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Indonesian Bitcoin study presents simulated returns as 2018 to 2026 findings

Risk shares and rebalancing hold up; Sharpe significance and drawdown gains tell us nothing about real Indonesian markets.

2026-10-27 · 5 min read · Multi-Asset Allocation and Rebalancing · Indonesian equities, Indonesian government bonds, gold, and spot cryptocurrencies, evaluated in rupiah; the required complete portfolio is unavailable.

Reviewing: THE ROLE OF CRYPTO ASSET DIVERSIFICATION IN REDUCING RETAIL INVESTOR PORTFOLIO RISK · Adang Sutisna, Nur Fitriyani, Arasy Alimudin et al. · Read it on openalex

The 1 to 5% Bitcoin allocation Sutisna, Fitriyani, Alimudin, Nilowardono and Arimbawa recommend for Indonesian retail investors rests on simulated returns. Readers have to look beyond the abstract to learn that. The method section presents the data as market series without explaining how the simulation works. Every results table and figure bears the line "Source: simulated data". The limitations are explicit: "Most importantly, the present results are based on simulated returns, and the analysis must be repeated with actual market data before substantive conclusions can be drawn." The conclusion likewise describes an "illustrative analysis". The abstract omits that qualification. It reports daily data from 3 January 2018 to 30 June 2026 and findings about Indonesian retail investors. Placement matters here: most readers will leave with the abstract's account.

Inside the allocation experiment

The experiment itself is straightforward. The authors use the Jakarta Composite Index (JCI), an Indonesian government bond total return index and rupiah gold to construct portfolios for retail risk profiles. Their fixed weights are conservative 20/70/10, moderate 50/40/10, aggressive 80/10/10. They add crypto at 0, 1, 2.5, 5, 10 and 20%, taking the funding pro rata from the other three assets. Rebalancing is monthly, and the sample contains 2,215 daily observations.

Bitcoin fills the sleeve in the main runs. The alternatives are BTC and ETH 50/50, an equal-weight basket of five coins, and a fixed cap-weighted basket. The risk measures cover volatility, daily 95% VaR and ES, maximum drawdown, worst month, Euler risk contributions and the diversification ratio. The last divides weighted-average asset volatility by portfolio volatility. To test Sharpe differences, the authors use a stationary block bootstrap with 20-day mean blocks and 2,000 resamples.

The simulated return assumptions do much of the work. Bitcoin earns 26.9% annually at 64.8% volatility; stocks return 3.4%, less than the 4.53% deposit rate. The baseline portfolios only just beat cash. The moderate portfolio has a Sharpe of 0.015. Add an asset with a large positive drift and a 0.24 correlation to stocks, and that Sharpe will rise. The paper gets the expected result.

Separating absolute risk from risk-adjusted return is the paper's first claimed contribution. Its own citation to Kajtazi and Moro (2019), however, credits them with finding that Bitcoin helps more through returns than through reduced volatility. The additions here are the comparison of coin baskets within the sleeve and the analysis of buy-and-hold drift, applied to portfolios shaped around Indonesian retail investors. With simulated data, the conclusions that survive are those dictated by the inputs.

A 5% Bitcoin weight accounts for 20% of moderate portfolio risk

The risk-share analysis is the paper's strongest section. Similar inputs would produce it on almost any data. Adding a 5% Bitcoin weight to the moderate portfolio raises volatility from 8.19% to 9.15%; daily ES increases from 1.23% to 1.39%. Bitcoin supplies 19.9% of portfolio variance. Its share reaches 44.8% at 10% and 74.7% at 20%. Put 64.8% volatility and that correlation into the Euler formula, and a risk share near 20% at a 5% weight follows regardless of how the path was generated.

The less-than-proportional increase is arithmetic as well. Five percent of Bitcoin contributes 2.11 points of return and 0.96 points of volatility. The diversification ratio moves from 1.30 to 1.46, reaches its high of 1.47 at 10%, then drops to 1.35 at 20%.

The comparison within crypto works the same way. Volatility for the equal-weight basket is 76.4%, versus Bitcoin's 64.8%. Their drawdowns are 91.9% and 75.6%, respectively, and the basket's diversification ratio is just 1.121. The abstract gives an average correlation among the coins of 0.75. Bitcoin's correlations with the other four range from 0.784 to 0.812. Adding individually more volatile coins increases the sleeve's risk.

At a 5% sleeve weight, the basket produces a portfolio Sharpe of 0.200, versus 0.242 for Bitcoin alone. This ordering relies on simulated returns in which XRP loses 8.6% annually and ADA loses 27.1%. The volatility ordering requires only that each of the other four coins has higher volatility than Bitcoin: 80.9% to 99.5% versus 64.8%.

How much does one simulated path tell us about drawdowns?

Small allocations, the abstract says, "improve the Sharpe ratio significantly, and even reduce the maximum drawdown and the worst monthly loss." The moderate portfolio's drawdown improves from -17.8% to -16.3% at 2.5%, and to -16.6% at 5%. Its worst month improves from -6.4% to -5.9%. The aggressive portfolio also benefits: its drawdown goes from -29.8% to -27.4% at 5%. The conservative portfolio deteriorates, moving from -7.9% to -9.3% at 5% and -12.6% at 10%.

Two of three profiles support the abstract's claim.

The authors explain the improvement through Bitcoin's recovery while traditional assets continue falling. That explanation describes the timing of the deepest drawdowns along a single simulated path.

The authors are frank about significance. At the 10% allocation, every reported interval contains zero, and p ranges from 0.055 to 0.063. At 5%, the moderate portfolio gains 0.227 (CI 0.008 to 0.449, p = 0.038). The aggressive portfolio gains 0.161 (p = 0.043). The conservative gain reaches significance only at the 10% level (p = 0.056). Across six tests, two fall below 0.05, without any adjustment for multiple tests.

The bootstrap has a more fundamental limit. Resampling simulated returns measures noise within the simulator. It cannot establish whether rupiah markets exhibit Bitcoin's 26.9% drift or the increase in its stress-day correlation from 0.157 to 0.304.

Rebalancing earns its place

For a retail desk, Table 7 is the most useful table. It compares buy-and-hold with monthly rebalancing. An initial 5% Bitcoin weight in the moderate portfolio reaches 41.5% and finishes at 23.0% if left untouched. With monthly rebalancing, its maximum is 7.2%.

Buy-and-hold volatility is 19.06%, against 9.15% with rebalancing. Drawdown is -32.9% against -16.6%, and Sharpe is 0.196 against 0.242. Returns are roughly alike, at 6.65% and 6.53%.

The mechanism makes sense. An asset compounding at 26.9% alongside a portfolio earning 4.41% will dominate unless someone trims it. The size of the effect depends on the path. Transaction costs, taxes and exchange fees are omitted, as the authors acknowledge.

We could not repeat the exercise with actual data. Our platform lacks both JCI return history and an Indonesian government bond total return index. We also lack the rupiah exchange-rate series required to express gold and crypto in IDR, along with the one-month rupiah deposit rate used as the Sharpe hurdle. Substituting US assets would address a different question.

The risk-share arithmetic and Table 7 can guide the sizing and control of a sleeve. The 1 to 5% recommendation remains open until these tables are rerun using actual Indonesian prices, with fees deducted. The authors themselves say that rerun is required.