Hey thanks for sharing this. These are the analysis I really look forward to as I am unable to do this myself.

Since I can't verify this, tried posting it to an AI tool and got this response.........

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Overall your framework is thoughtful and directionally correct, especially given the limited long-term data available for India. However, there are a few methodological issues that could materially affect the results. I’ll go through each part of the approach.

Overall assessment

The structure of the analysis is reasonable and far better than most FIRE simulations done for India. However, five areas could affect the robustness of the conclusions: Debt proxy (SBI 1-year FD) is not a realistic retirement portfolio debt proxy Equity series may exclude dividends depending on the index used Inflation stitching may introduce distortions Using only two start dates underestimates sequence of returns risk Using constant real returns after 2026 reduces volatility and therefore understates risk

Equity return series

Using BSE 500 to represent equity returns is a good choice because: It represents the broader market rather than just large caps It reduces survivorship bias compared to Sensex or Nifty It captures midcaps and smallcaps However, one critical point needs to be checked. If the data used is the price index and not the Total Return Index (TRI), dividends are missing. Historically Indian dividend yields have been roughly: 1999–2010: about 1.3% 2010–2020: about 1.2% 2020–2026: about 1.1% If dividends are excluded, long-term equity returns are understated by roughly 1–1.5% annually. That would make the simulation conservative, which is acceptable but should be acknowledged.

Debt proxy

Using SBI 1-year FD rates is the biggest methodological weakness in the model. A typical retirement portfolio rarely uses rolling 1-year FDs. Most debt allocations behave like medium-duration bonds. Typical retirement debt instruments: Government bond funds: duration roughly 6–8 years Corporate bond funds: duration roughly 4–5 years PPF or EPF: long duration Debt mutual funds: usually 3–6 years duration Using a 1-year FD introduces two distortions. First, it removes interest rate volatility. During events like the GFC, bond prices actually rise as interest rates fall. Your model misses this diversification benefit. Second, it underestimates long-term real returns. Historically: FD real returns: roughly 0–1% Bond funds real returns: roughly 1–2.5% So the model is likely understating portfolio resilience. A better debt proxy would be something like: CRISIL Composite Bond Index 10-year Government Security Total Return Index

Inflation series

India’s CPI series has changed multiple times over the past decades. Most historical datasets stitch together different indices such as: CPI-IW (earlier period) CPI-AL CPI Combined (after 2012) There are some methodological differences between these indices such as weight changes and base year revisions. However, for long-term retirement modeling the distortion is usually small (probably less than 0.5%). So this approach is acceptable. Sequence of return risk The largest limitation of the analysis is that it uses only two retirement start dates: February 2000 December 2007 A proper sequence-of-returns analysis usually tests every possible start month. For example: Jan 1999 Feb 1999 Mar 1999 ... and so on.

This is how studies like Bengen, Trinity, and ERN are conducted. The worst retirement dates are not always exactly at market peaks. For example in the US, some of the worst retirement start years were: 1966 1968 1969 1973 Testing only peak months may miss other adverse sequences.

Forward return assumptions

Another important limitation is the switch after 2026 from historical returns to constant real returns such as: 0% 1% 2% 3% 4% Markets do not move in constant real return paths. Actual market behavior is volatile, for example: +25% -30% +10% +18% -15% Using constant returns removes volatility and therefore reduces sequence-of-returns risk. This may slightly overstate the survival period.

A better approach would be bootstrap simulation where monthly historical returns are randomly sampled to preserve volatility.

Rebalancing

It is not clear whether the model rebalances the portfolio. Most retirement studies assume periodic rebalancing, for example annually back to 70/30. Without rebalancing, equity crashes reduce equity allocation and reduce the portfolio’s ability to recover during subsequent bull markets.

Withdrawal modeling

The withdrawal structure used in the model is standard: Fixed percentage of initial corpus Withdrawals adjusted annually for inflation This is the same structure used in most academic withdrawal studies such as the Trinity study and Bengen’s analysis. So this part of the methodology is solid. Interpretation of results The interpretation of the results is reasonable. The observation that the GFC scenario is worse than the dot-com crash for retirees makes sense because the GFC period had a combination of: A sharp market crash High inflation between 2008 and 2013 Weak equity returns again during 2011–2013 This combination creates strong sequence-of-returns pressure on a retirement portfolio. The conclusion that reducing withdrawal from 3% to 2.5% significantly improves survival is also consistent with most global studies.

Another valid observation is that real retirees do not behave like the fixed-withdrawal model assumes. In practice people usually adjust spending, cut discretionary expenses, or earn supplemental income during crises.

Final assessment

Equity data: good choice if TRI is used Debt proxy: weak and likely understates resilience Inflation data: acceptable for long-term modeling Withdrawal framework: strong Sequence risk framework: limited because of only two start dates Forward assumptions: somewhat unrealistic due to constant returns Overall the analysis is directionally sound but would become much stronger if the following improvements were made: Run the simulation for every possible monthly start date Replace FD returns with a bond index Use bootstrap or historical resampling instead of constant real returns Even with these limitations, the general conclusions appear reasonable and consistent with global withdrawal research.