Notes on the hydrology of the Waikato River

The catchment area of the Waikato River is 5,500 square miles. If its source is accepted as being the Upper Waikato, then its distance to the sea at Port Waikato including its journey through Lake Taupo is 266 miles. It rises, together with the Whangaehu, the Rangitikei and the Wanganui, between the volcanic region of Ruapehu 9,000 ft. above sea level and the Kaimanawa Ranges 5,000 ft. above sea level. The river flows northwards for 34 miles into Lake Taupo, losing its identity into the Tongariro for the last 26 miles to the lake. It emerges from Lake Taupo resuming its proper name and, still flowing northwards, passes for more than 100 miles through a series of lakes formed by hydroelectric dams to Cambridge. From here it continues through a deeply incised channel to Ngaruawahia where it is joined by its major tributary, the Waipa River. From Ngaruawahia to the mouth, a distance of 60 miles, shallow lakes and peat swamps predominate on both sides of the river, many of them protected and drained and developed into rich dairy farms. From Mercer, 35 miles downstream of Ngaruawahia, where slight tidal effects are discernable at low flows, the river changes its general northerly direction to a westerly one and, still 9 miles from the mouth, enters the delta. Here it is fragmented into many channels before emptying into the broad expanse of Maioro Bay and finally emerges by two fairly narrow channels into the sea on the west coast, 25 miles south of Manukau Heads

Statistical Inference on Stochastic Dominance Efficiency. Do Omitted Risk Factors Explain the Size and Book-to-Market Effects?

This paper discusses statistical inference on the second-orderstochastic dominance (SSD) efficiency of a given portfolio relative toall portfolios formed from a set of assets. We derive the asymptoticsampling distribution of the Post test statistic for SSD efficiency.Unfortunately, a test procedure based on this distribution involveslow power in small samples. Bootstrapping is a more powerful approachto sampling error. We use the bootstrap to test if the Fama and Frenchvalue-weighted market portfolio is SSD efficient relative to benchmarkportfolios formed on market capitalization and book-tomarket equityratio. During the late 1970s and during the 1980s, the marketportfolio is significantly SSD inefficient, even if we use samples ofonly 60 monthly observations. This suggests that the size andbook-to-market effects cannot be explained by omitted risk factorslike higher-order central moments or lower partial moments.market efficiency;asset pricing;stochastic dominance;size and book-to-market effects;statistical inference

Testing for Stochastic Dominance with Diversification Possibilities

We derive empirical tests for stochastic dominance that allow for diversification betweenchoice alternatives. The tests can be computed using straightforward linearprogramming. Bootstrapping techniques and asymptotic distribution theory canapproximate the sampling properties of the test results and allow for statistical inference.Our results could provide a stimulus to the further proliferation of stochastic dominancefor the problem of portfolio selection and evaluation (as well as other choice problemsunder uncertainty that involve diversification possibilities). An empirical application forUS stock market data illustrates our approach.stochastic dominance;portfolio selection;linear programming;portfolio diversification;portfolio evaluation

Spanning and Intersection: a stochastic dominance approach

We propose linear programming tests for spanning and intersection based on stochasticdominance rather than mean-variance analysis. An empirical application investigates thediversification benefits to US investors from emerging equity markets.stochastic dominance;linear programming;emerging markets;intersection;spanning

Testing for Third-Order Stochastic Dominance with Diversification Possibilities

We derive an empirical test for third-order stochastic dominance that allows fordiversification between choice alternatives. The test can be computed usingstraightforward linear programming. Bootstrapping techniques and asymptoticdistribution theory can approximate the sampling properties of the test results and allowfor statistical inference. Our approach is illustrated using real-life US stock market data.efficiency;stochastic dominance;portfolio selection;linear programming;portfolio evaluation

A Stochastic Dominance Approach to Spanning

We develop a Stochastic Dominance methodology to analyze if new assets expand theinvestment possibilities for rational nonsatiable and risk-averse investors. This methodologyavoids the simplifying assumptions underlying the traditional mean-variance approach tospanning. The methodology is applied to analyze the stock market behavior of small firms in themonth of January. Our findings suggest that the previously observed January effect isremarkably robust with respect to simplifying assumptions regarding the return distribution.stochastic dominance;portfolio selection;linear programming;portfolio evaluation;spanning

Asset prices and omitted moments; A stochastic dominance analysis of market efficiency

We analyze if the value-weighted stock market portfolio is second-order stochastic dominance (SSD) efficient relative to benchmark portfolios formed on market capitalization, book-to-market equity ratio and industry classification. During the period from the mid-1970s to the late 1980s, the market portfolio is significantly mean-variance inefficient. During this period, the market portfolio generally also is significantly SSD inefficient. This suggests that mean-variance inefficiency cannot be explained by omitted return moments like higher-order central moments or lower partial moments.market efficiency;asset pricing;stochastic dominance;size and book-to-market effects;statistical inference