2.2 Representative bond

    Given that corporate issuers often issue multiple bonds and that our analysis is

    directed at measuring asset volatility of the issuer, we need to select a representative

    bond for each issuer. To do this, we follow the criteria in Haesen, Houweling and

    VanZundert (2012). We repeat this exercise every month for our sample period. The

    criteria used for identifying the representative bond are selected so as to create a

    sample of liquid and cross-sectionally comparable bonds. Specifically, we select

    representative bonds on the basis of (i) seniority, (ii) maturity, (iii) age, and (iv) size.

    First, we filter bonds on the basis