Indian Rupee Spline-GARCH Volatility Analysis
High-persistence model: shocks decay very slowly, so the theoretical long-run value may not be practically meaningful
Volatility prediction for Tuesday, October 13th, 2026
1 Day
8.67%
increased by 4.00%
1 Week
8.93%
increased by 4.26%
1 Month
9.91%
increased by 5.24%
Analysis last updated: Monday, October 12, 2026 at 07:04 PM UTC
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News Impact Curve
How returns affect tomorrow's volatilityVolatility Forecast
How volatility evolves over timeParameter Estimates
Jul 4, 1991 to Oct 9, 2026Model Insight
With persistence 1.000, volatility shocks have a half-life of 86643 trading days (~343.8 years), close to a unit root, so long-run forecasts are highly sensitive to this estimate.
τ
Spline-GARCH Model
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High persistence: persistence 1.000, shock half-life ~86643 days
| Param | Value | t-stat |
|---|---|---|
| ωconst | 0.7268 | 0.06 |
| αARCH | 0.1708 | 0.00 |
| βGARCH | 0.8292 | 0.01 |
Spline Coefficients
K=10
| γ1 | 0.0058 | 0.00 |
| γ2 | -0.1237 | 0.00 |
| γ3 | 0.1267 | 0.00 |
| γ4 | 0.0085 | 0.00 |
| γ5 | -0.0240 | 0.00 |
| γ6 | -0.0307 | -0.01 |
| γ7 | 0.0643 | 0.02 |
| γ8 | -0.0117 | -0.01 |
| γ9 | -0.0975 | -0.04 |
| γ10 | 0.2956 | 0.09 |
1.000
Persistence86643d
Half-lifeτ
Spline-GARCH Model
Tap to view equation
| Parameter | Value | t-statistic |
|---|---|---|
ω const Unconditional variance weight | 0.7268 | 0.06 |
α ARCH Response to squared shocks | 0.1708 | 0.00 |
β GARCH Volatility persistence | 0.8292 | 0.01 |
Spline Coefficients
K=10
| γ1 | 0.0058 | 0.00 |
| γ2 | -0.1237 | 0.00 |
| γ3 | 0.1267 | 0.00 |
| γ4 | 0.0085 | 0.00 |
| γ5 | -0.0240 | 0.00 |
| γ6 | -0.0307 | -0.01 |
| γ7 | 0.0643 | 0.02 |
| γ8 | -0.0117 | -0.01 |
| γ9 | -0.0975 | -0.04 |
| γ10 | 0.2956 | 0.09 |
Persistence:
1.000
Half-life:
86643 days
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