nep-ets New Economics Papers
on Econometric Time Series
Issue of 2012‒01‒10
four papers chosen by
Yong Yin
SUNY at Buffalo

  1. What we can learn from pricing 139,879 Individual Stock Options By Lars Stentoft
  2. Risk and Return: Long-Run Relationships, Fractional Cointegration, and Return Predictability By Tim Bollerslev; Daniela Osterrieder; Natalia Sizova; George Tauchen
  3. Testing for Granger causality in a system of more than two variables By Theologos Pantelidis
  4. Panel-CADF Testing with R: Panel Unit Root Tests Made Easy By Lupi, Claudio

  1. By: Lars Stentoft (HEC Montréal, CIRANO, CIRPEÉ, and CREATES)
    Abstract: The GARCH framework has been used for option pricing with quite some success. While the initial work assumed conditional Gaussian innovations, recent contributions relax this assumption and allow for more flexible parametric specifications of the underlying distribution. However, until now the empirical applications have been limited to index options or options on only a few stocks and this using only few potential distributions and variance specififications. In this paper we test the GARCH framework on 30 stocks in the Dow Jones Industrial Average using two classical volatility specififications and 7 different underlying distributions. Our results provide clear support for using an asymmetric volatility specifification together with non-Gaussian distribution, particularly of the Normal Inverse Gaussian type, and statistical tests show that this model is most frequently among the set of best performing models.
    Keywords: American options, GARCH models, Model Confidence Set, Simulation.
    JEL: C22 C53 G13
    Date: 2011–12–21
  2. By: Tim Bollerslev (Duke University and CREATES); Daniela Osterrieder (Aarhus University and CREATES); Natalia Sizova (Rice University); George Tauchen (Duke University and CREATES)
    Abstract: The dynamic dependencies in financial market volatility are generally well described by a long-memory fractionally integrated process. At the same time, the volatility risk premium, defined as the difference between the ex-post realized volatility and the market’s ex-ante expectation thereof, tends to be much less persistent and well described by a short-memory process. Using newly available intraday data for the S&P 500 and the VIX volatility index, coupled with frequency domain inference procedures that allow us to focus on specific parts of the spectra, we show that the existing empirical evidence based on daily and coarser sampled data carries over to the high-frequency setting. Guided by these empirical findings, we formulate and estimate a fractionally cointegrated VAR model for the two high-frequency volatility series and the corresponding high-frequency S&P 500 returns. Consistent with the implications from a stylized equilibrium model that directly links the realized and expected volatilities to returns, we show that the equilibrium variance risk premium estimated with the intraday data within the fractionally cointegrated system results in non-trivial return predictability over longer interdaily and monthly horizons. These results in turn suggest that much of the existing literature seeking to establish a risk-return tradeoff relationship between expected returns and expected volatilities may be misguided, and that the variance risk premium provides a much better proxy for the true economic uncertainty that is being rewarded by the market.
    Keywords: High-frequency data, realized volatility, options implied volatility, variance risk premium, fractional integration, long-memory, fractional cointegration, equilibrium asset pricing, return predictability.
    JEL: C22 C32 C51 C52 G12 G13 G14
    Date: 2011–12–21
  3. By: Theologos Pantelidis (Department of Economics, University of Macedonia)
    Abstract: This paper provides useful guidelines to practitioners who investigate Granger causality within a system of more than two variables by means of the two-step procedure proposed by Cheung and Ng (Journal of Econometrics, 1996) and modified by Hong (Journal of Econometrics, 2001). First, a theoretical example highlights cases that can mislead the researcher into reporting false causal relations between the variables under scrutiny. Then, the size of the problem is revealed by means of Monte Carlo simulations. Finally, an empirical application that investigates causality-in-mean among six major European stock markets, illustrates the proper procedure to follow for correct inference.
    Keywords: Causality-in-mean; causality-in-variance; misspecification; simulation.
    JEL: C14 C22
    Date: 2012–01
  4. By: Lupi, Claudio
    Abstract: This paper presents the R implementation of the panel covariate augmented Dickey-Fuller (panel-CADF) test proposed in Costantini and Lupi (2011), as well as the implementation of the tests advocated in Choi (2001) and Demetrescu, Hassler, and Tarcolea (2006). A panel-CADF extension of the test suggested in Hanck (2008) is also discussed and its size and power properties are investigated via Monte Carlo analysis. The simulation results show that the panel-CADF tests have interesting properties in terms of size and power. The R implementation illustrated here is part of the ongoing work on a new R package named punitroots (Kleiber and Lupi 2011).
    Keywords: panel data, unit root, R
    JEL: C33 C12
    Date: 2011–12–29

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