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Estimating overnight volatility of asset returns by using the generalized dynamic factor model approach

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Abstract

This paper proposes a new approach to estimate the overnight volatility of an individual stock return. Since markets generally do not trade during the overnight period, measures of realized volatility cannot be computed on a “high-frequency” basis. Some studies have resorted to using the square overnight return as a proxy for the overnight realized volatility, but this measure is typically very noisy. The new estimator of the overnight volatility proposed is obtained using the generalized dynamic factor model. The performance of the new proxy is examined using simulated data. This is found to perform better than the squared overnight return. Empirical analysis of the S&P100 constituents confirms the potential of this proxy.

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References

  • Abramowitz M., Stegun I.: Handbook of Mathematical Functions. Dover, New York (1964)

    Google Scholar 

  • Ahoniemi K., Lanne M.: (2010) Realized volatility and overnight returns, Bank of Finland Research, Discussion Papers 19/2010

  • Andersen T., Bollerslev T.: Answering the skeptics: yes, standard volatility models do provide accurate forecasts. Int. Econ. Rev. 39, 885–905 (1998)

    Article  Google Scholar 

  • Andersen T., Bollerslev T., Diebold X., Ebens H.: The distribution of realized stock return volatility. J. Financ. Econ. 61, 43–76 (2001a)

    Article  Google Scholar 

  • Andersen T., Bollerslev T., Diebold X., Labys P.: The distribution of realized exchange rate volatility. J. Am. Stat. Assoc. 96, 42–55 (2001b)

    Article  Google Scholar 

  • Asai M., McAleer M.: Dynamic asymmetric leverage in stochastic volatility models. Econ. Rev. 24, 317–332 (2005)

    Article  Google Scholar 

  • Barndorff-Nielsen O.E., Shephard N.: Econometric analysis of realized volatility and its use in estimating stochastic volatility models. J. R. Stat. Soc. Ser. B. 64, 253–280 (2002)

    Article  Google Scholar 

  • Basu S., Meckesheimer S.: Automatic outlier detection for time series: an application to sensor data. Knowl. Inf. Syst. 2, 137–154 (2007)

    Article  Google Scholar 

  • Black, F.: Studies of stock market volatility changes. In: Proceedings of the American Statistical Association, Business and Economic Statistics Section, pp. 177–181 (1976)

  • Bollerslev T.: A conditional heteroskedastic time series model for speculative prices and rates of return. Rev. Econ. Stat. 69, 542–547 (1987)

    Article  Google Scholar 

  • Bollerslev T., Tauchen G., Zhou H.: Expected stock returns and variance risk premia. Rev. Financ. Stud. 22, 4463–4492 (2009)

    Article  Google Scholar 

  • Boivin, J., Ng, S.: Are more data always better for factor analysis? NBER Working Paper, p. 9829 (2003)

  • Campbell J.Y., Lettau M., Malkiel B.G., Xu Y.: Have individual stocks become more volatile? An empirical exploration of idiosyncratic risk. J. Finance. 56, 1–43 (2001)

    Article  Google Scholar 

  • Chamberlain G., Rothschild M.: Arbitrage, factor structure and mean-variance analysis in large asset markets. Econometrica 51, 1281–1304 (1983)

    Article  Google Scholar 

  • Chan K., Chockalingam M., Lai K.W.L.: Overnight information and intraday trading behavior: evidence from NYSE cross-listed stocks and their local market information. J. Multinatl. Financ. Manag. 10, 495–509 (2000)

    Article  Google Scholar 

  • Christie A.: The stochastic behavior of common stock variances. J. Financ. Econ. 10, 407–432 (1982)

    Article  Google Scholar 

  • Connor G., Korajczyk R.A., Linton O.: The common and specific components of dynamic volatility. J. Econ. 132, 231–255 (2006)

    Article  Google Scholar 

  • Forni M., Hallin M., Lippi M., Reichlin L.: The generalized dynamic factor model: identification and estimation. Rev. Econ. Stat. 82, 540–554 (2000)

    Article  Google Scholar 

  • Forni M., Hallin M., Lippi M., Reichlin L.: Coincident and leading indicators for the Euro area. Econ. J. 111, 62–85 (2001)

    Article  Google Scholar 

  • Forni M., Hallin M., Lippi M., Reichlin L.: The generalized dynamic factor model consistency and rates. J. Econ. 119, 231–255 (2004)

    Article  Google Scholar 

  • Geweke J.: The dynamic factor analysis of economic time series. In: Aigner, D.J., Goldberger, A.S. (eds.) Latent Variables in Socio-Economic Models, North-Holland, Amsterdam (1977)

    Google Scholar 

  • Hallin M., Liska R.: The generalized dynamic factor model: determining the number of factors. J. Am. Stat. Assoc. 102, 603–617 (2007)

    Article  Google Scholar 

  • Hansen P.R., Lunde A.: A realized variance for the whole day based on intermittent high-frequency data. J. Financ. Econ. 3, 525–554 (2005)

    Google Scholar 

  • Jacquier E., Polson N.G., Rossi P.E.: Bayesian analysis of stochastic volatility models with fat-tails and Correlated Errors. J. Econ. 122, 185–212 (2004)

    Article  Google Scholar 

  • Liesenfeld R., Jung R.: Stochastic volatility models: conditional normality versus heavy-tailed distributions. J. Appl. Econ. 15, 137–160 (2000)

    Article  Google Scholar 

  • Lopez J.A.: Evaluating the predictive accuracy of volatility models. J. Forecast. 20, 87–109 (2001)

    Article  Google Scholar 

  • Martens M.: Measuring and forecasting S&P 500 index-futures volatility using high-frequency data. J. Futur. Mark. 22, 497–518 (2002)

    Article  Google Scholar 

  • Omori Y., Chib S., Shephard N., Nakajima J.: Stochastic volatility with leverage: fast likelihood inference. J. Econ. 140, 425–449 (2007)

    Article  Google Scholar 

  • Patton A.J.: Data-based ranking of realised volatility estimators. J. Econ. 161, 284–303 (2011)

    Article  Google Scholar 

  • Sargent T.J., Sims A.C.: Business cycle modeling without pretending to have too much a priori economic theory. In: Sims, A.C. (ed.) New Methods in Business Research, Federal Reserve Bank of Minneapolis, Minneapolis (1977)

    Google Scholar 

  • Taylor S.: Simulating financial prices. J. Oper. Res. Soc. 6, 567–569 (1989)

    Article  Google Scholar 

  • Taylor S.: Asset Price Dynamics, Volatility, and Prediction. Princeton University Press, Princeton (2005)

    Google Scholar 

  • Tsiakas I.: Overnight information and stochastic volatility: a study of European and US stock exchanges. J. Banking Finance. 32, 251–268 (2008)

    Article  Google Scholar 

  • Yu J.: On leverage in a stochastic volatility model. J. Econ. 127, 165–178 (2005)

    Article  Google Scholar 

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Correspondence to Umberto Triacca.

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The views expressed in the note are those of the authors and do not involve the responsibility of the Bank of Italy.

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Triacca, U., Focker, F. Estimating overnight volatility of asset returns by using the generalized dynamic factor model approach. Decisions Econ Finan 37, 235–254 (2014). https://doi.org/10.1007/s10203-012-0130-x

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