Invest Without Emotions
A lot of thick blue squares mean that positive correlations are high. Red squares mean negative correlations are high. Whites are the doldrums.
Relative Strength Spread of a stock is the relative cumulative performance of the stock vs. the CNX 500 index over the same period.
We had discussed how the Relative Strength Spread Index is a coincident indicator of momentum earlier. With a new update, we are publishing charts of relative strengths of individual stocks over multiple lookback periods. This can be found under the ‘Quant’ tab of the equity pages.
Relative Strength Spread is a measure of historical out-performance. By observing the RS-Spread over different lookback periods, investors can get a sense of future direction of momentum. For example, if you look at the RS-Spread of TATAMOTORS, you can observe how the stock lagged the broad market for the better part of the year before suddenly turning around in the last few days.
Now contrast that to BHARATFORG:
Unlike a price or return chart that looks at an individual stock, the RS-Spread chart incorporates information from a broad set of stocks and compares it to the market average. It has more information content than the ‘Relative Momentum’ chart as well.
We compared the total returns from the short-end of the curve to Nifty. Here’s what we found:
Annual returns:
Equity curve:
Drawdowns:
The right place for bonds in a portfolio is for regular income. From a returns perspective, you are better off investing in equities. Bonds are no less volatile when compared to the returns they give, and are mostly correlated with equity volatility.
Probably the reason why banks have been reluctant to pass on rate-cuts is because the yield curve has been flat as a pancake. The difference between short-term and long-term rates are near their historical lows.
GARCH(1,1) is a common approach for modeling volatility. They were developed by Robert Engle to deal with the problem of auto-correlated residuals (which occurs when you have volatility clustering, for example) in time-series regression.
What we did:
We found that modeling based on the previous 500-day VIX closing levels gave us the least prediction errors. The appendix has the charts for other lookback periods.
Note how in some periods, the predicted value (red) is just the previous value.
Values less than zero implies that the model prediction overshoots the actual VIX level the next day.
The model bias towards higher estimation of VIX is made explicit here.
We will integrate this model to our morning ‘Options Daily’ posts so that we get an idea of both the current state of VIX and the expected modeled behavior.
Caveats:
VIX Model vs. Actual across various lookback periods. (pdf)
Volatility Forecasting I: GARCH Models, Rob Reider (pdf)