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Within the credit correlation modeling framework, a fairly new correlation approach is top–down modeling. Here the evolution of the portfolio intensity distribution is derived directly, i.e. abstracting from the default intensities of individual entities. Top-down models are typically applied in practice if:
Modern portfolio theory (MPT), or mean-variance analysis, is a mathematical framework for assembling a portfolio of assets such that the expected return is maximized for a given level of risk. It is a formalization and extension of diversification in investing, the idea that owning different kinds of financial assets is less risky than owning ...
In finance, correlation trading is a strategy in which the investor gets exposure to the average correlation of an index.. The key to correlation trading is being able to predict when future realized correlation amongst the stocks of a particular index will be greater or less than the "implied" correlation level derived from derivatives on the index and its single stocks.
It consists of essentially all shares and securities in the capital market (either long or short). The Market Portfolio would not include a specific security if the correlation between the portfolio and the security is zero with negative return (gambling), or if the correlation is one (whichever has lower return would not warrant investment).
Portfolio optimization is the process of selecting an optimal portfolio (asset distribution), out of a set of considered portfolios, according to some objective.The objective typically maximizes factors such as expected return, and minimizes costs like financial risk, resulting in a multi-objective optimization problem.
Correlation parity" is an extension of risk parity, and is the solution whereby each asset in a portfolio has an equal correlation with the portfolio, and is therefore the "most diversified portfolio". Risk parity is the special case of correlation parity when all pair-wise correlations are equal. [9]
For example, if the stock market went up by 20% in a given year, and a manager had a portfolio with a market-beta of 2.0, this portfolio should have returned 40% in the absence of specific stock picking skills. This is measured by the alpha in the market-model, holding beta constant. Occasionally, other betas than market-betas are used.
Under the assumption of normality of returns, an active risk of x per cent would mean that approximately 2/3 of the portfolio's active returns (one standard deviation from the mean) can be expected to fall between +x and -x per cent of the mean excess return and about 95% of the portfolio's active returns (two standard deviations from the mean) can be expected to fall between +2x and -2x per ...