National Repository of Grey Literature 5 records found  Search took 0.00 seconds. 
Log-optimal investment
Král, Stanislav ; Dostál, Petr (advisor) ; Večeř, Jan (referee)
1. Abstrakt Suppose we have a capital, which we will redistribute into investment op- portunities. The financial valuation of these investments will be a sequence of independent, identically distributed random vectors that acquire finite amount of values. We will have full knowledge of the entire history of these valuations before each investment. It turns out that if our strategy is to always maximizes the mean value of the logarithm of the investment value, denoted by Λ∗ , then this strategy is asymptotically the best one possible. If strategy Λ is not asymptotically close to Λ∗ and if x goes to infinity, then the mean of the time we earn atleast x using Λ∗ is infinitely smaller than the time if we used Λ. We also earn infinitely times more money using the strategy Λ∗ . 1
Log-optimal investment
Král, Stanislav ; Dostál, Petr (advisor) ; Večeř, Jan (referee)
Suppose we have capital, which we will redistribute into investment oppor- tunities. The financial valuation of these investments will form a sequence of independent, identically distributed random vectors taking values in some clo- sed, positive interval. We will have full knowledge of the entire history of these valuations before each investment. It turns out that if our strategy is to always maximize the mean value of the logarithm return on these investments, then this strategy is in a sense asymptotically optimal. 1
Log-optimal investment
Král, Stanislav ; Dostál, Petr (advisor) ; Večeř, Jan (referee)
1. Abstrakt Suppose we have a capital, which we will redistribute into investment op- portunities. The financial valuation of these investments will be a sequence of independent, identically distributed random vectors that acquire finite amount of values. We will have full knowledge of the entire history of these valuations before each investment. It turns out that if our strategy is to always maximizes the mean value of the logarithm of the investment value, denoted by Λ∗ , then this strategy is asymptotically the best one possible. If strategy Λ is not asymptotically close to Λ∗ and if x goes to infinity, then the mean of the time we earn atleast x using Λ∗ is infinitely smaller than the time if we used Λ. We also earn infinitely times more money using the strategy Λ∗ . 1
Divergence mezi modely a daty pri hypotetickém a empirickém kvantování
Vajda, Igor ; van der Meulen, Edward
It is shown that the hypothetical quantization generates classical Pearson-type statistical criteria while the empirical quantization generates criteria asymptotically equivalent to the classical spacings-type statistical criteria. Asymptotic properties of the empirically generated criteria are derived and optimality of the quadratic criterion is proved.
Testy dobré shody na základě pozorování kvantových pomocí teoretických a výběrových kvantilů
Vajda, Igor ; van der Meulen, E.
The research report studies goodnes-of-fit statistics defined by means of divergences, in particular power divergences, between hypothetical and empirical distributions quantized by means of hypothetical and empirical kvantiles taken as the quantization cutpoints. New asymptotic results and new relations to the classical goodness-of-fit statistcs are found.

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