Hedge Funds

The key focus areas of this chapter will be the features of hedge funds. The chapter will also do a comparison between mutual funds and hedge funds. Biases that are often prevalent in hedge funds’ databases will be studied. We…

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Correlations and Copulas

Candidate’s objectives: After completing this reading you should be able to: Define correlation and covariance and differentiate between correlation and dependence. Calculate covariance using the EWMA and GARCH(1,1) models. Apply the consistency condition to covariance. Describe the procedure of generating…

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Simulation Methods

Candidate’s objectives: After completing this reading you should be able to: Describe the basic steps to conduct a Monte Carlo simulation. Describe ways to reduce Monte Carlo sampling error. Explain how to use antithetic variate technique to reduce Monte Carlo…

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Volatility

Candidate’s objectives: After completing this reading you should be able to: Define and distinguish between volatility, variance rate, and implied volatility. Describe the power law. Explain how various weighting schemes can be used in estimating volatility. Apply the exponentially weighted…

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Modeling Cycles: MA, AR, and ARMA Models

Candidate’s objectives: After completing this reading you should be able to: Describe the properties of the first-order moving average (MA(1)) process, and distinguish between autoregressive representation and moving average representation. Describe the properties of a general finite-order process of order…

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Charaterizing Cycles

Candidate’s objectives: After completing this reading you should be able to: Define covariance stationary, autocovariance function, autocorrelation function, partial autocorrelation function, and autoregression. Describe the requirements for a series to be covariance stationary. Explain the implications of working with models…

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Modeling and Forecasting Seasonality

Candidate’s objectives: After completing this reading you should be able to: Describe the sources of seasonality and how to deal with it in time series analysis. Explain how to use regression analysis to model seasonality. Explain how to construct an…

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Modeling and Forecasting Trend

Candidate’s objectives: After completing this reading you should be able to: Describe linear and nonlinear trends. Describe trend models to estimate and forecast trends. Compare and evaluate model selection criteria, including mean squared error (MSE), s2, the Akaike information criterion…

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Hypothesis Tests and Confidence Intervals in Multiple Regression

Candidate’s objectives: After completing this reading you should be able to: Construct, apply, and interpret hypothesis tests and confidence intervals for a single coefficient in a multiple regression. Construct, apply, and interpret joint hypothesis tests and confidence intervals for multiple…

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Linear Regression with Multiple Regressors

Candidate’s objectives: After completing this reading you should be able to: Define and interpret omitted variable bias, and describe the methods for addressing this bias. Distinguish between single and multiple regression. Interpret the slope coefficient in a multiple regression. Describe…

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