New PDF release: Calcolo

By Flavio Sartoretto

ISBN-10: 1121679315

ISBN-13: 9781121679313

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12) is The decision rule is as follows. Reject the null hypothesis at the α significance level, if |t| > Zα/2, where Zα/2 is the upper 100(α/2)th quantile of the standard normal distribution. Alternatively, one can compute the p-value of the test statistic t and reject Ho if and only if the p-value is less than α. Similarly, one can test the excess kurtosis of the return series using the hypotheses Ho: K(r) − 3 = 0 versus Ha: K(r) − 3 ≠ 0. The test statistic is which is asymptotically a standard normal random variable.

In forecasting using models built for log returns). Alternatively, let m1 and m2 be the mean and variance of the simple return Rt, which is lognormally distributed. Then the mean and variance of the corresponding log return rt are Because the sum of a finite number of iid normal random variables is normal, rt[k] is also normally distributed under the normal assumption for {rt}. In addition, there is no lower bound for rt, and the lower bound for Rt is satisfied using 1 + Rt = exp(rt). However, the lognormal assumption is not consistent with all the properties of historical stock returns.

To cover various multivariate volatility models in Chapter 10. 9. To add an effective MCMC method for estimating stochastic volatility models in Chapter 12. The revision benefits greatly from constructive comments of colleagues, friends, and many readers on the first edition. I am indebted to them all. In particular, I thank J. C. Artigas, Spencer Graves, Chung-Ming Kuan, Henry Lin, Daniel Peña, Jeff Russell, Michael Steele, George Tiao, Mark Wohar, Eric Zivot, and students of my MBA classes on financial time series for their comments and discussions, and Rosalyn Farkas, production editor, at John Wiley.

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Calcolo by Flavio Sartoretto


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