SmoothingIndex {PerformanceAnalytics} R Documentation

## calculate Normalized Getmansky Smoothing Index

### Description

Proposed by Getmansky et al to provide a normalized measure of "liquidity risk."

### Usage

```SmoothingIndex(R, neg.thetas = FALSE, MAorder = 2, verbose=FALSE, ...)
```

### Arguments

 `R` an xts, vector, matrix, data frame, timeSeries or zoo object of asset returns `neg.thetas` if FALSE, function removes negative coefficients (thetas) when calculating the index `MAorder` specify the number of periods used to calculate the moving average, defaults to 2 `verbose` if TRUE, return a list containing the Thetas in addition to the smoothing index/ `...` any other passthru parameters

### Details

To measure the effects of smoothing, Getmansky, Lo, et al (2004) define a "smoothing profile" as a vector of coefficients for an MLE fit on returns using a two-period moving-average process.

The moving-average process of order k=2 (specified using `MAorder`) gives R_t = theta_{0} R_{t} + theta_1 R_{t -1} + theta_2 R_{t-2}, under the constraint that the sum of the coefficients is equal to 1. In R, the `arima` function allows us to create an MA(2) model using an "ARIMA(p,d,q)" model, where p is the number of autoregressive terms (AR), d is the degree of differencing, and q is the number of lagged forecast errors (MA) in the prediction equation. The `order` parameter allows us to specify the three components (p, d, q) as an argument, e.g., `order = c(0, 0, 2)`. The `method` specifies how to fit the model, in this case using maximum likelihood estimation (MLE) in a fashion similar to the estimation of standard moving-average time series models, using:

`arima(ra, order=c(0,0,2), method="ML", transform.pars=TRUE, include.mean=FALSE)`

`include.mean`: Getmansky, et al. (2004) p 555 "By applying the above procedure to observed de-meaned returns...", so we set that parameter to 'FALSE'.

`transform.pars`: ibid, "we impose the additional restriction that the estimated MA(k) process be invertible," so we set the parameter to 'TRUE'.

The coefficients, theta_{j}, are then normalized to sum to interpreted as a "weighted average of the fund's true returns over the most recent k + 1 periods, including the current period."

If these weights are disproportionately centered on a small number of lags, relatively little serial correlation will be induced. However, if the weights are evenly distributed among many lags, this would show higher serial correlation.

The paper notes that because theta_j in [0, 1], xi is also confined to the unit interval, and is minimized when all the theta_j's are identical. That implies a value of 1/(k + 1) for xi, and a maximum value of xi = 1 when one coefficient is 1 and the rest are 0. In the context of smoothed returns, a lower value of xi implies more smoothing, and the upper bound of 1 implies no smoothing.

The "smoothing index," represented as xi, is calculated the same way the Herfindahl index. The Herfindal measure is well known in the industrial organization literature as a measure of the concentration of firms in a given industry where y_j represents the market share of firm j.

This method (as well as the implementation described in the paper), does not enforce theta_j in [0, 1], so xi is not limited to that range either. All we can say is that lower values are "less liquid" and higher values are "more liquid" or mis-specified. In this function, setting the parameter neg.thetas = FALSE does enforce the limitation, eliminating negative autocorrelation coefficients from the calculation (the papers below do not make an economic case for eliminating negative autocorrelation, however).

Interpretation of the resulting value is difficult. All we can say is that lower values appear to have autocorrelation structure like we might expect of "less liquid" instruments. Higher values appear "more liquid" or are poorly fit or mis-specified.

### Value

a value ranging from 0 to 1 (enforced only when neg.thetas = FALSE)

### Acknowledgments

Thanks to Dr. Stefan Albrecht, CFA, for invaluable input.

Peter Carl

### References

Chan, Nicholas, Mila Getmansky, Shane M. Haas, and Andrew W. Lo. 2005. Systemic Risk and Hedge Funds. NBER Working Paper Series (11200). Getmansky, Mila, Andrew W. Lo, and Igor Makarov. 2004. An Econometric Model of Serial Correlation and Illiquidity in Hedge Fund Returns. Journal of Financial Economics (74): 529-609.

### Examples

```data(managers)
data(edhec)
SmoothingIndex(managers[,1,drop=FALSE])
SmoothingIndex(managers[,1:8])
SmoothingIndex(edhec)
```

[Package PerformanceAnalytics version 0.9.9-5 Index]