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Statistical Inference for Financial Engineering

Part of the SpringerBriefs in Statistics Book Series

DOI: 10.1007/978-3-319-03497-3

ISBN: 978-3-319-03496-6

Publisher: Springer International Publishing - Springer Journals

Mar 27, 2014

Available

Features of Financial Data

Taniguchi, Masanobu; Amano, Tomoyuki; Ogata, Hiroaki; Taniai, Hiroyuki

Pages: 1 –39

[This chapter discusses actual features of financial time series data, and how to model them statistically. Because the mechanism of financial market is obviously complicated, modeling for financial time series is difficult. For this, first, we look at some empirical characteristics of financial...
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Empirical Likelihood Approaches for Financial Returns

Taniguchi, Masanobu; Amano, Tomoyuki; Ogata, Hiroaki; Taniai, Hiroyuki

Pages: 41 –64

[We deal with an empirical likelihood and apply it to several financial problems. Empirical likelihood is one of the nonparametric methods of statistical inference. It allows us to use likelihood methods although we do not assume that the data comes from a known family. Consequently, the...
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Various Methods for Financial Engineering

Taniguchi, Masanobu; Amano, Tomoyuki; Ogata, Hiroaki; Taniai, Hiroyuki

Pages: 65 –83

[Various statistical methods have been introduced to many application fields. Such methods are often designed for standard settings, i.e., i.i.d. cases, regular model etc. However, financial data are usually dependent and have complicated features (see Chap. 1). In this chapter, we state various...
Available

Some Techniques for ARCH Financial Time Series

Taniguchi, Masanobu; Amano, Tomoyuki; Ogata, Hiroaki; Taniai, Hiroyuki

Pages: 85 –116

[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...

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