The world price of covariance risk

Article Abstract:

In a financially integrated global market, the conditionally expected return on a portfolio of securities from a particular country is determined by the country's world risk exposure. This paper measures the conditional risk of 17 countries. The reward per unit of risk is the world price of covariance risk. Although the tests provide evidence on the conditional mean variance efficiency of the benchmark portfolio, the results show that countries' risk exposures help explain differences in performance. Evidence is also presented which indicates that these risk exposures change through time and that the world price of covariance risk is not constant. (Reprinted by permission of the publisher.)

Risk assessment, International aspects, Portfolio management, Securities

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On the inverse of the covariance matrix in portfolio analysis

Article Abstract:

The goal of this paper is the derivation and application of a direct characterization of the inverse of the covariance matrix central to portfolio analysis. Such a characterization, in terms of a few primitive constructs, provides the basis for new and illuminating expressions for key concepts as the optimal holding of a given risky asset and the slope of the risk-return efficiency frontier faced by the individual investor. The building blocks of the inverse turn to be the regression coefficients and residual variance obtained by regressing the asset's excess return on the set of excess returns for all other risky assets. (Reprinted by permission of the publisher.)

Analysis, Investment analysis, Securities analysis

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Statistical properties of the roll serial covariance bid/ask spread estimator

Article Abstract:

Exact small sample population moments of the standard serial covariance and variance estimators are derived under the assumptions of the Roll bid/ask spread model. Noise explains why serial covariance estimates are often positive in annual samples of daily and weekly returns. Small sample estimator bias partially explains why weekly estimates are more biased by Jensen's inequality. The French-Roll adjusted variance estimator is unbiased but noisy. Empirical tests confirm the major implications. (Reprinted by permission of the publisher.)

Research, Stocks, Statistics, Statistics (Data)

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