Do Prediction Markets Forecast Cryptocurrency Volatility? Evidence from Kalshi Macro Contracts

This paper demonstrates that daily probability changes in Kalshi macro prediction markets, specifically those reflecting monetary policy, recession risk, and inflation expectations, provide statistically significant and out-of-sample predictive power for the realized volatility of Bitcoin and various altcoins, offering information distinct from conventional financial instruments.

The Gist

Imagine you are trying to predict how much a rollercoaster is going to shake tomorrow. Usually, you look at the weather, the track condition, or how fast the train was going yesterday. But what if there was a secret group of people betting on why the ride might shake, and their bets gave you a clue before the ride even started?

That is essentially what this paper is about. The researchers, Hardhik Mohanty and Bhaskar Krishnamachari, investigated whether prediction markets (specifically a platform called Kalshi) can predict how "shaky" or volatile cryptocurrency prices will be.

Here is the breakdown in simple terms:

1. The Setup: The "Betting Pool" vs. The "Crypto Rollercoaster"

• The Crypto Rollercoaster: Cryptocurrencies like Bitcoin, Ethereum, and Solana are famous for swinging wildly up and down. This is called "volatility."
• The Prediction Market (Kalshi): This is a legal betting site in the US where people bet on real-world events, like "Will the Federal Reserve cut interest rates?" or "Will inflation go up?"
• The Idea: The researchers asked: If the betting odds on Kalshi start changing rapidly today, does that tell us how crazy the crypto rollercoaster will be over the next five days?

2. The Big Discovery: Different Keys for Different Locks

The most interesting finding is that one size does not fit all. Different cryptocurrencies react to different types of "news," and the betting markets caught this perfectly.

• Bitcoin is the "Big Banker": Bitcoin acts like a giant, institutional asset. It cares mostly about Interest Rates (Monetary Policy).

  • The Analogy: Imagine Bitcoin is a giant ship. If the betting markets start shifting odds that the "Captain" (the Federal Reserve) is going to slow down the ship (cut rates), Bitcoin gets nervous and shakes.
  • The Result: When Kalshi bettors started re-pricing the odds of rate cuts, Bitcoin's volatility spiked. However, this only worked well during the specific time the Fed was actually cutting rates (2024–2025). It's like a weather vane that only works when the wind is blowing from a specific direction.

• Altcoins (Ethereum, Solana, etc.) are the "Retail Shoppers": Smaller coins care more about Inflation (CPI).

  • The Analogy: Imagine these coins are a group of shoppers at a grocery store. They are worried about the price of milk (inflation). If the betting markets show people are suddenly worried about milk prices, these shoppers get jittery.
  • The Twist: Here's the magic part. When the betting markets showed big changes in inflation expectations, the volatility of these smaller coins actually went down the next week.
  • Why? It's the "Uncertainty Resolution" effect. Think of it like waiting for a medical test result. While you are waiting, you are super stressed (high volatility). The moment the result comes out (even if it's bad news), the stress of not knowing disappears, and you calm down. The betting markets were signaling that the "test result" was coming, so the panic subsided.

3. Why This Matters (The "Secret Sauce")

Usually, financial experts look at standard tools to predict volatility, like the VIX (the stock market's fear gauge) or Treasury bond yields.

The researchers proved that Kalshi's betting odds contain "secret information" that these standard tools miss.

• It's like having a weather forecast that doesn't just look at the clouds (standard tools) but also listens to the birds (prediction markets).
• Even after they stripped away all the usual data, the betting signals still predicted the crypto swings. This means the "wisdom of the crowd" on Kalshi is seeing something the big banks aren't.

4. The Catch: It's Not Magic

The paper warns that these signals aren't a crystal ball that works forever.

• Regime Dependence: The "Interest Rate" signal for Bitcoin was a superstar during the 2024–2025 rate-cutting cycle, but it might not work if the economy is in a different mode (like a recession or a rate-hiking cycle).
• The "Uncertainty" Signal: The inflation signal for smaller coins was more stable and reliable over time.

The Bottom Line

This paper tells us that where people are betting on the future matters just as much as what the future actually is.

If you want to know how much Bitcoin will shake, watch the betting markets on Interest Rates.
If you want to know how much Ethereum or Solana will shake, watch the betting markets on Inflation.

By listening to these "betting pools," investors can get a head start on the rollercoaster, knowing exactly which switch (rates or inflation) is about to make the ride bumpy.

Technical Summary

1. Problem Statement

Cryptocurrency markets are characterized by continuous trading, heterogeneous global investor bases, and a lack of institutional anchors, making the link between macroeconomic news and asset volatility difficult to model. While existing literature confirms that scheduled macro announcements (e.g., CPI, Fed meetings) impact crypto volatility, there is a significant gap in continuous, real-time measurement of macro uncertainty between these scheduled events. Conventional "surprise" measures are only available on announcement days, leaving a blind spot regarding how market expectations shift daily.

The paper investigates whether daily probability changes in KALSHI prediction markets can fill this gap. Specifically, it asks:

1. Do Kalshi signals contain predictive information for cryptocurrency realized volatility that is orthogonal to conventional financial instruments (e.g., Fed Funds futures, Treasury yields)?
2. Which macroeconomic channels (monetary policy, inflation, recession risk) drive volatility for specific cryptocurrency assets?

2. Methodology

Data Construction

• Prediction Market Signals: The authors utilize data from ten Kalshi event contract series (CFTC-regulated) covering January 2023 to March 2026. Key series include:
  • KXFED: Federal Reserve rate decisions.
  • KXCPI: Consumer Price Index prints.
  • KXRECSSNBER: NBER recession determinations.
  • Signal Definition: The primary signal is the absolute volume-weighted daily change in contract prices ($\Delta p_{s,t}$). This measures the magnitude of macro repricing regardless of direction. Directional signals (e.g., "Fed-dovish") are also constructed.
• Cryptocurrency Data: Daily closing prices for six assets (Bitcoin, Ethereum, Solana, Cardano, Avalanche, Chainlink) from CoinGecko.
• Dependent Variable: Five-day forward Realized Volatility (RVol), calculated as the annualized standard deviation of log returns over the next 5 days.
• Controls: CBOE VIX, DXY index returns, S&P 500 returns, and conventional macro proxies (Fed Funds futures changes, 10-year Treasury yields).

Empirical Framework

The study employs a Heterogeneous Autoregressive (HAR) model as the baseline, augmented with market controls and Kalshi signals:

1. Baseline (M1): Standard HAR model (daily, weekly, monthly lags of absolute returns).
2. Augmented (M2): Adds market controls (VIX, DXY, S&P 500).
3. Augmented (M3): Adds the lagged Kalshi signal ($\Delta p_{s,t-1}$).

Inference & Testing:

• Standard Errors: Newey–West HAC standard errors (5 lags) to account for overlapping 5-day windows.
• Multiple Testing: Benjamini–Hochberg False Discovery Rate (FDR) correction applied across 60 signal-asset combinations ($q=0.05$).
• Out-of-Sample (OOS) Evaluation: Expanding-window scheme (initial 120 days). Performance is measured using the Clark–West (CW) statistic for nested model comparison, Mean Squared Forecast Error (MSFE) ratios, and $R^2_{OOS}$.
• Robustness: Includes orthogonalization (regressing Kalshi signals against conventional instruments to isolate unique information), lead-lag tests, block bootstrapping, and alternative volatility measures (GARCH, log-RVol).

3. Key Contributions

1. Novel Data Source: Introduces regulated prediction market prices (Kalshi) as a continuous, high-frequency proxy for macro uncertainty, distinct from traditional financial instruments.
2. Channel Specificity: Demonstrates that different cryptocurrencies react to different macro channels, challenging the "one-size-fits-all" risk-off narrative.
3. Orthogonality: Proves that Kalshi signals contain information not embedded in Fed Funds futures, Treasury yields, or crypto-specific implied volatility indices (Deribit DVOL).
4. Regime Dependence: Identifies that the predictive power of monetary policy signals is highly dependent on the macroeconomic regime (specifically active rate-cutting cycles).

4. Key Results

A. Channel Specificity (In-Sample)

• Bitcoin (BTC): Volatility is driven by the Monetary Policy channel. The "Fed-dovish" signal (expectations of rate cuts) significantly predicts higher BTC volatility ($t=3.63, p<0.001$).
• Altcoins (ETH, SOL, ADA, LINK): Volatility is driven by the Inflation (CPI) channel. Large absolute changes in CPI probability predictions predict lower next-week volatility for these assets ($t$-stats ranging from $-2.1$ to $-3.4$). This suggests an "uncertainty resolution" effect where the release of CPI data resolves ambiguity, calming altcoin markets.
• Recession Risk: The NBER recession signal ($KXRECSSNBER$) is a significant predictor for Bitcoin ($t=-2.76$) but not for altcoins.

B. Out-of-Sample Performance

• Bitcoin: The "Fed-dovish" signal fails to deliver OOS gains over the full window due to regime dependence (it performed well during the 2024–2025 rate-cutting cycle but reversed afterward). However, the Recession Risk signal provides robust OOS gains ($MSFE = 0.979, p=0.020$).
• Altcoins: The CPI signal delivers consistent OOS gains for Ethereum ($MSFE=0.959, p=0.010$) and Solana ($MSFE=0.983, p=0.048$).
• Significance: After Benjamini–Hochberg correction, two specifications remain significant: Bitcoin–Fed-dovish and Chainlink–CPI.

C. Orthogonality and Robustness

• Unique Information: Orthogonalization exercises show that 97.7% of the variation in the Fed-dovish signal and 92.5% of the CPI signal is orthogonal to Fed Funds futures, Treasury yields, and VIX.
• Lead-Lag Tests: For the Fed-dovish signal, the lagged signal is significant while the lead is not, ruling out reverse causality. For CPI, both lag and lead are significant, likely due to the multi-day nature of inflation repricing around BLS release dates, but this does not imply reverse causality.
• Comparison with Deribit: Kalshi signals outperform the Deribit Bitcoin Volatility Index (DVOL) in predicting realized volatility, indicating Kalshi captures macro-driven information missing from crypto options markets.

5. Significance and Implications

• For Risk Management: The findings suggest that volatility-managed portfolios can improve position sizing by incorporating prediction market signals. For example, a high "Fed-dovish" signal predicts a ~7% increase in Bitcoin volatility, warranting a reduction in position size.
• For Options Pricing: The negative correlation between CPI signal magnitude and altcoin volatility implies that options markets may systematically overprice straddles following large CPI events, creating potential arbitrage opportunities (short straddles).
• Theoretical Insight: The results support the hypothesis that Bitcoin behaves like a macro asset sensitive to dollar liquidity and rate expectations (institutional behavior), while Altcoins are more sensitive to inflation regime uncertainty (retail behavior).
• Limitations: The monetary policy channel exhibits strong regime dependence; its utility is concentrated during active rate-repricing cycles. The sample period (2023–2026) includes high-volatility macro events, and generalizability to calmer environments requires further study.

Conclusion

The paper establishes that Kalshi prediction markets provide a continuous, high-frequency signal of macro uncertainty that significantly forecasts cryptocurrency realized volatility. Crucially, these signals offer information orthogonal to traditional financial instruments, with distinct transmission channels: Monetary Policy drives Bitcoin volatility, while Inflation expectations drive Altcoin volatility. This offers a new framework for forecasting crypto risk that moves beyond standard autoregressive models.