This article is also available in PDF form here (recommended).

To read Chaos Labs’ complementary article on the subject, see here.

1. Introduction: Pool-Based Perpetual AMMs

1.1 Structural Overview

In this article, we explore the potential for diversifying Real World Assets (RWAs) to mitigate risk in multi-asset, pool-based perpetual AMMs. These DEXes face a unique set of risk management challenges that make them particularly well suited to the benefits of asset diversification.

Before diving deeper, we’ll begin by clearly defining the distinct perpetual exchange design paradigm we’re focused on. This model diverges substantially from both traditional Central Limit Order Books (CLOB) and virtual Automated Market Makers (vAMM). Often loosely described as an “oracle-based AMM,” this label does not fully capture its unique approach to price discovery, which significantly departs from standard AMMs that enable true price discovery of the underlying asset.

Key characteristics of this DEX model include:

1. Unified Reference Price. Unlike models with differing mark and index prices, this system uses a single reference price for each traded asset. This does not facilitate organic price discovery of the perpetual itself but rather involves synthetic or virtual pricing of the cost of holding the position, enabled by market-condition-dependent fees.
2. Fee Structuring. Use of fees to price the risk of positions to the protocol by simulating the effects of organic price discovery.
   - One-Time Fees. At trade opening/closing, fees may include a skew-dependent fee simulating price impact, or a fixed percentage of the notional trade size.
   - Compounding Fees. These are crucial during the trade’s lifetime for pricing externalities. They often include a blend of borrowing costs, volatility risk, and open interest skew, thereby managing the risk of positions to the protocol and its counterparty pool.
3. Fully Onchain, Asynchronous Order Fulfillment. This model allows immediate trade execution within liquidity bounds, independent of a direct counterparty. This requires:
   - Risk-dependent compounding fees to restore protocol equilibrium and minimize directional risk.
   - Pooled capital to balance temporary liquidity imbalances.
4. Pooled Capital as a Third Counterparty. Similar to AMMs, this model relies on pooled liquidity for trade execution without an immediate counterparty. However, unlike AMMs where liquidity providers (LPs) face impermanent loss risk, here LPs face PnL risk, directly counterbalancing trader profits and losses. Recent developments have sought to mitigate risks through liquidity segmentation to manage the impact of market anomalies or sustained trader gains.

In summary, the primary distinguishing feature of this DEX model is its approach to virtual price exposure, underpinned by asynchronous, onchain trade execution. This model opens the door for un- or less-correlated RWAs to play a unique role in enhancing risk management.

1.2 Risk Management Challenges

Despite the growing popularity of this model, it faces complex risk management challenges. These arise primarily from its asynchronous order matching, the potentially significant resulting directional exposure, and risk concentration across multiple, often highly correlated assets within a single pool.

Current risk management strategies focus mainly on pricing and risk concentration. Examples include:

• Dividing liquidity into asset-specific pools, reducing risk concentration compared to a unified pool approach.
• Conservatively limiting Open Interest (OI) relative to pool size to manage risk, albeit at the cost of capital efficiency.
• Designing fee structures that more accurately reflect the risks each position brings to the protocol, like implementing more aggressive, non-linear funding rates.

A significant aspect yet to be thoroughly explored is the use of asset diversification as a risk management tool. The issue of counterparty exposure concentration is intensified by the high correlation typically observed among crypto-native assets. To illustrate, consider Figure 1, which shows contrasting correlation matrices between the top five non-stablecoin crypto pairs and a mix of forex and commodity pairs.

Both basic intuition and Modern Portfolio Theory (MPT) suggest that combining a mix of uncorrelated and negatively correlated assets could reduce the overall risk per unit of return, especially when compared to a portfolio of highly correlated assets. This is under the assumption that Open Interest is similarly skewed (either long or short) across all assets. Our risk-scoring system incorporates widely used MPT formulas to quantify risk.

The following sections aim to investigate how asset diversification, including through forex, commodities, and other traditional asset classes beyond crypto-native assets, can enhance risk management in these perpetual AMM models. We introduce a novel scoring mechanism to quantify a pool’s total counterparty risk at any given time. Given the complexity of assessing combined risk in a multi-asset portfolio, this scoring system condenses various risk dimensions into a single, comprehensive risk score. The core question we address is whether a diverse range of tradeable Real World Assets can substantively improve risk management in these innovative perpetual AMM models.

2. Analysis: Theoretical Framework

2.1 Model Assumptions

Defining the scope and assumptions of our analysis

The specific risk we’re interested in evaluating in this context is that of trader counterparty risk; namely the net risk taken on by the pool that acts as a counterparty to traders and enables the trading engine’s asynchronous order matching. It’s worth noting that the specific nature and risks taken on by this counterparty pool vary depending on protocol implementation. The Ostium Protocol, for instance, features a “liquidity cushion” that accrues liquidation rewards over time and acts as first settlement of trader PnL, reducing the volatility in value of the liquidity pool. Other protocols, as discussed previously, choose to split liquidity into multiple pools rather than a single one.

For the purposes of this analysis, however, we’ll model the simplest implementation of this system. We assume a single counterparty pool that takes on traders’ net directional exposure at any given point in time (Figure 2).

To make explicit a few more of our terms and assumptions before moving forward:

1. Stablecoin settlement. Settlement only in a dollar-denominated stablecoin. This avoids the additional complexity of accounting for fluctuations in the dollar-denominated value of assets in the Counterparty Pool. If those assets differ from those used as collateral by traders, they may move with or against traded assets, further magnifying or reducing total risk.
2. Static pool and Open Interest caps. We assume pool size is static aside from trade settlement and not subject to fluctuation from depositor behavior (withdrawal/deposit).
3. Fully synthetic trading and oracle pricing. The only asset “changing hands” is a dollar-denominated stablecoin. All price exposure is denominated in dollars and provided real-time via reliable oracles (no erroneous price reporting).
4. Fee exclusion. Compounding funding and borrowing fees are excluded from our model to avoid further complexity.
5. Notional exposure. All Open Interest “exposure” values are notional. E.g., a 10x long on $100 of collateral (=$1,000 long exposure) and a 5x long on $200 of collateral (=$1,000 long exposure), are treated equivalently in this analysis.
6. Imbalance definition. “Imbalance” refers to the cumulative net imbalance in Open Interest for a given asset. E.g., if an asset’s long exposures total $1,000 and its short exposures total $800, the imbalance is $200 long.
7. Portfolio definition. The term “portfolio” refers to the specific set of assets traded, to which the pool serves as a counterparty and settlement layer.
8. Risk quantification. Risk is defined as any factor that leads to a loss or gain for LPs or traders, as protocols themselves should take no directional opinion on who should “make money.” A protocol’s assumed “objective function” is one of zero PnL, net of fees, for either party. The goal is to minimize variance around this median.
9. Unrealized PnL (uPNL). We quantify risk assuming positions can be closed at any time. The term unrealized PnL to denote positions’ outstanding gains or losses.

2.2 Building Intuition

Why a linear combination of open interest imbalances is insufficient to capture risk.

With that out of the way, let’s begin building intuition for how to go about our stated goal of quantifying counterparty pool risk. While the fairly straightforward Sum of Absolute Imbalances (∑ |B_i|) across assets i might seem like a good place to start, we’ll explain why simple sums of this nature are insufficient to generate a quantitative and interpretable measure of actual risk.

Consider, for instance, a protocol with positions open only on two assets, A and B. If:

• Asset A has an imbalance B_a of 100 USD long, and
• Asset B also has an imbalance B_b of 100 USD long,

One way to determine the risk might be,

But, if

• Assets A and B are perfectly inversely correlated assets (ρ{a,b} = −1),

One asset’s long imbalance should hedge out the other asset’s long imbalance. Following this logic and now factoring correlation into our risk measure, these imbalances cancel each other out:

However, this measure still doesn’t properly capture risk. If:

• A is twice as volatile as B, and
• A moves up 10% in price (uPnL = 10), then
• Given B is inversely correlated with A and is half as volatile,
• B moves down 5% in price (uPnL = -5).
• Resulting in a total uPnL: 10–5 = 5 USD

Compare that to a scenario where A and B are equally volatile — resulting in equal movements in price and no change in uPnL — and it becomes clear that ignoring volatility leads to an inaccurate measurement of risk. A risk score of 0 at time t fails to capture the increased probability of greater future imbalance resulting from differing volatilities.

Thus, any best approximation of risk will need to be more sophisticated than a simple linear combination, simultaneously accounting for imbalance, volatility, and asset correlation.

2.3 Sources of Counterparty Risk

Quantifying and formalizing the sources of counterparty risk in our model.

Let’s start by identifying the main metrics influencing counterparty risk discussed above, i ∈ I = {1, . . . , n} represents the assets in a portfolio:

1. Asset Volatility: degree of variation or fluctuation in the prices of an asset. We measure this using a vector of the standard deviation of returns, computed using historical price data. The dimensions are asset_i per period, or some defined historical data timeframe (1 day, 1 hour, 15 min. . . ):

2. Asset Correlation: degree to which values or returns of two or more assets move in relation to one another. We measure this using a Pearson Correlation Matrix, computed using historical price data. Correlation is dimensional:

3. Asset Imbalances: a snapshot of the set of long and short Open Interest imbalances at time t across assets. We measure this using a vector of asset imbalances, computed by pulling (for real data) or simulating (for experimentation purposes) the set of open interest imbalances for each asset i of n total assets at time t. We denominate this in USD per asset_i:

Figure 3 succinctly summarizes the framing of concepts (Risk, Dimension), information (Metric), and data (Data Source).

Our goal is to develop a single metric, which we’ll term Imbalance Score, that acts as a best approximation of protocol counterparty risk. In general, a good risk score will increase, ceteris paribus, if any of the subcomponents driving risk increase (and the inverse), conditional on this increase not itself canceling out another source of risk (e.g., an increase in the Sum of Absolute Imbalances reducing risk due to perfect inverse correlation between assets).

In summary, the Imbalance Score will be the output of a function that takes as inputs assets’ volatilities, correlations, and imbalances:

In the next section, we’ll combine these inputs to derive a function for the Imbalance Score.

2.4 Deriving the Imbalance Score

Deriving the imbalance score through a step-by-step walk-through of each input to the score and assessing its limitations before introducing additional dimensions.

Factoring in Volatility

Now that we’ve agreed on both the primary sources of risk and concrete ways to measure them individually, let’s look at the first way we can begin combining these sources of risk into a single metric. As discussed above, the correlations and differing volatilities between listed assets mean the true implied risk of any given set of imbalances differs from the simplest linear combination.

This leads us to the concept of Imbalance Implied Risk (IIR). This metric not only considers the imbalance amount at time t for a given asset, but also considers the asset’s volatility by multiplying this imbalance by the historical standard deviation of daily returns:

Each IIR value represents the expected dollar return for the next period if a one Standard Deviation event occurs. It quantifies the risk of a given imbalance for a given asset. An asset’s imbalance is riskier if it carries a larger IIR.

Why do we need this metric? Put simply, even if two assets have the same imbalance today, if the probability of a large price move for asset A exceeds that of a move in asset B, that future risk should be priced into any quantitative measure of risk at time t. Multiplying an asset’s imbalance by its volatility scales the value of that imbalance to a standardized value. This allows us to compare the risk of a given imbalance (e.g. 100 USD) for different assets (e.g., Gold vs. Oil).

IIR data at time t for each asset can be stored in a vector, which we’ll call K. K_i is the i’th asset’s IIR:

There are plenty of different ways we could aggregate the components of K, but we chose to use the Euclidian Norm (vector length). This scalar value stands for Cumulative Imbalance Implied Risk, and measures the cumulative risk that does not account for correlation between traded assets:

Factoring in Correlation

To address this limitation, we reintroduce R below, the asset correlation matrix presented briefly in section 3.2. If we multiply this n · n Pearson correlation matrix of asset i by j it will introduce an additional dimension — correlation — to enhance the metric, providing a more comprehensive measure of counterparty risk.

The problem with the resulting value is that it is of the wrong dimension. Multiplying this result by the Transpose of K, however, solves this issue:

There’s one last problem with this result: the resulting units are in (USD^2/day^2), and they should be the same as ||K||, meaning in (USD / day). Thus:

We’ve now formally derived the Imbalance Score (IS), the core metric of our risk assessment in this research article, which captures the risk of a set of imbalances in Open Interest at time t:

3.5 Comparison to Modern Portfolio Theory

Outlining the ways in which the Imbalance Score is analogous to Portfolio Return Variance.

The structure of the Imbalance Score bears many similarities to Modern Portfolio Theory (MPT), which we’ll discuss in this section.

In MPT, the Portfolio Return Variance (PRV) is a measure of the total risk of a portfolio. Similar to the IS, a larger value represents a larger risk.

The PRV formula takes into account the individual variances of each asset in the portfolio, as well as the covariances between pairs of assets:

For comparison, we’ll expand the squared Imbalance Score below:

The Portfolio Return Variance (PRV) and Imbalance Score (IS) are analogous. Both represent:

1. Risk from asset exposure: term on the left side of “+” in IS & PRV equations
2. Risk from correlation: term on the right side of “+” in IS & PRV equations

To prove both metrics are related, let’s consider,

Where B∗ represents the Sum of Absolute Imbalances across a portfolio. A measure of the weight of any individual imbalance, relative to the sum of imbalances in a portfolio, would be:

The above formula is a representation of portfolio asset weights, as they would be used in Modern Portfolio Theory, adapted to the features of our protocol: the “weight” of an asset is its imbalance relative to the total imbalances. Substituting the w_i in PRV for the above,

We can precisely see how the Imbalance Score is analogous to PRV, adapted to our use case. More specifically, the IS goes beyond the PRV in that it accounts not only for the relative weight of imbalances as PRV does, but also for the overall magnitude of these imbalances.

To conclude: the Imbalance Score is derived from and closely mirrors the makeup and structure of MPT’s Portfolio Return Variance to suit our specific use case of quantifying counterparty risk in pool-based perpetual AMMs.

3. Validation and Simulation

Simulating Crypto and RWA portfolio behavior and comparing their resulting risk profiles using the Imbalance Score.

In the following section, we’ll run simulations to demonstrate that for a given set of imbalances, a diversified Real World Asset-containing portfolio with lower inter-asset correlation consistently yields lower risk.

We begin by simulating imbalances and showing the impact on the Imbalance Score for different portfolios, without factoring out the impacts of volatility or correlation. Secondly, we narrow our focus, standardizing for volatility and imbalance, and plotting results on a dispersion chart to illustrate the specific impact of including less-correlated Real World Assets in a perpetual AMM portfolio to reduce overall risk.

3.1 Data Specifications

The following simulations use crypto and Real World Asset price data with the below specifications:

• Five of the largest-cap, non-stablecoin crypto assets (BTC, ETH, SOL, XRP, BNB), the three most widely traded G10 currencies (EUR/USD, GBP/USD, USD/JPY) and the two most widely traded commodities (XAU, WTI)
• The last 2 years of data (2021–09 → 2023–09)
• Time units of days: the average daily price change was used to compute the period return

Figure 5 shows crypto assets’ greater volatility while Figure 6 shows the higher correlations between crypto than selected Real World Assets.

3.2 Imbalance Score in Action

Next, we outline two scenarios to help build intuition for why the Imbalance Score uniquely captures the risk posed by a given set of assets and imbalances.

Scenario 1

Let’s consider a BTC imbalance of $500 and an ETH imbalance of $200 (both long). Drawing insights from historical data, their standard deviations of daily returns are 0.03 and 0.04, respectively, with a correlation of 0.89.

Let’s start by computing the inputs B, σ, K and R under this scenario.

Calculating the Imbalance Score, we get

Scenario 2

Let’s now consider the same variables as above, but instead, with the imbalance for BTC flipped to -$500 (short skew) and $200 for ethereum.

Despite Scenario 2’s lower IS, both scenarios share the same ||K|| (Cumulative Imbalance Implied Risk):

Following intuition, we expect Scenario 2 to carry less risk: BTC’s short imbalance partially hedges ETH’s long imbalance due to BTC and ETH’s positive correlation. Unlike ||K||, which remains the same, the Imbalance Score correctly reflects these differing risks:

• The first scenario’s IS is larger than ||K||, indicating the positive correlation between both long skewed assets increases the counterparty risk,
• The second scenario’s IS is smaller than ||K||, indicating that inversely skewed imbalances mitigate risk.

Further, plotting the total IS against an individual asset’s imbalance allows us to visually grasp our intuition about the optimal set of imbalances to minimize counterparty risk:

For each asset, ceteris paribus — assuming the other asset’s imbalance remains constant — there is an optimal imbalance that hedges the other’s assets imbalance.

3.3 Simulation 1: Imbalance Score Only

We’ll now split these assets into various portfolios for our simulation. Asset characteristics (standard deviation of returns, correlations) are derived from historical data (see above):

• Crypto Portfolio: BTC, ETH, SOL, XRP, BNB
• RWA Portfolio: XAU, WTI, EUR, GBP, JPY
• Crypto+RWA Portfolio: BTC, SOL, WTI, EUR, XAU

Figure 7 shows the frequency of Imbalance Scores generated by simulating asset imbalances over 5,000 iterations for the three portfolios above. The RWA-only and RWA+crypto portfolios both show a lower mean and narrower distribution of Imbalance Scores than the crypto-only portfolio.

While this figure shows a clear pattern of lower global risk, as measured by the Imbalance Score, for portfolios containing RWAs, it fails to standardize both for volatility and imbalance. Crypto’s greater volatility, for instance, by default results in a higher mean and wider distribution of Imbalance Scores. This plot thus fails to display only the specific impact of inter-asset correlation on Imbalance Scores.

3.4 Simulation 2: Standardizing for Imbalance & Volatility

To account for these shortcomings, we’ll instead plot Imbalance Scores against ||K||, the measure of cumulative risk that only factors in imbalance and volatility.

Why? Recall the three inputs to risk mentioned at the start of our analysis: volatility, correlation, and imbalance. To parse out the impact of correlation specifically on risk, we’ll need to plot a metric that captures only volatility and imbalance (||K||) against one that captures all three (IS).

The results from Figure 8 show the imbalances are much further from the central line for the same ||K|| in the crypto portfolio, representing greater IS variability.

The central black line represents the IS as a function of ||K|| where all portfolio assets would be independent. If a dot is above the central line, the total cross-asset correlation for that specific imbalance increases the IS. The inverse is also true.

The crypto portfolio’s R^2 of 0.616, compared to the latter two portfolio’s R^2 of 0.970 and 0.956 provide further quantitative validation of our previous statement. Values closer to 1 indicate a lower variability in IS for the same ||K||. Adding Real World Assets to a crypto-only pool-based perpetual AMM portfolio reduces total risk.

Ultimately, both simulations yield the same conclusion. A crypto-only portfolio systematically carries more risk, whether or not results are standardized. Adding Real World Assets to crypto-only portfolios yields a lower IS variability, and thus a lower risk.

4. Conclusion

In conclusion, this study provides an in-depth exploration of risk management in multi-asset, pool-based perpetual AMMs, emphasizing the benefits of integrating Real World Assets. By developing the Imbalance Score, we offer a new perspective on quantifying and managing risks in these complex systems. We compare counterparty risks across various protocols and investigate the impact of incorporating a range of RWAs with historically low or negative correlation on enhancing risk management. Our simulations and data analysis reveal that the inclusion of RWAs can significantly mitigate risk, reducing both the mean and variance in Imbalance Scores, and underscoring the value of synthetic RWAs in diversifying and stabilizing AMM portfolios. This research contributes to a deeper understanding of risk dynamics in perpetual AMMs and highlights the potential of RWAs to contribute to DeFi’s evolution.

Bitcoin Cycles

Instead of the planned inflation deep dive, we decided to shift our focus this week from gold to digital gold. With BTC up nearly 25% MoM, now seems like an appropriate time to take a step back and try to quantitatively evaluate where we’re at in the cycle — before it gets too far along. In this post, we look at metrics like the ratio of short- to long-term holders, momentum moving averages, and average cost basis to compare historical market peaks and troughs to today’s metrics.

Even accounting for this month’s rally, we appear to be very early in the cycle. More below.

Holder Behavior & Accumulation Patterns

On-chain data allows us to paint a far clearer picture of stocks and flows than we could for any other asset class.

First off: the number of bitcoin addresses with balances above a certain threshold. This first chart below shows the number of holders of 0.1+ BTC (~$3,500) more or less monotonically increasing since inception and continuing to reach new highs, albeit more slowly than e.g. 2021.

This second chart paints a slightly different picture; wallets with holdings of 10k+ BTC (~$350m) have grown steadily in number through the bear, with aggressive buying around the $20k mark. The last year in particular clearly shows strategic accumulation by large holders.

Next, the realized HODL ratio. Created by Philip Swift, it uses on-chain data about how long wallets have been holding their Bitcoin to calculate the ratio between 1 week holders and 1 or 2 year holders. This helps paint a picture of market dominance, be it by long-term or short-term holders.

Areas dominated by long-term holders (green) have coincided with market bottoms. Those dominated by short-term holders (red) have historically been tops.

The RHODL has climbed steadily this year, shooting up in particular this fall, but remains substantially below prior market peaks — again pointing to the market remaining in the early innings of the cycle.

The long/short-term holder threshold is a visual representation of prices at which short-term and long-term holders accumulated their Bitcoin. Focusing in on the chart’s red line, it’s clear to see that shorter term holders (red) are the biggest buyers at the top of each cycle, while longer term holders (blue) sell into these these shorter term holders, buying when they sell.

Once again, the data suggests skittish short-term holding patterns have reached their cyclical lows. Long-term holders, on the other hand, are accumulating.

Market Valuation & Cycle Analysis

Let’s now turn our focus to market valuation.

The chart below shows the average acquisition price for short-term holders (chart below, red line) vs. long-term holders (blue line) to arrive at a realised price (orange line).

Areas shaded purple (where spot BTC price falls below all cost basis) have proven excellent accumulation prices in the past.

The MVRV Z-Score compares market value to realized value. Realized value is calculated taking the average price of when Bitcoin was moved between wallets. It does this to try to remove short-term sentiment and thus create a ‘fair’ reference price.

When market price is much higher than realized price (red), it has often been a top sign. The inverse is also true. Judging by this metric, now remains an excellent time to accumulate (NFA!).

Our last chart is another of Philip Swift’s creations: the Pi Cycle Top Indicator. This metric is a momentum moving average indicator that has historically proven effective at identifying — you guessed it! — market tops and bottoms.

It compares the 111 SMA with the 2 * 350 SMA of Bitcoin’s Price. These two moving averages were selected as 350 / 111 = 3.153 — hence the name “Pi.”

Now What?

To return to our original question — where are we in the cycle?

To summarize holder behavior, the ratio of short- to long-term holders is on the upswing but remains near historical lows. “Committed” investors have accumulated heavily in the past year while flightier investors only just seem to be rediscovering the asset. So by all accounts, early.

On the valuation side, prices have climbed somewhat beyond historical indicators of prime accumulation periods, but remain low relative to the current cycle. Beyond the market trough, but with plenty more room to run. (NFA!)

So, early or missed the boat? Whatever your view, Ostium’s here for you. (Yes, we’re poets and we know it).

Inflation deep dive next!

Gold and Inflation: A Closer Look

With rising inflation concerns, many suggest buying gold as a hedge, with the asset’s performance in the inflationary 1970s often serving as the primary supporting evidence. This thesis, however, merits a closer look. Does this singular focus on inflation really tell the whole story?

The 1970s: An Era of Change

The 1970s were a decade like no other. A closer look suggests the end of the gold standard and extreme geopolitical uncertainty likely played a more important role than inflation alone in driving gold’s performance.

First: the lifting of the gold standard. In 1971, the United States under Nixon shifted from the gold standard, allowing the price of the metal in U.S. dollar terms to become freely discoverable for the first time in decades. The ban on private gold ownership was fully lifted in December 1974, allowing Americans to once again own and trade bullion, coins, and other forms of gold. This cessation of artificial price suppression allowed market forces to determine gold’s “true” value and helped directly lead to its upwards price trajectory.

The dollar simultaneously experienced a precipitous decline in value, dropping 30% in the ensuing decade. Rampant inflation was the defining economic phenomenon of the 1970s, leading to persistent flight from the U.S. dollar into assets perceived as safe havens — gold among them.

Geopolitical uncertainty, energy crises, and heightened Cold War tensions accentuated perceived U.S. weakness and investor appetite for safe haven assets. During the first oil shock in 1973, for instance, OAPEC (an OPEC subset) imposed an oil embargo on the U.S., leading to a quadrupling in the price of oil. Iranian regime change and Soviet action in Afghanistan, both in 1979, further highlighted the energy and geopolitical uncertainty of the time.

Why does this matter? The lifting of the gold standard and geopolitical tensions all played a role in pushing investors into safe haven assets, contributing to gold’s meteoric rise. A singular focus on inflation as the source of gold’s rise in the 1970s misses the bigger picture in favor of a reductionist one — one which investors interested in hedging against inflation would do well to understand.

Since 1980

What’s happened since that defining decade?

While other factors evidently played a central role, the correlation between gold and inflation in the 1970s is clear nonetheless. However, this correlation drops to a mere -0.07 if measured from the 1980s onwards. Is the folk wisdom wrong? Is the inflation indicator uncorrelated, with gold offering no inflation protection at all?

Well, not quite. A plausible explanation for this data is the conditional nature of inflation hedges: only when inflation expectations surpass a certain threshold — say, 3%, or some standard deviation above a moving average — do investors begin piling into inflation hedges. For most of the period since the 1980s, inflation and expectations thereof have stayed below 5%. With such low inflation figures, a strong U.S. dollar, and relatively strong growth, there has been limited motivation to invest in gold as a prophylactic. The correlation between gold and inflation likely only applies conditionally under circumstances of persistently high inflation expectations.

Another likely explanation for the diminishing correlation between gold and inflation since the 1980s lies in the evolving landscape of gold investment options. Before the 2000s, options were limited, with non-institutional investors mainly purchasing physical gold, gold mining stocks, or navigating complex gold futures and options. The introduction of gold ETFs in the early 2000s (SPDR Gold Shares ETF launched in 2004) revolutionized this landscape, providing a simple and liquid way to invest in the asset. This greater accessibility attracted a surge of capital, both retail and institutional, increasing demand and likely contributing to the run-up in gold prices, further diminishing its direct correlation with inflation trends. It’s worth noting the potential parallels to Bitcoin here (ETF bulls rejoice).

Looking Ahead

Many market beliefs are reflexive. While the popular focus on inflation as the source of gold’s outperformance in the 1970s is likely disproportionate and overshadows stronger drivers — geopolitical uncertainty, dollar weakness, and the decline of the gold standard — this popular belief may ultimately be self-fulfilling. Enough belief in gold’s role as an inflation hedge may be enough to make it so.

Nevertheless, a more nuanced reading suggests gold is best regarded as a safe haven asset, with inflation expectations as only one component of that equation. Today’s growing geopolitical uncertainty alone may be enough to propel gold to new highs, even as inflation cools.

We’ll be back in the next weeks with another edition of our Macro Research Bites, this time focused on breaking down inflation drivers.

Historical Precedent

Gold has long been negatively correlated with real interest rates. A study in the mid-2010s, for instance, found the correlation to be as high as -0.82.

The traditional explanation for this phenomenon is that when real rates are positive, the opportunity cost to hold gold is high because it doesn’t pay any dividends or interest. However, under lower rate conditions, capital flows out of yield-bearing assets like bonds and into assets like gold.

Recent data, however, shows a break from this pattern. Since 2022, the price of gold and (inverted) real interest rates have increasingly diverged.

Visualized another way, it’s clear to see that as real yields fall (candlesticks), gold prices increase (orange line).

The area shaded red highlights the current situation, where we would expect gold to have sold off much more under the usual strong correlation.

This change of pattern begs the question — has the relationship between gold and real rates broken? If so, why?

Potential Explanations

We’ll posit a few potential explanations for gold’s remarkable continued performance despite the rapid rise in real rates over the last two years.

1. Central banks are buying gold

2022 saw central banks making the highest net gold purchases in over 70 years. Emerging economies — Russia, India, China, Turkey and others — made up a majority of these purchases.

Gold has long been viewed as safe haven asset during turbulent times. In a world faced with increasing geopolitical uncertainty, demand for gold rises.

2. Inflation expectations remain high despite rate hikes

Central banks are not the only buyers propping up demand for gold.

Despite the meteoric rise in rates, inflation expectations have not declined as quickly. Given that many consider gold an inflation hedge, investors may be continuing to hold on to gold through rate hikes in the hopes of protecting against slowly declining inflation, or, worse, a resurgence in inflation akin to what took place in the latter half of the 1970s. However, as we’ll cover in a future blog post, gold may not be quite the inflation hedge many seem to think.

3. Forward-looking investors expect declining rates

The now consensus view that Central Banks are nearing the end of the tightening cycle suggests a forward-looking investor should begin accumulating gold, with the expectation that soon-to-decline real yields will drive new inflows (assuming the historically inverse relationship between gold and real rates returns).

Elevated real yields’ domino effect on global economies and their credit conditions may serve as a further catalyst for concluding the tightening cycle, and soon. The BOJ, for instance, is already suspected to have intervened several times to try to protect the yen.

4. The commodities supercycle

Equities and commodities alternate leading the market in approximately 18-year cycles (see e.g. Bannister and Forward), in line with deflationary and inflationary cycles. At present, we are nearly 15 years into a deflationary/equity-leading cycle beginning in 2009; see chart below.

Geopolitical turmoil, supply chain and transport disruptions, chaotic attempts at transitioning from fossil fuels — and the uneven changes to demand for oil, natural gas, and metals like Lithium and Nickel, critical to battery production — may accelerate this transition to a new commodities supercycle. Persistent gold holders may be betting on this shift.

What Next?

If gold fails to return to its historically inverse correlation to real rates, we may in retrospect point to one of the major shifts above as the cause.

We’ll continue our focus on gold next week, with a piece on the asset as an inflation hedge (hint: conventional wisdom here may be wrong).

Finally, whether you’re a gold bug or an oil aficionado, there’s now a place for you to trade these assets on-chain (last hint: look no further!).

We’re thrilled to announce that Ostium Labs has raised $3.5M from General Catalyst and LocalGlobe, with participation from Susquehanna International Group (SIG), DeFi Alliance, Balaji Srinivasan, Shiliang Tang (LedgerPrime), and nearly 20 additional founders, investors, and advisors. This funding will facilitate the launch of our flagship protocol, the first decentralized, non-custodial perpetuals exchange for Real World Assets.

Market Paradigm Shift

Our launch comes at a critical time in financial markets. Unprecedented growth in the monetary base, supply chain bottlenecks, and rapid geopolitical change since 2020 drove inflation to 40-year highs. Central banks then surprised markets with a meteoric rise in interest rates, crushing tech, growth, and crypto markets and blowing out yields on government debt.

As a result, long-held relationships between asset classes have broken down. Traditional diversification strategies like the 60/40 stock and bond portfolio, for instance, suffered a 16% loss in 2022. Commodity and currency prices are increasingly unstable.

We are seeing a paradigm shift.

Accelerating Diversification

Unsurprisingly, investors are increasingly diversifying into alternative asset classes.

This is true both on- and off-chain. In traditional markets, consumer trading of institutionally-dominated commodities is through the roof, with 90% YoY volume growth in micro contracts for metals and crude oil last year. Niche alternatives like investable fine wines and fractional Private Equity are seeing traction as well.

On-chain, Real World Assets are growing rapidly and the term has become a catch-all for anything diversifying. Demand for tokenized treasuries and equities has soared five-fold and ten-fold respectively year-to-date. Both on- and off-chain traders are adapting their exposure to the changing macro environment.

Accelerating Real-World DeFi Adoption

The vast majority of these on-chain Real World Asset solutions are geared towards long-term holders, rather than traders. Yet if there’s one area of crypto with Product Market Fit, it’s trading. The growth in perpetual futures volume the last 18 months has been exponential.

The market lacks a dedicated platform for trading Real World Assets in a format already familiar to DeFi power users, catering to the increasing demand for on-chain exposure to diversifying bets on macro beyond crypto.

Enter Ostium

We’re building to bridge this gap, through the first decentralized perpetuals DEX purpose-engineered for Real World Assets, starting with commodities and FX.

For the first time, both the market demand and technical infrastructure exists to enable these financial primitives. At a high level, the V1 of our flagship protocol is:

• Stablecoin settled, for both LPs and traders
• Oracle based, using Chainlink’s newly announced low-latency Data Streams
• Margin enabled, with substantial leverage across asset classes
• Designed to minimize LP directional risk, aided by our risk management partnership with Chaos Labs:
  - Hyperbolic funding rates
  - Skew-dependent opening fees
  - Capital efficient counterparty pool
• Unbiased and disintermediated:
  - Fully on-chain settlement and matching (no off-chain sequencer)
  - Outsourced triggering of automated orders using Chainlink Automations 2.0 (no in-house keeper system)

We’re thrilled to announce our public testnet rollout on Arbitrum this quarter and mainnet launch early next year, pending audits. Join us and sign up for our testnet here!

Who We Are

Our founders met at Harvard in a freshman economics class before both working briefly at Bridgewater Associates. We first bonded over our shared experience competing in high-stakes environments, Marco as an International Physics and Math Olympiad medalist, and Kaledora in a previous career as a professional ballerina with the Royal Danish Ballet.

We began trading together across both DeFi and traditional markets in college. The more time we spent trading, the more we became frustrated with the cost, friction, and attention split needed to manage both on- and off-chain trades across asset classes. If blockchains are the future of financial infrastructure, why are traders using the blockchain for settlement limited to placing bets on crypto, rather than the full suite of assets they might want to trade? We envisioned Ostium to solve this problem and facilitate seamless on-chain diversification.

“Blockchain-based networks are seeking to transform the core infrastructure of the financial world through reduced transaction costs, improved settlement times, and composability. We believe that new financial primitives like Ostium will contribute to the much-needed upgrade of the system for all financial assets. Ostium is focused on enabling a greater set of assets, beyond crypto-native tokens, to be traded on-chain from FX to commodities. We are excited to share in the company’s values and partner.” — Nick van Eck, Partner, and Kyle Doherty, Managing Director at General Catalyst

“In the last century, we’ve seen vast growth in access to both financial and tangible assets. However, consumer access to and transparency in tangible assets still lags far behind, leading to reduced trading volumes and higher fees. We believe tangible, or “real-world” assets, are overdue for a technological upgrade. Blockchains are uniquely poised to facilitate this transition by enhancing transparency, facilitating fractionalization, and democratizing access. Ostium’s brilliant team and their ambition to become the leading on-chain platform for democratizing tangible asset investments resonates deeply with the foundational principles of web3. We’re thrilled about Ostium’s potential to cultivate access to a truly novel suite of on-chain assets and democratize consumer access to long underserved asset classes.” — Ash Arora, Partner at LocalGlobe

Our shared love of macro and first-hand experience of the underserved and antiquated commodities markets in particular has been a key driving force behind our protocol vision from day one.

Further Reading

We’re releasing more of our in-house research in the coming weeks, but sharing some of our longer-form threads below for additional reading.

Theses

• Real World Assets are more than tokenization
• Real World Assets for traders

Trading Tales

• George Soros: the man who broke the Bank of England
• The Hunt Brothers: Silver Thursday and cornering the silver market
• John Paulson: betting on the collapse of the US housing market
• Druckenmiller: the man turning a 30% YoY return for 30+ years
• Long Term Capital Management: the spectacular rise and fall of LTCM and the birth of modern bail-outs

Market Events

• LME Nickel Squeeze: who benefited, and who didn’t
• Negative Oil: the day the oil market crashed
• Macro Recap

Ostium & Chaos Labs: Advancing DEX Innovation

We’re thrilled to announce our partnership with Chaos Labs! We’ll be working together closely to improve our mechanism design and create a risk modeling and monitoring system for the Ostium Protocol. Our foremost priority will be to ensure the system’s robust and secure functioning to bridge the gap between on-chain trading and off-chain asset offerings.

Research, Robustness & Economic Security

A core component of our collaboration is the research phase, which is currently underway. This phase will focus on refining the Ostium Protocol’s mechanism design, particularly evaluating the fee structure and its influence on user behavior. Through agent-based simulations, Chaos Labs will provide insights into the system dynamics and potential areas of improvement. In addition, the Chaos team will help ensure the protocol design properly accounts for the unique dynamics and features of Real World Assets to maximize economic security.

Our Vision for Real-World Assets

Currently, “Real World Assets” are synonymous with tokenization in DeFi, focusing on offering yield-maximizing solutions to long-term holders rather than catering to the needs of traders. As capital flows onto blockchain rails, traders increasingly seek diversification and exposure to a growing universe of assets. Yet, from forex pairs to commodities, most diversifying assets in the traditional markets are inaccessible on-chain. Further, despite the volatile macro environment’s increasing influence on market dynamics — from geopolitical uncertainty to evolving interest rates and inflation — on-chain traders have little opportunity to express their macroeconomic views outside directional bets on large-cap crypto. On-chain exposure to a diverse set of Real World Assets will allow traders to build a more diversified portfolio and express granular views on the macro developments that shape markets.

Beyond that, many traditional markets Ostium will soon offer on-chain are plagued by centralization, inefficiency, and a lack of immutability and accessibility. The team believes tangible or “real-world” assets are overdue for a technological upgrade. Blockchains are uniquely poised to facilitate this transition by enhancing transparency, facilitating fractionalization, and democratizing access. Both teams strongly believe that long-term market activity for the vast majority of asset classes will transition on-chain — and are building towards that future.

User-Centric Focus, Risk Management & Tooling

After the initial research phase, the core of our work together as a team will focus on building a risk monitoring system and simulation platform to track core protocol metrics in real time. This risk portal will be publicly accessible, allowing anyone to simulate market events like large drawdowns in asset pricing and assess their potential impact on the protocol. The simulation platform will power real-time risk parameter recommendations in response to fluctuating market conditions and trader behavior. We’re excited to increase protocol transparency and equip Ostium’s community of users with critical tooling.

Our partnership with Chaos Labs is a testament to our commitment to accelerating the future of on-chain trading. We’ll be sharing more news and research soon!

More about Ostium Labs

Please visit Ostium Labs’ website to learn more about the Ostium Protocol and our Real World Asset-focused DEX.

To read Chaos Labs’ version of this announcement, please visit their blog.

As we continue development towards the launch of our testnet in Q4 on Arbitrum, we are striving to find our most dedicated community members who share our passion for the mission of bringing Real World Asset perpetuals on-chain.

We ran a successful paper trading competition in May, with nearly 300 active wallet addresses and over 2,500 USDC in prizes awarded. We have now launched our campaign with Zealy here to continue our goal of growing community involvement.

If you are already a member of our Discord, you may have noticed a recent role change. If you aren’t a member, join us!

Beginning today, all verified members on our Discord have been dubbed ‘Soybean’. Soybeans are high in protein and native to Eastern Asia. They were first traded on the CBOT in 1936 and are the leading agricultural export from the United States. We hope you’re as excited as we are about soybeans, but there are more fun commodity roles to come…

As a special incentive to our Zealy questers, we are announcing the top 5 on the leaderboard will secure a yet more coveted commodity related role in our discord: ‘Juicer.’ The term is derived from another one of our favorite commodities, Frozen Concentrate Orange Juice futures. Juicers are the first elevated role in our Discord, and a step towards rewarding engagement from our most passionate community members. Juicers will benefit from:

• Exclusive access to discussions with the Ostium team
• Auto whitelisting for upcoming trading competitions and product tests
• Discounts on future Ostium merchandise
• First consideration for moving into more senior roles (coming soon)
• + more

In addition to the Juicer role for the top 5 on the Zealy quest, we are giving away the following:

• 50 USDC to the follower who ranks #1 on the Zealy leaderboard.
• 50 USDC to 1 follower chosen at random who retweets our original quest announcement tweet here.
• 50 USDC to the follower who writes the best thread about why RWA perpetuals are ready for prime time in crypto.

For more context on the thread, we are looking for someone to articulate the key market trends & developments that support why we’re building Ostium. While most RWA protocols are focused on the underlying infrastructure layer for tokenization and on-chain access to off-chain yield, Ostium is instead focused on building the end user application that will enable broad access to these assets directly. For more information, see our pinned thread here which contains some of the benefits (no copying — only original content will be rewarded!).

We have lots of exciting updates in the pipeline. Whether you are a new trader looking to learn about lean hog futures or an experienced commodity and forex trading veteran, we want you to be part of our community and grow with us!

To keep up to date, check out our aggregated socials here.

See you on the trading floor!

Ostium Labs Team

Calling all Ostiumites!

We’re running a two-week quest on @zealy_io to help find our most engaged community members and build excitement about Real World Asset perps on-chain. Read on for more background on our mission or skip to the bottom for instructions on how to participate.

Where we are and why we’re here

As we continue to build towards our Testnet launch this summer, we need your help to share our excitement about why we believe RWAs are finally ready for prime time. As capital flows on to blockchain rails — and stays there through market cycles — traders are increasingly seeking diversification and exposure to a growing universe of assets on crypto rails.

Yet from forex pairs to commodities, most diversifying assets in the traditional markets are inaccessible on-chain. Further, as volatile macro conditions — from geopolitical uncertainty to changing interest rates and inflation across jurisdictions — play an increasingly important role in market performance, on-chain traders have little opportunity to make macro-based bets outside of directional bets on large-cap crypto. A greater diversity in asset classes will allow traders to both build a more diversified portfolio and place more granular bets on the macro developments that shape markets.

Beyond that, many of the traditional markets we’ll soon be offering on-chain are currently plagued by centralization, inefficiency, and a lack of immutability (and the risk for manipulation and trade reversion by insiders this poses; see our most recent thread on Nickel here). We strongly believe that long-term, the vast majority of market activity will move on-chain — and are building towards that future.

At Ostium, we’re thrilled to be building a platform that gives traders the ability to build their portfolio of assets in one place on-chain: permissionlessly, efficiently, and self-custodially.

Participate in our Zealy quest

We need your help to spread the word about Real World Asset trading on-chain.

To enter our Zealy quest, follow the link here. Participants have 2 weeks to rack up XP points for a chance to win a [redacted🤐] prize.

For all Zealy quest-related questions, please use the dedicated discord channel titled #zealy-questions.

Good luck — see you on the trading floor! 📈

Ostium Labs

To give early supporters a first look at our product, we’re launching a paper trading competition beginning Monday, May 8th. The competition will take place over a period of three days. Only limited whitelist spots are available; please fill out this short form to claim a spot if you haven’t already.

📆 Sign-Up and Competition Period

Monday May 8th, 2023 13:00 UTC to Thursday May 11th, 2023 13:00 UTC

• Check start times in your time zone here.
• Register here to claim your spot.
• Applicants will be notified of their eligibility and whitelisting status latest by Sunday, May 7th via message through their provided means of contact (Discord, Telegram, Email) and provided with a link to the app. App password gating will be lifted at the start of the competition at 13:00 UTC on Monday, May 8th.

Note: Your public wallet address will appear on our leaderboard.

🏅 Prizes

Participants will be eligible for prizes based on PnL percentage performance. Additional prizes will be awarded to those supplying actionable feedback on the platform through the interface’s “give feedback” function on the bottom right portion of the screen.

Prizes will be airdropped based on Profit and Loss percentage ranking at the time of competition’s end. Winners within Cross and Isolated margin will be ranked separately, e.g. if Alice places 1st in Isolated Margin and 3rd in Cross Margin, she will receive one first place and one third place prize. For all intents and purposes, Cross and Isolated can be seen as separate competitions: all three prizes will be distributed to winners in each of the two categories. Both longs and shorts across all trading pairs count towards performance. Prize amounts below will be paid out in USDC on Arbitrum.

PnL percentage performance prizes:

1st place: 500 USDC

2nd place: 250 USDC

3rd place: 125 USDC

Feedback prizes:

1st place: 100 USDC

2nd place: 75 USDC

3rd place: 50 USDC

Honourable mentions and supplementary prizes of 25–50 USDC will be awarded to 5–20 additional providers of actionable, quality feedback not placing in the top 3.

If a participant provides multiple pieces of feedback from the same wallet address, aggregate feedback will be taken into account in determining ranking. Please be sure to copy your wallet address into the feedback form box — direct wallet address detection for our feedback module isn’t yet live. Only feedback provided via the on-screen give feedback button will be taken into consideration.

Prizes are designed to reward top traders and engaged early community members. Participation in the protocol’s first paper trading competition will be heavily taken into account and rewarded when determining whitelisting status for future product releases (e.g. gated testnet).

📌 Summary and Disclaimers

• The top three traders ranked by PnL percentage performance during the competition period will be awarded prizes. Isolated and Cross Margin performance will be analysed separately; performance in one domain does not affect performance in the other.
• The top three providers of feedback will receive prizes. Excellent feedback will be rewarded through additional bounties, even if a participant does not place in one of the top three spots. Rewards are merit-based.
• Prizes will be distributed within 14 days of the competition’s end. Please allow several days for feedback ranking and analysis.
• Ostium reserves the right to modify competition period, reward distribution, activity and any other related matters at its sole discretion, for technical reasons or otherwise.
• Ostium is not available to OFAC-sanctioned individuals or groups. Citizens or residents of the following jurisdictions are not permitted to use Ostium’s interface: Myanmar (Burma), Côte D’Ivoire (Ivory Coast), Cuba, Crimea and Sevastopol, Democratic Republic of Congo, Iran, Iraq, Libya, Mali, Nicaragua, Democratic People’s Republic of Korea (North Korea), Somalia, Sudan, Syria, the U.S., Yemen, Zimbabwe or any other state, country or region that is included in the Sanction Lists.