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Some Techniques for ARCH Financial Time Series

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Statistical Inference for Financial Engineering

Part of the book series: SpringerBriefs in Statistics ((BRIEFSSTATIST))

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Abstract

This chapter introduces two techniques which can be utilized in study of financial risks. The first one is the method called Quantile Regression (QR), which can be used to analyze the conditional quantile of financial assets. There, by means of rank-based semiparametrics, we provide the statistically efficient inference under the autoregressive conditional heteroskedasticity (ARCH). The second technique, the realized volatility, estimates the conditional variance, or “volatility” of financial assets. Revealing the fact that its inference can be greatly affected by the existence of additional noize called market microstructure, we introduce and study the asymptotics of some appropriate estimator under the microstructure with ARCH-dependent structure.

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Authors and Affiliations

  1. Department of Applied Mathematics, Waseda University, Tokyo, Japan

    Masanobu Taniguchi

  2. Faculty of Economics, Wakayama University, Wakayama, Japan

    Tomoyuki Amano

  3. School of Business Administration, Faculty of Urban Liberal Arts, Tokyo Metropolitan University, Tokyo, Japan

    Hiroaki Ogata

  4. School of International Liberal Studies, Waseda University, Tokyo, Japan

    Hiroyuki Taniai

Authors
  1. Masanobu Taniguchi
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  2. Tomoyuki Amano
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  3. Hiroaki Ogata
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  4. Hiroyuki Taniai
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Correspondence to Masanobu Taniguchi .

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Taniguchi, M., Amano, T., Ogata, H., Taniai, H. (2014). Some Techniques for ARCH Financial Time Series. In: Statistical Inference for Financial Engineering. SpringerBriefs in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-03497-3_4

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