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0111 · Mar 201919922001200920172026
9 results for altcoins

We give an algorithm and source code for a cryptoasset statistical arbitrage alpha based on a mean-reversion effect driven by the leading momentum factor in cryptoasset returns discussed in https://ssrn.com/abstract=3245641. Using empirical data, we identify the cross-section of cryptoassets for which this altcoin-Bitc…

2019-03-13abs ↗pdf ↗

Study compares altcoins to Bitcoin, analyzing their features and market performance.

problem Comparing altcoins to Bitcoin to understand market performance and features.
method Used Google Trend data, price, volume, and market capitalization data from coinmarketcap.com.
result Features of Litecoin, Zcash, Bitcoin Cash, Ethereum, and Bitcoin Gold affect market performance and user preferences.

Proposes C2RM to mine cross-cryptocurrency relationships for better Bitcoin price prediction.

problem Limited consideration of historical relationships and interactions between cryptocurrencies for Bitcoin price prediction.
method C2RM module using Dynamic Time Warping for lead-lag relationship extraction and aggregation.
result Improves existing price prediction methods by significant performance improvement.

dYdX updates liquidity provider incentives to enhance trading efficiency.

problem Incentivizing liquidity providers to maintain efficient market structures.
method Analyzed various metrics (makerVolume, depths, spreads) and used historical trades to update the LP Incentives Programme.
result Updated the LP Incentives Programme to encourage more active and efficient liquidity.

Estimates crypto risk premia using hidden factors and finds significant integration with traditional markets.

problem Estimating risk premia in cryptocurrency returns.
method Giglio-Xiu (2021) three-pass approach, controlling for latent factors and non-tradable state variables.
result Latent factors significantly impact crypto returns, highlighting the importance of controlling for unobserved risks.

Kalshi prediction markets forecast cryptocurrency volatility through monetary policy and inflation signals.

problem Forecasting cryptocurrency volatility using prediction markets.
method Monetary policy and inflation signals from Kalshi prediction markets.
result Signals from Kalshi prediction markets predict cryptocurrency volatility with statistical significance.

Backtesting framework for CLMMs on Uniswap V3 reduces reward estimation error.

problem Estimating rewards for CLMMs in Uniswap V3 liquidity pools.
method Parametric model for liquidity distribution, historical data analysis.
result Error in reward estimation less than 1% for each pool.