nep-fmk New Economics Papers
on Financial Markets
Issue of 2013‒05‒19
six papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Return-Volatility Relationship: Insights from Linear and Non-Linear Quantile Regression By David E. Allen; Abhay K. Singh; Robert J. Powell; Michael McAleer; James Taylor; Lyn Thomas
  2. Aggregate Stock Market Illiquidity and Bond Risk Premia By Kees E. Bouwman; Elvira Sojli; Wing Wah Tham
  3. Why is Price Discovery in Credit Default Swap Markets News-Specific? By Ian W. Marsh; Wolf Wagner
  4. Volatility Spillovers from the US to Australia and China across the GFC By David E. Allen; Michael McAleer; R.J. Powell; A.K. Singh
  5. Has the Basel Accord Improved Risk Management During the Global Financial Crisis? By Michael McAleer; Juan-Ángel Jiménez-Martín; Teodosio Pérez-Amaral
  6. Cross-Border Mergers and Acquisitions: The Role of Private Equity Firms By Mark Humphery-Jenner; Zacharias Sautner; Jo-Ann Suchard

  1. By: David E. Allen (Edith Cowan University, Australia); Abhay K. Singh (Edith Cowan University, Australia); Robert J. Powell (Edith Cowan University, Australia); Michael McAleer (Erasmus University Rotterdam, Complutense University of Madrid, Spain, and Kyoto University, Japan); James Taylor (University of Oxford, Oxford); Lyn Thomas (University of Southampton, Southampton)
    Abstract: The purpose of this paper is to examine the asymmetric relationship between price and implied volatility and the associated extreme quantile dependence using linear and non linear quantile regression approach. Our goal in this paper is to demonstrate that the relationship between the volatility and market return as quantified by Ordinary Least Square (OLS) regression is not uniform across the distribution of the volatility-price return pairs using quantile regressions. We examine the bivariate relationship of six volatility-return pairs, viz. CBOE-VIX and S&P-500, FTSE-100 Volatility and FTSE-100, NASDAQ-100 Volatility (VXN) and NASDAQ, DAX Volatility (VDAX) and DAX-30, CAC Volatility (VCAC) and CAC-40 and STOXX Volatility (VSTOXX) and STOXX. The assumption of a normal distribution in the return series is not appropriate when the distribution is skewed and hence OLS does not capture the complete picture of the relationship. Quantile regression on the other hand can be set up with various loss functions, both parametric and non-parametric (linear case) and can be evaluated with skewed marginal based copulas (for the non linear case). Which is helpful in evaluating the non-normal and non-linear nature of the relationship between price and volatility. In the empirical analysis we compare the results from linear quantile regression (LQR) and copula based non linear quantile regression known as copula quantile regression (CQR). The discussion of the properties of the volatility series and empirical findings in this paper have significance for portfolio optimization, hedging strategies, trading strategies and risk management in general.
    Keywords: Return-Volatility relationship, quantile regression, copula, copula quantile regression, volatility index, tail dependence
    JEL: C14 C58 G11
    Date: 2013–01–18
    URL: http://d.repec.org/n?u=RePEc:dgr:uvatin:2013020&r=fmk
  2. By: Kees E. Bouwman (Erasmus University Rotterdam); Elvira Sojli (Erasmus University Rotterdam); Wing Wah Tham (Erasmus University Rotterdam)
    Abstract: We assess the effect of aggregate stock market illiquidity on U.S. Treasury bond risk premia. We find that the stock market illiquidity variable adds to the well established Cochrane-Piazzesi and Ludvigson-Ng factors. It explains 10%, 9%, 7%, and 7% of the one-year-ahead variation in the excess return for two-, three-, four-, and ve-year bonds respectively and increases the adjusted R<SUP>2</SUP> by 3-6% across all maturities over Cochrane and Piazzesi (2005) and Ludvigson and Ng (2009) factors. The effects are highly statistically and economically significant both in and out of sample. We find that our result is robust to and is not driven by information from open interest in the futures market, long-run inflation expectations, dispersion in beliefs, and funding liquidity. We argue that stock market illiquidity is a timely variable that is related to " right-to-quality" episodes and might contain information about expected future business conditions through funding liquidity and investment channels.
    Keywords: Market liquidity; Bond risk premia; Flight-to-quality
    JEL: G10 G20 G14
    Date: 2012–12–12
    URL: http://d.repec.org/n?u=RePEc:dgr:uvatin:2012140&r=fmk
  3. By: Ian W. Marsh (Cass Business School); Wolf Wagner (Tilburg University, and Duisenberg school of finance)
    Abstract: We analyse daily lead-lag patterns in US equity and credit default swap (CDS) returns. We first document that equity returns robustly lead CDS returns. However, we find that the CDS-lag is due to <I>common</I> (and not firm-specific) news and arises predominantly in response to <I>positive</I> (instead of negative) equity market news. We provide an explanation for this news-specific price discovery based on dealers in the CDS market exploiting their informational advantage vis-à-vis institutional investors with hedging demands. In support of this explanation we find that the CDS-lag and its news-specificity are related to various firm-level proxies for hedging demand in the cross-section as well measures for economy-wide informational asymmetries over time.
    Keywords: price discovery, hedging demand, CDS markets, equity markets
    JEL: G1 G12 G14
    Date: 2012–04–02
    URL: http://d.repec.org/n?u=RePEc:dgr:uvatin:2012033&r=fmk
  4. By: David E. Allen (Edith Cowan University); Michael McAleer (Econometric Institute, Erasmus University Rotterdam, Complutense University of Madrid, and Kyoto University); R.J. Powell (Edith Cowan University); A.K. Singh (Edith Cowan University)
    Abstract: This paper features an analysis of volatility spillover effects from the US market, represented by the S&P500 index to the Australian capital market as represented by the Australian S&P200 for a period running from 12th September 2002 to 9th September 2012. This captures the impact of the Global Financial Crisis (GFC). The GARCH analysis features an exploration of whether there are any spillover effects in the mean equations as well as in the variance equations. We adopt a bi-mean equation to model the conditional mean in the Australian markets plus an ARMA model to capture volatility spillovers from the US. We also apply a Markov Switching GARCH model to explore the existence of regime changes during this period and we also explore the non-constancy of correlations between the markets and apply a moving window of 120 days of daily observations to explore time-varying conditional and fitted correlations. There appears to be strong evidence of regime switching behaviour in the Australian market and changes in correlations between the two markets particularly in the period of the GFC. We also apply a tri-variate Cholesky-GARCH model to include potential effects from the Chinese market, as represented by the Hang Seng Index.
    Keywords: Volatility spillovers, Markov-switching GARCH, Cholesky-GARCH, Time-varying correlations
    JEL: C22 C32 G11 G15
    Date: 2013–01–08
    URL: http://d.repec.org/n?u=RePEc:dgr:uvatin:2013009&r=fmk
  5. By: Michael McAleer (Erasmus University Rotterdam); Juan-Ángel Jiménez-Martín (Complutense University of Madrid); Teodosio Pérez-Amaral (Complutense University of Madrid)
    Abstract: The Basel II Accord requires that banks and other Authorized Deposit-taking Institutions (ADIs) communicate their daily risk forecasts to the appropriate monetary authorities at the beginning of each trading day, using one or more risk models to measure Value-at-Risk (VaR). The risk estimates of these models are used to determine capital requirements and associated capital costs of ADIs, depending in part on the number of previous violations, whereby realised losses exceed the estimated VaR. In this paper we define risk management in terms of choosing from a variety of risk models, and discuss the selection of optimal risk models. A new approach to model selection for predicting VaR is proposed, consisting of combining alternative risk models, and we compare conservative and aggressive strategies for choosing between VaR models. We then examine how different risk management strategies performed during the 2008-09 global financial crisis. These issues are illustrated using Standard and Poor’s 500 Composite Index.
    Keywords: Value-at-Risk (VaR), daily capital charges, violation penalties, optimizing strategy, risk forecasts, aggressive or conservative risk management strategies, Basel Accord, global financial crisis
    JEL: G32 G11 G17 C53 C22
    Date: 2013–01–08
    URL: http://d.repec.org/n?u=RePEc:dgr:uvatin:2013010&r=fmk
  6. By: Mark Humphery-Jenner (University of New South Wales, Tilburg University); Zacharias Sautner (University of Amsterdam, Duisenberg school of finance); Jo-Ann Suchard (University of New South Wales)
    Abstract: We study the role of private equity firms in cross-border mergers and acquisitions. We find that private equity-owned firms are more likely to become targets in crossborder M&A transactions. This effect is particularly strong in transactions where the target or its shareholders actively reach out for an acquirer. On average, cross-border deals with private equity-involvement are not associated with higher announcement returns. However, announcement returns are higher if the acquirer is owned by a private equity firm and the target is from a country with poor corporate governance. We provide evidence indicating that the international networks and connections that result from prior cross-border deals can explain why private equity firms create value in such deals. Our findings suggest that private equity firms can help to reduce information asymmetries in certain cross-border M&A deals. We perform several tests to address possible endogeneity concerns.
    Keywords: G34, G32, G24
    Date: 2012–03–29
    URL: http://d.repec.org/n?u=RePEc:dgr:uvatin:2012031&r=fmk

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