nep-fmk New Economics Papers
on Financial Markets
Issue of 2012‒11‒11
eleven papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Have financial markets become more informative? By Jennie Bai; Thomas Philippon; Alexi Savov
  2. The Volatility-Return Relationship:Insights from Linear and Non-Linear Quantile Regressions By David E Allen; Abhay K Singh; Robert J Powell; Michael McAleer; James Taylor; Lyn Thomas
  3. The Stock Market Crash of 2008 Caused the Great Recession By Roger Farmer
  4. Multivariate statistical analysis for portfolio selection of italian stock market By Alessia Naccarato; Andrea Pierini
  5. The Risk Map: A New Tool for Validating Risk Models By Gilbert Colletaz; Christophe Hurlin; Christophe Pérignon
  6. Margin Backtesting By Christophe Hurlin; Christophe Pérignon
  7. A Theoretical and Empirical Comparison of Systemic Risk Measures By Sylvain Benoit; Gilbert Colletaz; Christophe Hurlin; Christophe Pérignon
  8. Has the Basel Accord Improved Risk Management During the Global Financial Crisis? By Michael McAleer; Juan-Angel Jimenez-Martin; Teodosio Perez-Amaral
  9. Impact of Changes in the Global Financial Regulatory Landscape on Asian Emerging Markets By Watanagase, Tarisa
  10. Covered bonds, core markets, and financial stability By Kartik Anand; James Chapman; Prasanna Gai;
  11. Common factors in credit defaults swaps markets By Yi-Hsuan Chen; Wolfgang Karl Härdle; ;

  1. By: Jennie Bai; Thomas Philippon; Alexi Savov
    Abstract: The finance industry has grown. Financial markets have become more liquid. Information technology has improved. But have prices become more informative? Using stock and bond prices to forecast earnings, we find that the information content of market prices has not increased since 1960. The magnitude of earnings surprises, however, has increased. A baseline model predicts that as the efficiency of information production increases, prices become more disperse and covary more strongly with future earnings. The forecastable component of earnings improves capital allocation and serves as a direct measure of welfare. We find that this measure has remained stable. A model with endogenous information acquisition predicts that an increase in fundamental uncertainty also increases informativeness as the incentive to produce information grows. We find that uncertainty has indeed increased outside of the S&P 500, but price informativeness has not.
    Keywords: Financial markets ; Prices ; Information technology ; Investments ; Stock - Prices ; Uncertainty
    Date: 2012
  2. By: David E Allen (School of Accouting Finance & Economics, Edith Cowan University, Australia); Abhay K Singh (School of Accouting Finance & Economics, Edith Cowan University, Australia); Robert J Powell (School of Accouting Finance & Economics, Edith Cowan University, Australia); Michael McAleer (Erasmus School of Economics, Erasmus University Rotterdam, Institute for Economic Research,Kyoto University, and Department of Quantitative Economics, Complutense University of Madrid); James Taylor (Said Business School, University of Oxford, Oxford); Lyn Thomas (Southampton Management School, University of Southampton, Southampton)
    Abstract: This paper examines the asymmetric relationship between price and implied volatility and the associated extreme quantile dependence using a linear and non- linear quantile regression approach. Our goal is to demonstrate that the relationship between the volatility and market return, as quantied by Ordinary Least Square (OLS) regression, is not uniform across the distribution of the volatility-price re- turn pairs using quantile regressions. We examine the bivariate relationships of six volatility-return pairs, namely: 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 (VS- TOXX) and STOXX. The assumption of a normal distribution in the return series is not appropriate when the distribution is skewed, and hence OLS may not capture a 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 prop- erties of the volatility series and empirical ndings in this paper have signicance 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: 2012–11
  3. By: Roger Farmer (University of California Los Angeles)
    Abstract: This paper argues that the stock market crash of 2008, triggered by a collapse in house prices, caused the Great Recession. The paper has three parts. First, it provides evidence of a high correlation between the value of the stock market and the unemployment rate in U.S. data since 1929. Second, it compares a new model of the economy developed in recent papers and books by Farmer, with a classical model and with a textbook Keynesian approach. Third, it provides evidence that fiscal stimulus will not permanently restore full employment. In Farmer's model, as in the Keynesian model, employment is demand determined. But aggregate demand depends on wealth, not on income.
    Date: 2012
  4. By: Alessia Naccarato; Andrea Pierini
    Abstract: The use of bivariate cointegrated vector autoregressive models and Baba-Engle-Kraft-Kroner models ( Engle et al. 1995), is proposed for the selection of a stock portfolio (Markowitz type portfolio) based on estimates of average returns on shares and the volatility of share prices. The model put forward envisages the use of explicative variables. This article employs the intrinsic value of shares as a variable, which will make it possible to take the theory of value into account. The model put forward is applied to a series of data regarding the prices of 150 shares traded on the Italian stock market.
    Keywords: Markowitz Portfolio, Cointegrated Vector Autoregressive Models, BEKK Model
    JEL: C58
    Date: 2012–10
  5. By: Gilbert Colletaz (LEO - Laboratoire d'économie d'Orleans - CNRS : UMR6221 - Université d'Orléans); Christophe Hurlin (LEO - Laboratoire d'économie d'Orleans - CNRS : UMR6221 - Université d'Orléans); Christophe Pérignon (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - GROUPE HEC - CNRS : UMR2959)
    Abstract: This paper presents a new method to validate risk models: the Risk Map. This method jointly accounts for the number and the magnitude of extreme losses and graphically summarizes all information about the performance of a risk model. It relies on the concept of a super exception, which is de.ned as a situation in which the loss exceeds both the standard Value-at-Risk (VaR) and a VaR de.ned at an extremely low coverage probability. We then formally test whether the sequences of exceptions and super exceptions are rejected by standard model validation tests. We show that the Risk Map can be used to validate market, credit, operational, or systemic risk estimates (VaR, stressed VaR, expected shortfall, and CoVaR) or to assess the performance of the margin system of a clearing house.
    Keywords: Financial Risk Management; Tail Risk; Basel III
    Date: 2012–10–28
  6. By: Christophe Hurlin (LEO - Laboratoire d'économie d'Orleans - CNRS : UMR6221 - Université d'Orléans); Christophe Pérignon (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - GROUPE HEC - CNRS : UMR2959)
    Abstract: This paper presents a validation framework for collateral requirements or margins on a derivatives exchange. It can be used by investors, risk managers, and regulators to check the accuracy of a margining system. The statistical tests presented in this study are based either on the number, frequency, magnitude, or timing of margin exceedances, which are defined as situations in which the trading loss of a market participant exceeds his or her margin. We also propose an original way to validate globally the margining system by aggregating individual backtesting statistics obtained for each market participant.
    Keywords: Collateral Requirements; Futures Markets; Tail Risk; Derivatives Clearing
    Date: 2012–10–28
  7. By: Sylvain Benoit (LEO - Laboratoire d'économie d'Orleans - CNRS : UMR6221 - Université d'Orléans); Gilbert Colletaz (LEO - Laboratoire d'économie d'Orleans - CNRS : UMR6221 - Université d'Orléans); Christophe Hurlin (LEO - Laboratoire d'économie d'Orleans - CNRS : UMR6221 - Université d'Orléans); Christophe Pérignon (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - GROUPE HEC - CNRS : UMR2959)
    Abstract: We propose a theoretical and empirical comparison of the most popular systemic risk measures. To do so, we derive the systemic risk measures in a common framework and show that they can be expressed as linear transformations of firms' market risk (e.g., beta). We also derive conditions under which the different measures lead similar rankings of systemically important financial institutions (SIFIs). In an empirical analysis of US financial institutions, we show that (1) different systemic risk measures identify different SIFIs and that (2) firm rankings based on systemic risk estimates mirror rankings obtained by sorting firms on market risk or liabilities. One-factor linear models explain between 83% and 100% of the variability of the systemic risk estimates, which indicates that standard systemic risk measures fall short in capturing the multiple facets of systemic risk.
    Keywords: Banking Regulation; Systemically Important Financial Firms; Marginal Expected; Shortfall; SRISK; CoVaR; Systemic vs. Systematic Risk.
    Date: 2012–10–28
  8. By: Michael McAleer (Econometric Institute Erasmus School of Economics Erasmus University Rotterdam and Tinbergen Institute The Netherlands and Institute of Economic Research Kyoto University and Department of Quantitative Economics Complutense University of Madrid); Juan-Angel Jimenez-Martin (Department of Quantitative Economics Complutense University of Madrid); Teodosio Perez-Amaral (Department of Quantitative Economics 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: 2012–11
  9. By: Watanagase, Tarisa (Asian Development Bank Institute)
    Abstract: This paper discusses the relevance of Basel III to Asian emerging markets. It reviews some of the proposed regulations of Basel III in order to evaluate their likely implications for, and their ability to enhance, the stability of the banking and financial system. This is followed by a discussion on the challenges faced by the regulators of Asian emerging markets in effectively managing their financial regulations, given their capacity and institutional constraints. The paper concludes with policy recommendations for Asian emerging markets to strengthen and enhance the stability of their banking and financial systems.
    Keywords: basel iii; asian emerging markets; banking system; financial system; financial regulations
    JEL: E52 G21 G28
    Date: 2012–10–26
  10. By: Kartik Anand; James Chapman; Prasanna Gai;
    Abstract: We examine the financial stability implications of covered bonds. Banks issue covered bonds by encumbering assets on their balance sheet and placing them within a dynamic ring fence. As more assets are encumbered, jittery unsecured creditors may run, leading to a banking crisis. We provide conditions for such a crisis to occur. We examine how different over-the-counter market network structures influence the liquidity of secured funding markets and crisis dynamics. We draw on the framework to consider several policy measures aimed at mitigating systemic risk, including caps on asset encumbrance, global legal entity identifiers, and swaps of good for bad collateral by central banks.
    Keywords: covered bonds, over-the-counter markets, systemic risk, asset encumbrance, legal entity identifiers, velocity of collateral
    JEL: G01 G18 G21
    Date: 2012–10
  11. By: Yi-Hsuan Chen; Wolfgang Karl Härdle; ;
    Abstract: We examine what are common factors that determine systematic credit risk and estimate and interpret the common risk factors. We also compare the contributions of common factors in explaining the changes of credit default swap (CDS) spreads during the pre-crisis, crisis and post-crisis period. Based on the testing result from the common principal components model, this study finds that the eigenstructures across the three subperiods are distinct and the determinants of risk factors differ from three subperiods. Furthermore, we analyze the predictive ability of dynamics in CDS indices changes by dynamic factor models.
    Keywords: credit default swaps; common factors; credit risk
    JEL: C38 G32 E43
    Date: 2012–10

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