New Economics Papers
on Market Microstructure
Issue of 2013‒04‒13
four papers chosen by
Thanos Verousis

  1. Identification and Inference Using Event Studies By Gürkaynak, Refet S.; Wright, Jonathan
  2. Short-term GDP forecasting with a mixed frequency dynamic factor model with stochastic volatility By Marcellino, Massimiliano; Porqueddu, Mario; Venditti, Fabrizio
  3. A robust neighborhood truncation approach to estimation of integrated quarticity By Torben G. Andersen; Dobrislav Dobrev; Ernst Schaumburg
  4. Modeling and Estimating Volatility of Options on Standard & Poor’s 500 Index By Boleslaw Borkowski; Monika Krawiec; Yochanan Shachmurove

  1. By: Gürkaynak, Refet S.; Wright, Jonathan
    Abstract: We discuss the use of event studies in macroeconomics and finance, arguing that many important macro-finance questions can only be answered using event studies with high-frequency financial market data. We provide a broad picture of the use of event studies, along with their limitations. As examples, we study financial markets' responses to specific events that help address questions such as the slope of bond demand functions and the efficacy of central bank liquidity programs. We also study the change in financial market responses to news in payrolls and unemployment in response to former Fed Chairman Greenspan's statement that payrolls are more informative.
    Keywords: Bond Markets; Event Study; High-Frequency Data; Identification; TAF
    JEL: E43 E52 E58 G12 G14
    Date: 2013–03
  2. By: Marcellino, Massimiliano; Porqueddu, Mario; Venditti, Fabrizio
    Abstract: In this paper we develop a mixed frequency dynamic factor model featuring stochastic shifts in the volatility of both the latent common factor and the idiosyncratic components. We take a Bayesian perspective and derive a Gibbs sampler to obtain the posterior density of the model parameters. This new tool is then used to investigate business cycle dynamics and for forecasting GDP growth at short-term horizons in the euro area. We discuss three sets of empirical results. First we use the model to evaluate the impact of macroeconomic releases on point and density forecast accuracy and on the width of forecast intervals. Second, we show how our setup allows to make a probabilistic assessment of the contribution of releases to forecast revisions. Third we design a pseudo out of sample forecasting exercise and examine point and density forecast accuracy. In line with findings in the Bayesian Vector Autoregressions (BVAR) literature we find that stochastic volatility contributes to an improvement in density forecast accuracy.
    Keywords: Business cycle; Forecasting; Mixed-frequency data; Nonlinear models; Nowcasting
    JEL: C22 E27 E32
    Date: 2013–02
  3. By: Torben G. Andersen; Dobrislav Dobrev; Ernst Schaumburg
    Abstract: We provide a first in-depth look at robust estimation of integrated quarticity (IQ) based on high frequency data. IQ is the key ingredient enabling inference about volatility and the presence of jumps in financial time series and is thus of considerable interest in applications. We document the significant empirical challenges for IQ estimation posed by commonly encountered data imperfections and set forth three complementary approaches for improving IQ based inference. First, we show that many common deviations from the jump diffusive null can be dealt with by a novel filtering scheme that generalizes truncation of individual returns to truncation of arbitrary functionals on return blocks. Second, we propose a new family of efficient robust neighborhood truncation (RNT) estimators for integrated power variation based on order statistics of a set of unbiased local power variation estimators on a block of returns. Third, we find that ratio-based inference, originally proposed in this context by Barndorff-Nielsen and Shephard (2002), has desirable robustness properties in the face of regularly occurring data imperfections and thus is well suited for empirical applications. We confirm that the proposed filtering scheme and the RNT estimators perform well in our extensive simulation designs and in an application to the individual Dow Jones 30 stocks.
    Date: 2013
  4. By: Boleslaw Borkowski (Department of Econometrics and Statistics, Warsaw University of Life Sciences); Monika Krawiec (Department of Econometrics and Statistics, Warsaw University of Life Sciences); Yochanan Shachmurove (Department of Economics and Business, The City College of the City University of New York)
    Abstract: This paper explores the impact of volatility estimation methods on theoretical option values based upon the Black-Scholes-Merton (BSM) model. Volatility is the only input used in the BSM model that cannot be observed in the market or a priori determined in a contract. Thus, properly calculating volatility is crucial. Two approaches to estimate volatility are implied volatility and historical prices. Iterative techniques are applied, based on daily S&P index options. Additionally, using option data on S&P 500 Index listed on the Chicago Board of Options Exchange, historical volatility can be estimated.
    Keywords: historical volatility; option premium; index options; Black-Scholes-Merton model; Chicago Board of Options Exchange
    JEL: C0 C01 C2 C58 D53 G0 G13 G17
    Date: 2013–02–01

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