fitTsfmMT: Fit a market timing time series factor model
In sangeeuw/factorAnalytics_Avinash: Factor Analytics

Description Usage Arguments Details Value Author(s) References See Also Examples

This is a wrapper function to fit a market timing time series factor model for one or more asset returns or excess returns using time series regression. Users can choose between ordinary least squares-LS, discounted least squares-DLS (or) robust regression. An object of class "tsfm" is returned.

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fitTsfmMT(asset.names, mkt.name, rf.name = NULL, data = data,
  fit.method = c("LS", "DLS", "Robust"), control = fitTsfm.control(...),
  ...)

`asset.names`	vector containing names of assets, whose returns or excess returns are the dependent variable.
`mkt.name`	name of the column for market returns (required).
`rf.name`	name of the column of risk free rate variable to calculate excess returns for all assets (in `asset.names`) and the market factor (in `mkt.name`). Default is NULL, and no action is taken.
`data`	vector, matrix, data.frame, xts, timeSeries or zoo object containing column(s) named in `asset.names`, `factor.names` and optionally, `mkt.name` and `rf.name`.
`fit.method`	the estimation method, one of "LS", "DLS" or "Robust". See details. Default is "LS".
`control`	list of control parameters passed to `fitTsfm`. Refer to `fitTsfm.control` for details.
`...`	arguments passed to `fitTsfm.control`

Market timing accounts for the price movement of the general stock market relative to fixed income securities. A market-timing factor is added to the time series regression, following Henriksson & Merton (1981). Here, we use down.market = max(0, R_f-R_m), where Rm is the (excess) return on the market. The coefficient of this down-market factor can be interpreted as the number of "free" put options on the market provided by the manager's market-timings skills.

Similar to fitTsfm, fitTsfmMT also returns an object of class "tsfm", for which print, plot, predict and summary methods exist. The generic accessor functions coef, fitted, residuals and fmCov can be applied as well.

An object of class "tsfm" is a list containing the following components:

`asset.fit`	list of fitted objects for each asset. Each object is of class `lm` if `fit.method="LS" or "DLS"`, class `lmRob` if the `fit.method="Robust"`.
`alpha`	length-N vector of estimated alphas.
`beta`	N x 2 matrix of estimated betas.
`r2`	length-N vector of R-squared values.
`resid.sd`	length-N vector of residual standard deviations.
`call`	the matched function call.
`data`	xts data object containing the asset(s) and factor(s) returns.
`asset.names`	asset.names as input.
`factor.names`	vector containing the names of the market-timing factor and the market factor
`mkt.name`	mkt.name as input
`fit.method`	fit.method as input.

Where N is the number of assets and T is the number of time periods.

Yi-An Chen, Sangeetha Srinivasan.

Christopherson, J. A., Carino, D. R., & Ferson, W. E. (2009). Portfolio performance measurement and benchmarking. McGraw Hill Professional. pp.127-133

Henriksson, R. D., & Merton, R. C. (1981). On market timing and investment performance. II. Statistical procedures for evaluating forecasting skills. Journal of business, 513-533.

Treynor, J., & Mazuy, K. (1966). Can mutual funds outguess the market. Harvard business review, 44(4), 131-136.

The original time series factor model fitting function fitTsfm and related methods.

# load data from the database
data(managers)

# example: Market-timing time series factor model with LS fit
fit <- fitTsfmMT(asset.names=colnames(managers[,(1:6)]), mkt.name="SP500.TR",
                 rf.name="US.3m.TR", data=managers)
summary(fit)