Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2604.01431

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

Hardhik Mohanty·Bhaskar Krishnamachari
View original source ↗Read source document ↗Export metadata ↓

Abstract

Daily probability changes in Kalshi macro prediction markets forecast cryptocurrency realized volatility through two distinct channels. The monetary policy channel, measured by Fed rate repricing on KXFED contracts, predicts Bitcoin volatility in sample with t = 3.63 and p < 0.001 but exhibits regime dependence tied to the 2024-2025 rate-cutting cycle. The recession risk signal from KXRECSSNBER proves more stable out of sample, delivering an MSFE ratio of 0.979 with Clark-West p = 0.020. The inflation channel, measured by CPI repricing on KXCPI contracts, predicts altcoin volatility for Ethereum, Solana, Cardano, and Chainlink with t-statistics ranging from -2.1 to -3.4 and out-of-sample gains for Ethereum at MSFE = 0.959 with p = 0.010 and Solana at p = 0.048. Both the Bitcoin--Fed-dovish and Chainlink--CPI specifications survive Benjamini-Hochberg correction at q = 0.05. Orthogonalization and baseline comparisons against Fed Funds futures, Treasury yields, and the Deribit implied volatility index confirm that these signals carry information not embedded in conventional financial instruments. The sample covers ten Kalshi event series and six cryptocurrency assets over January 2023 to March 2026.

Explore related subjects

q-fin.STq-fin.RM

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Hardhik Mohanty, Bhaskar Krishnamachari. 2026-04-01. Do Prediction Markets Forecast Cryptocurrency Volatility? Evidence from Kalshi Macro Contracts. https://arxiv.org/abs/2604.01431

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Causal Discovery in Financial Markets: A Framework for Nonstationary Time-Series Data

This paper introduces a new causal structure learning method for nonstationary time series data, a common data type found in fields such as finance, economics, healthcare, and environmental science. Our work builds upon the constraint-based causal discovery from nonstationary data algorithm (CD-NOD). We introduce a refined version (CDNOTS) which is designed specifically to account for lagged dependencies in time series data. We compare the performance of different algorithmic choices, such as the type of conditional independence test and the significance level, to help select the best hyperparameters given various scenarios of sample size, problem dimensionality, and availability of computational resources. Using the results from the simulated data, we apply CDNOTS to a broad range of real-world financial applications in order to identify causal connections among nonstationary time series data, thereby illustrating applications in factor-based investing, portfolio diversification, and comprehension of market dynamics.

q-fin.ST↗
arXiv2026

Extreme Value Analysis for Finite, Multivariate and Correlated Systems with Finance as an Example

Extreme values and the tail behavior of probability distributions are essential for quantifying and mitigating risk in complex systems of all kinds. In multivariate settings, accounting for correlations is crucial. Although extreme value analysis for infinite correlated systems remains an open challenge, we propose a practical framework for handling a large but finite number of correlated time series. We develop our approach for finance as a concrete example but emphasize its generality. We study the extremal behavior of high-frequency stock returns after rotating them into the eigenbasis of the correlation matrix. This separates and extracts various collective effects, including information on the correlated market as a whole and on correlated sectoral behavior from idiosyncratic features, while allowing us to use univariate tools of extreme value analysis. This holds even for high-frequency data where discretization effects normally complicate analysis. We employ a peaks-over-threshold approach and thereby fully avoid the analysis of block maxima. We estimate the tail shape of the rotated returns while explicitly accounting for nonstationarity, a key feature in finance and many other complex systems. Our framework facilitates tail risk estimation relative to larger trends and intraday seasonalities at both market and sectoral levels.

q-fin.ST↗
arXiv2026

Does Crypto Sentiment Extremity Widen Estimated Spreads? Evidence Depends on the Specification

We examine whether extreme values of the Crypto Fear & Greed Index are associated with a daily high-low spread estimate for Bitcoin. The sample contains 2,896 BTC/USDT observations from February 2018 to January 2026. We find an unconditional extreme-minus-neutral gap of 61.99 basis points. After close-to-close realised-volatility-quintile demeaning it is 24.79 basis points, although none of the five separate quintile contrasts survives Holm correction. With quadratic realised-volatility and strictly lagged momentum controls, the HAC estimate is 11.81 basis points (95% CI [-2.31,25.93], p=.101). A fixed non-parametric stratification gives 20.44 basis points (p=.0195 under circular shifts), while separate models for a zero-floored estimate's incidence and positive magnitude are imprecise. The results therefore show only a descriptive, specification-dependent association. We conclude that they do not establish a stable or causal liquidity premium.

q-fin.ST↗