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It is used to help determine the levered beta and, through this, the optimal capital structure of firms. It was named after Robert Hamada, the Professor of Finance behind the theory. Hamada’s equation relates the beta of a levered firm (a firm financed by both debt and equity) to that of its unlevered (i.e., a firm which has no debt) counterpart.
Even if unavailable directly, the complete and incomplete beta function values can be calculated using functions commonly included in spreadsheet or computer algebra systems. In Microsoft Excel, for example, the complete beta function can be computed with the GammaLn function (or special.gammaln in Python's SciPy package):
In probability theory and statistics, the beta distribution is a family of continuous probability distributions defined on the interval [0, 1] or (0, 1) in terms of two positive parameters, denoted by alpha (α) and beta (β), that appear as exponents of the variable and its complement to 1, respectively, and control the shape of the distribution.
Beta is the hedge ratio of an investment with respect to the stock market. For example, to hedge out the market-risk of a stock with a market beta of 2.0, an investor would short $2,000 in the stock market for every $1,000 invested in the stock. Thus insured, movements of the overall stock market no longer influence the combined position on ...
The Dirac comb of period 2 π, although not strictly a function, is a limiting form of many directional distributions. It is essentially a wrapped Dirac delta function. It represents a discrete probability distribution concentrated at 2 π n — a degenerate distribution — but the notation treats it as if it were a continuous distribution.
Beta regression is a form of regression which is used when the response variable, , takes values within (,) and can be assumed to follow a beta distribution. [1] It is generalisable to variables which takes values in the arbitrary open interval ( a , b ) {\displaystyle (a,b)} through transformations. [ 1 ]
For example, experiments by Tversky and Kahneman showed that the same people who would choose 1 candy bar now over 2 candy bars tomorrow, would choose 2 candy bars 101 days from now over 1 candy bar 100 days from now. (This is inconsistent because if the same question were posed 100 days from now, the person would ostensibly again choose 1 ...
The dual-beta model attempts to differentiate downside risk (risk of loss) from upside risk (gain), both measured in terms of beta with respect to the market and not individual idiosyncratic risk. Mathematically, neither the two betas nor their average needs to be similar to the overall single beta.