Standardized time series L(sub p)-norm variance estimators for simulations

Article Abstract:

A set of standardized time series estimators for the variance parameter sigma(super 2) = lim(sub n->infinity) n Var(Y(sub n)) is shown. These estimators are very similar to L(sub p) norms of Brownian bridges. They generalize several formerly analyzed estimators of sigma(super 2), namely, the unweighted area estimator of L.W. Schruben and the unweighted CvM estimator of D. Goldsman et.al. The proposed estimators are all asymptotically unbiased for sigma(super 2) but their finite-sample bias cannot be ignored. On the positive side, the new estimators result into substantial asymptotic variance decreases in contrast to the unweighted area estimator. Hence, the new estimators are more efficient than the previous estimators when the sample size is large enough.

Research and Development in the Physical, Engineering, and Life Sciences, Statistics, Methods, Analysis of variance, Statistics (Mathematics), Time-series analysis, Time series analysis

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Characterizations of optimal portfolios by univariate and multivariate risk aversion

Article Abstract:

Rubenstein's measures of risk aversion are shown to yield the characterizations of wealth effects and risk aversion for a portfolio selection model with two risky investments that have bivariate normally distributed returns. It is also shown that a matrix measure of risk aversion can be used to characterize optimal portfolios for investors whose preferences are represented by multi-attributed utility functions and when and returns on different investments have a joint, normal distribution.

Analysis, Portfolio management, Risk management

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