Correlation Tracking Error
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it indicates how closely a portfolio follows the index to which it is benchmarked. The best measure is the standard deviation of the difference between the portfolio and index returns. Many portfolios are managed to a benchmark, tracking error formula typically an index. Some portfolios are expected to replicate, before trading and other costs, the tracking error information ratio returns of an index exactly (e.g., an index fund), while others are expected to 'actively manage' the portfolio by deviating slightly from the tracking error interpretation index in order to generate active returns. Tracking error is a measure of the deviation from the benchmark; the aforementioned index fund would have a tracking error close to zero, while an actively managed portfolio would normally have annualized tracking error a higher tracking error. Thus the tracking error does not include any risk (return) that is merely a function of the market's movement. In addition to risk (return) from specific stock selection or industry and factor "bets," it can also include risk (return) from market timing decisions. Dividing portfolio active return by portfolio tracking error gives the information ratio, which is a risk adjusted performance measure. Contents 1 Definition 1.1 Formulas 1.2 Interpretation 2 Examples 3
Negative Tracking Error
References 4 External links Definition[edit] If tracking error is measured historically, it is called 'realized' or 'ex post' tracking error. If a model is used to predict tracking error, it is called 'ex ante' tracking error. Ex-post tracking error is more useful for reporting performance, whereas ex-ante tracking error is generally used by portfolio managers to control risk. Various types of ex-ante tracking error models exist, from simple equity models which use beta as a primary determinant to more complicated multi-factor fixed income models. In a factor model of a portfolio, the non-systematic risk (i.e., the standard deviation of the residuals) is called "tracking error" in the investment field. The latter way to compute the tracking error complements the formulas below but results can vary (sometimes by a factor of 2). Formulas[edit] The ex-post tracking error formula is the standard deviation of the active returns, given by: T E = ω = Var ( r p − r b ) = E [ ( r p − r b ) 2 ] − ( E [ r p − r b ] ) 2 {\displaystyle TE=\omega ={\sqrt {\operatorname {Var} (r_{p}-r_{b})}}={\sqrt {{E}[(r_{p}-r_{b})^{2}]-({E}[r_{p}-r_{b}])^{2}}}} where rp−rb is the active return, i.e., the difference between the portfolio return and the benchmark return. Interpretation[edit] An active risk of x per cent would mean that approximately 2/3 of the portfolio’s activ
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Tracking Error Cfa
Leadership Market and Economics Portfolio and Risk Analysis Companies and Earnings Shareholder Distributions ETFs tracking error etf Fixed Income Hedge Funds M&A and Corporate Activism Market Summaries | Product Insight Product Updates & Tips Webcasts Factsheets White tracking error volatility Papers | Events | Press Releases | Subscribe Type Thought Leadership Product Insight Subscribe ✉ Market and Economics Portfolio and Risk Analysis Companies and Earnings Shareholder Distributions ETFs Fixed Income Hedge Funds M&A and Corporate https://en.wikipedia.org/wiki/Tracking_error Activism Market Summaries Category Fact Sheets Tools and Tips Webcasts White Papers How different are the risk model providers, part three: predicted tracking error Jun 19, 2010 In my third and final post on the question of “How different are APT, Barra, and Northfield?” I’ll shift focus to the change in predicted tracking error over time. It is critical to understand whether the portfolio is becoming more risky or http://www.factset.com/insight/blogs/how-different-are-the-risk-model-providers-part-three-predicted-tracking-error reducing risk to become more like the benchmark (risk direction). So, we should want to understand whether APT, Barra, and Northfield suggest comparable change. Our framework is mostly similar to the previous blog posts (part one, part two). First, we use the same 300 U.S. equity mutual funds, focusing on the same three U.S. long term equity risk models. Our change over time will consider the six month and twelve month periods ending on 12/31/2008. So, our starting point is 2,700 predicted tracking errors (300 portfolios x 3 risk models x 3 points in time). Comparing the change across models, we are most interested in both correlation and covariance. The correlation coefficient reveals the strength and direction of a linear relationship between the tracking errors for two models. We see consistently positive correlations across models, periods, and styles. For the most part, the tracking error changes between the risk models are largely correlated. Clearly, the Small Cap Growth and Small Cap Value correlations are smaller. Also, Model X is less correlated with the other two models. Hand in hand with correlation, we should consider the covariance. This metric will tell us how likely one tracking error change is to be unexpectedly large when another mode
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