By Ruey S. Tsay
This booklet offers a huge, mature, and systematic creation to present monetary econometric types and their purposes to modeling and prediction of economic time sequence information. It makes use of real-world examples and actual monetary facts during the booklet to use the types and strategies described.The writer starts off with simple features of economic time sequence information earlier than protecting 3 major topics:Analysis and alertness of univariate monetary time seriesThe go back sequence of a number of assetsBayesian inference in finance methodsKey positive factors of the recent version contain extra insurance of recent day subject matters corresponding to arbitrage, pair buying and selling, discovered volatility, and credits danger modeling; a tender transition from S-Plus to R; and extended empirical monetary information sets.The total aim of the e-book is to supply a few wisdom of economic time sequence, introduce a few statistical instruments precious for reading those sequence and achieve adventure in monetary functions of varied econometric equipment.
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Extra info for Analysis of Financial Time Series, Third Edition (Wiley Series in Probability and Statistics)
Thus, the excess kurtosis of a normal random variable is zero. A distribution with positive excess kurtosis is said to have heavy tails, implying that the distribution puts more mass on the tails of its support than a normal distribution does. In practice, this means that a random sample from such a distribution tends to contain more extreme values. Such a distribution is said to be leptokurtic. , a uniform distribution over a ﬁnite interval). Such a distribution is said to be platykurtic. In application, skewness and kurtosis can be estimated by their sample counterparts.
1 Review of Statistical Distributions and Their Moments We brieﬂy review some basic properties of statistical distributions and the moment equations of a random variable. Let R k be the k-dimensional Euclidean space. A point in R k is denoted by x ∈ R k . Consider two random vectors X = (X1 , . . , Xk ) and Y = (Y1 , . . , Yq ) . Let P (X ∈ A, Y ∈ B) be the probability that X is in the subspace A ⊂ R k and Y is in the subspace B ⊂ R q . For most of the cases considered in this book, both random vectors are assumed to be continuous.
However, if the time series rt is normally distributed, then weak stationarity is equivalent to strict stationarity. In this book, we are mainly concerned with weakly stationary series. The covariance γ = Cov(rt , rt− ) is called the lag- autocovariance of rt . It has two important properties: (a) γ0 = Var(rt ) and (b) γ− = γ . The second property holds because Cov(rt , rt−(− ) ) = Cov(rt−(− ) , rt ) = Cov(rt+ , rt ) = Cov(rt1 , rt1 − ), where t1 = t + . In the ﬁnance literature, it is common to assume that an asset return series is weakly stationary.