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)
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:
- Picked the best fit ARIMA(p,d,q) model for historical VIX over different look back periods
- Created a GARCH(1,1) model based on ARMA(p,q)
- Predicted t+1 VIX
500-day lookback
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.
Prediction vs. Actual
Note how in some periods, the predicted value (red) is just the previous value.
Prediction error
Values less than zero implies that the model prediction overshoots the actual VIX level the next day.
Prediction vs. Actual Density Plot
The model bias towards higher estimation of VIX is made explicit here.
Next steps
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:
- The 500-day lookback is purely empirical. Maybe some other look-back period that we have not tested would have been ideal to model. We will never know.
- Only the known history can be modeled. The outputs should be used along with market determined proxies of expected volatility.
- There is always a probability distribution around a predicted value. We will publish this in our daily posts.
Appendix
VIX Model vs. Actual across various lookback periods. (pdf)
Volatility Forecasting I: GARCH Models, Rob Reider (pdf)
Equities
MINTs | |
---|---|
JCI(IDN) | +8.65% |
INMEX(MEX) | +4.12% |
NGSEINDX(NGA) | -3.37% |
XU030(TUR) | +6.45% |
BRICS | |
---|---|
IBOV(BRA) | +8.88% |
SHCOMP(CHN) | +4.27% |
NIFTY(IND) | +3.00% |
INDEXCF(RUS) | +6.24% |
TOP40(ZAF) | +5.65% |
Commodities
Energy | |
---|---|
Brent Crude Oil | +9.37% |
Natural Gas | +4.09% |
Heating Oil | +3.53% |
Ethanol | -2.34% |
RBOB Gasoline | +3.14% |
WTI Crude Oil | +9.25% |
Metals | |
---|---|
Copper | +4.76% |
Silver 5000oz | +8.97% |
Palladium | +5.16% |
Platinum | +8.59% |
Gold 100oz | +3.85% |
Currencies
MINTs | |
---|---|
USDIDR(IDN) | -8.85% |
USDMXN(MEX) | -2.69% |
USDNGN(NGA) | +0.43% |
USDTRY(TUR) | -3.89% |
BRICS | |
---|---|
USDBRL(BRA) | -6.14% |
USDCNY(CHN) | -0.19% |
USDINR(IND) | -1.19% |
USDRUB(RUS) | -6.23% |
USDZAR(ZAF) | -4.16% |
Agricultural | |
---|---|
Cocoa | -2.16% |
Coffee (Robusta) | +3.98% |
Cotton | +3.77% |
Sugar #11 | +7.09% |
Lean Hogs | +1.75% |
Lumber | +8.36% |
Orange Juice | +10.30% |
Soybean Meal | +1.05% |
White Sugar | +3.09% |
Coffee (Arabica) | +7.78% |
Feeder Cattle | +6.87% |
Wheat | -2.17% |
Cattle | +8.30% |
Corn | -1.86% |
Soybeans | +0.83% |
Credit Indices
Index | Change |
---|---|
Markit CDX EM | +1.73% |
Markit CDX NA HY | +2.03% |
Markit CDX NA IG | -9.85% |
Markit iTraxx Asia ex-Japan IG | -18.19% |
Markit iTraxx Australia | -11.71% |
Markit iTraxx Europe | -7.10% |
Markit iTraxx Europe Crossover | -37.22% |
Markit iTraxx Japan | -8.18% |
Markit iTraxx SovX Western Europe | -2.20% |
Markit LCDX (Loan CDS) | +0.00% |
Markit MCDX (Municipal CDS) | -6.68% |
Global ETFs (USD)
Nifty Heatmap
Index Returns
For a deeper dive into indices, check out our weekly Index Update.
Market Cap Decile Performance
Decile | Mkt. Cap. | Adv/Decl |
---|---|---|
1 (micro) | +4.84% | 74/57 |
2 | +7.45% | 83/47 |
3 | +4.50% | 76/54 |
4 | +6.77% | 74/56 |
5 | +5.63% | 74/56 |
6 | +5.99% | 71/60 |
7 | +4.23% | 71/59 |
8 | +3.29% | 74/56 |
9 | +1.77% | 66/64 |
10 (mega) | +1.89% | 65/66 |
Top Winners and Losers
HINDALCO | +18.41% |
TATAMOTORS | +19.71% |
VEDL | +24.22% |
MARUTI | -6.64% |
IBULHSGFIN | -6.62% |
RECLTD | -3.99% |
ETF Performance
CPSEETF | +5.23% |
PSUBNKBEES | +4.51% |
INFRABEES | +2.98% |
NIFTYBEES | +2.86% |
BANKBEES | +2.65% |
GOLDBEES | +1.96% |
JUNIORBEES | +1.59% |
Yield Curve
Bond Indices
Sub Index | Change in YTM | Total Return(%) |
---|---|---|
0 5 | -0.02 | +0.22% |
5 10 | -0.04 | +0.37% |
10 15 | -0.04 | +0.47% |
15 20 | -0.03 | +0.45% |
20 30 | -0.04 | +0.59% |
Investment Theme Performance
Momentum | +5.64% |
ADAG stocks | +4.50% |
High Beta | +4.48% |
Velocity | +4.07% |
Next Trillion | +2.64% |
ASK Life | +2.56% |
The RBI Restricted List | +2.51% |
PPFAS Long Term Value | +2.35% |
Magic Formula | +1.95% |
Quality to Price | +1.94% |
CNX 100 Enterprise Yield | +1.81% |
Financial Strength Value | +1.79% |
Low Volatility | +1.57% |
Balance Sheet Strength | +0.67% |
Tactical CNX 100 | +0.00% |
Equity Mutual Funds
Bond Mutual Funds
Thought for the weekend
- High stock prices, just like high house prices, are harbingers of low returns.
- Investing in price-depressed residential rental property in Atlanta is like investing in EM equities today—the future expected long-term yield is much superior to their respective high-priced alternatives.
- Many parallels exist between the political/economic environment and the relative valuation of U.S. and EM equities in the periods from 1994 to 2002 and 2008 to 2015.
- Our forecast of the 10-year real return for U.S. equities is 1% compared to that of EM equities at 8%, now valued at less than half the U.S. CAPE.
Source: Investing versus Flipping
MOMENTUM
We run our proprietary momentum scoring algorithm on indices just like we do on stocks. You can use the momentum scores of sub-indices to get a sense for which sectors have the wind on their backs and those that are facing headwinds.
Traders can pick their longs in sectors with high short-term momentum and their shorts in sectors with low momentum. Investors can use the longer lookback scores to position themselves using our re-factored index Themes.
You can see how the momentum algorithm has performed on individual stocks here.
Here are the best and the worst sub-indices:
Relative Strength Spread
Refactored Index Performance
50-day performance, from July 29, 2015 through October 09, 2015:
Trend Model Summary
Index | Signal | % From Peak | Day of Peak |
---|---|---|---|
CNX AUTO | LONG |
11.52
|
2015-Jan-27
|
CNX BANK | LONG |
14.42
|
2015-Jan-27
|
CNX COMMODITIES | LONG |
35.32
|
2008-Jan-04
|
CNX CONSUMPTION | LONG |
5.71
|
2015-Aug-05
|
CNX ENERGY | LONG |
33.93
|
2008-Jan-14
|
CNX FMCG | LONG |
7.96
|
2015-Feb-25
|
CNX INFRA | LONG |
53.04
|
2008-Jan-09
|
CNX IT | SHORT |
87.64
|
2000-Feb-21
|
CNX MEDIA | LONG |
18.06
|
2008-Jan-04
|
CNX METAL | LONG |
66.60
|
2008-Jan-04
|
CNX MNC | SHORT |
9.28
|
2015-Aug-10
|
CNX NIFTY | LONG |
8.97
|
2015-Mar-03
|
CNX PHARMA | LONG |
4.00
|
2015-Apr-08
|
CNX PSE | LONG |
33.36
|
2008-Jan-04
|
CNX PSU BANK | SHORT |
40.81
|
2010-Nov-05
|
CNX REALTY | LONG |
90.30
|
2008-Jan-14
|
CNX SERVICE | LONG |
9.02
|
2015-Mar-03
|
Nifty one year daily return correlations
Nifty one month daily return correlations
Bank Nifty one year daily return correlations
Bank Nifty one month daily return correlations
Midcap one year daily return correlations
Midcap one month daily return correlations
A lot of thick blue squares mean that positive correlations are high. Red squares mean negative correlations are high. Whites are the doldrums.