By Masanobu Taniguchi
This monograph presents the basics of statistical inference for monetary engineering and covers a few chosen equipment compatible for studying monetary time sequence facts. in an effort to describe the particular monetary info, a variety of stochastic techniques, e.g. non-Gaussian linear tactics, non-linear approaches, long-memory strategies, in the community desk bound tactics and so on. are brought and their optimum estimation is taken into account to boot. This publication additionally comprises numerous statistical methods, e.g., discriminant research, the empirical chance procedure, keep an eye on variate approach, quantile regression, discovered volatility etc., that have been lately built and are thought of to be strong instruments for examining the monetary information, setting up a brand new bridge among time sequence and monetary engineering.
This e-book is definitely desirable as a certified reference ebook on finance, data and statistical monetary engineering. Readers are anticipated to have an undergraduate-level wisdom of statistics.
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Asymptotic Theory of Statistical Inference for Time Series. : Nonparametric approach for non-Gaussian vector stationary processes. J. Multivariate Anal. : Estimation in nonlinear time series models. Stoch. Process. Appl. : Asymptotic Statistics. : Discriminant analysis for stationary vector time series. J. Time Ser. Anal. 15, 117–126 (1994) Chapter 2 Empirical Likelihood Approaches for Financial Returns Abstract We deal with an empirical likelihood and apply it to several financial problems. Empirical likelihood is one of the nonparametric methods of statistical inference.
N}. The estimator is the solution to a saddle point problem θˆ GEL = arg min sup θ ∞ρ n ξ ∞τˆ n (θ ) t=1 λ ξ ≥ m(θt ; θ ) . The empirical likelihood (EL) estimator of Qin and Lawless (1994), the exponential tilting (ET) estimator of Kitamura and Stutzer (1997) and the continuous updating estimator (CUE) of Hansen et al. (1996) are special cases with λ(y) = log(1 − y), λ(y) = −e y and λ(y) = −(1 + y)2 /2, respectively. Let Ω = E[m(θt ; θ 0 )m(θt ; σ0 )≥ ]. The following assumptions and theorems are frequency domain version of those in Newey and Smith (2004), and are found in Ogata (2012).
Plann. Infer. : A central limit theorem for stationary processes and the parameter estimation of linear processes. Ann. Stat. 10, 132–153 (1982). Correction: 21, 1115–1117 (1993). : Some aspects of asymptotic theory with applications to time series models. : Discriminant analysis for non-Gaussian vector stationary processes. J. Nonparametric Stat. : Discrimination and clustering for multivariate time series. J. Am. Stat. Assoc. : Asset Pricing: Discrete Time Approach. : Statistical analysis for multiplicatively modulated nonlinear autoregressive model and its applications to electrophysiological signal analysis in humans.