Study evaluates cryptocurrency option pricing models, finds Kou and Bates models perform best.
problem High volatility and low liquidity in cryptocurrency futures contracts make traditional option pricing models unreliable.
method Calibrated and evaluated the performance of six option pricing models (Black-Scholes, Merton Jump Diffusion, Variance Gamma, Kou, Heston, and Bates) on BTC and ETH futures options.
result Kou and Bates models achieve the lowest pricing errors, with Kou outperforming Bates for BTC and ETH options respectively.
Study proposes pricing mechanism for cryptocurrency options.
problem High speculation, volatility, and discontinuity in cryptocurrency markets.
method Proposes a pricing mechanism based on SVCJ model with co-jumps.
result Shows significant contemporaneous anti-correlation between jumps in price and volatility.
The paper models cryptocurrency price and volatility with jumps and fractional volatility.
problem Empirical evidence shows jumps in cryptocurrency price and volatility.
method Fractional stochastic volatility model with jumps and short-term volatility dependency.
result Fractional stochastic volatility models outperform other models in pricing and hedging cryptocurrency options.
Study the hedging of cryptocurrency options in a volatile market.
problem Hedging options in a volatile, non-stationary cryptocurrency market.
method Calibrated to SVI-implied volatility surfaces, Monte Carlo price paths generated using SVCJ, GARCH, and historical data. Delta, Delta-Gamma, Delta-Vega, and Minimum Variance strategies applied. Wide range of market models tested.
result Calibration results indicate stochastic volatility, low jump frequency, and infinite activity. Short-dated options less sensitive to volatility or Gamma hedges; longer-dated options benefit from multiple-instrument hedges.
The paper solves a pricing problem for a multiple reset put option using integral equations.
problem Valuation of a multiple reset put option with reset rights.
method Formulated as a multiple optimal stopping problem, reduced to single optimal stopping problems, solved by induction and integral equations.
result Characterized optimal reset boundaries as solutions to nonlinear integral equations and derived reset premium representations.
New method calibrates crypto option prices more robustly.
problem Large bid-ask spreads and missing quotes in crypto markets.
method Designs a novel calibration procedure for crypto options.
result Calibration is more robust and accurate than standard methods.
Model predicts jump risk premia influencing cryptocurrency futures and option performance.
problem Capturing asymmetric and time-varying skewness in cryptocurrency returns.
method Bivariate Hawkes process with positive and negative jump premia.
result Inferred jump risk premia predict futures cost of carry and option performance.
Proposes NDIG model to capture bitcoin volatility and option pricing.
problem Capturing the volatility and option pricing of cryptocurrency Bitcoin.
method Doubly subordinated Levy process (NDIG) to model Bitcoin time series properties.
result NDIG model perfectly captures observed in-sample volatility.
Study compares volatility models for Bitcoin, finds GARCH and EGARCH outperform.
problem Evaluating which volatility models best predict Bitcoin spot and option prices.
method Used HIST, EMA ARCH, GARCH, and EGARCH models on Bitcoin spot price series.
result GARCH and EGARCH models outperform other models in both in-sample and out-of-sample forecasts.
Paper examines floating exercise boundaries for American options in time-inhomogeneous models.
problem Floating exercise boundaries in time-inhomogeneous models with negative interest rates or yields.
method Semi-analytical approach for pricing American options.
result Specialized pricing methodologies are required for models with floating exercise boundaries.
The atomic swap protocol allows for the exchange of cryptocurrencies on different blockchains without the need to trust a third-party. However, market participants who desire to hold derivative assets such as options or futures would also benefit from trustless exchange. In this paper I propose the atomic swaption, whi…
In recent years cryptocurrency trading has captured the attention of practitioners and academics. The volume of the exchange with standard currencies has known a dramatic increasing of late. This paper addresses to the need of models describing a bitcoin-US dollar exchange dynamic and their use to evaluate European opt…
Despite being described as a medium of exchange, cryptocurrencies do not have the typical attributes of a medium of exchange. Consequently, cryptocurrencies are more appropriately described as crypto assets. A common investment attribute shared by the more than 2,500 crypto assets is that they are highly volatile. An i…
This work analyzes impermanent loss in decentralized markets and provides a hedging strategy.
problem Impermanent loss in automated market makers (AMMs).
method Analytical derivation of a static replication formula using European options, and numerical example with real data.
result Guaranteed hedging coverage for all final prices within a predefined interval.
Study uses neural networks to value Bitcoin options considering price jumps and sentiment.
problem Valuing Bitcoin options under price jumps and market sentiment.
method Bivariate jump-diffusion model, incorporating Google search sentiment, and artificial neural networks.
result Derives a closed formula for Bitcoin option pricing and validates using high-volatile stocks.
Hydrodynamics principles applied to finance, solving complex market models.
problem Understanding financial market dynamics using physics principles.
method Unified mathematical framework using Kelvin waves to solve differential and pseudo-differential equations.
result Solved various financial models including volatility and variance swaps.
This study compares three volatility metrics for Bitcoin, highlighting high expected volatility.
problem Understanding Bitcoin's volatility in financial markets.
method Historical volatility, forecasted volatility (GARCH models), and implied volatility (from options market).
result High expected volatility across all methodologies, especially implied volatility.
Study examines cryptocurrency risk spillover effects before and after pandemic.
problem Analyzing risk propagation among cryptocurrencies during extreme events.
method Asymmetric breakpoint approach and network analysis.
result Cryptocurrency risk spillover effect increased during pandemic.
The paper uses AI to analyze on-chain parameters and identify risky cryptocurrencies.
problem Identifying risky cryptocurrencies and understanding their price factors.
method Historical data analysis, AI algorithms, clustering, classification.
result A significant negative correlation between cryptocurrency price and maximum and total supply, and a weak positive correlation with 24-hour trading volume.
Prediction markets and crypto options show persistent pricing gaps.
problem Comparing prediction markets and crypto options for identical payoffs.
method Comparing Polymarket Yes prices with Binance call option prices.
result Mean pricing gap of 5.6 percentage points across 214 hourly observations.
Cryptocurrencies show similarities to traditional markets but also have unique characteristics.
problem Understanding the investment potential and characteristics of cryptocurrencies.
method Organized stylized facts and analyzed through empirical asset pricing.
result Cryptocurrencies exhibit similarities to traditional markets but also have distinct characteristics.
This paper surveys cryptocurrency trading research, covering various aspects.
problem Understanding the unique nature and behavior of cryptocurrencies as assets.
method Comprehensive review of 146 research papers on cryptocurrency trading.
result Identifies promising open opportunities in cryptocurrency trading.
Study analyzes cryptocurrency market complexity, comparing it to traditional markets.
problem Understanding the dynamics and characteristics of cryptocurrency markets.
method Statistical physics methods and analysis of price fluctuations.
result Cryptocurrency market exhibits complexity similar to traditional markets but with slower information flow.
Study shows cryptocurrency investor base affects volatility.
problem Investor base changes impact cryptocurrency volatility.
method Proxying investor base with subreddit follower changes, analyzed idiosyncratic volatility.
result Changes in cryptocurrency investor base significantly increase idiosyncratic volatility.
TDA improves cryptocurrency portfolio management.
problem Traditional methods fail to manage cryptocurrencies effectively.
method Topological Data Analysis (TDA) for identifying investment opportunities.
result TDA-based portfolio management outperforms traditional methods.
Cryptocurrencies use blockchain tech for secure transactions, offering new research opportunities.
problem Misunderstanding of cryptocurrency technology and lack of empirical data.
method Analyzing detailed transaction data and summarizing statistics.
result Opportunity for academic research in financial economics.
Clusters cryptocurrency market states via cross correlation analysis.
problem Analyse cryptocurrency market dynamics.
method Cross correlation structure analysis over 5 years.
result Cryptocurrency market clusters into 4 states.
Investigates if adding cryptocurrencies to German portfolios diversifies better, finding mixed results.
problem Improving diversification in German investor portfolios using cryptocurrencies.
method Portfolio analysis with descriptive statistics, graphical methods, and econometric spanning tests, using a customized EWCI.
result Cryptocurrencies can improve diversification in some windows but not as a normal case.
DBNs predict cryptocurrency price directions by uncovering causal relationships.
problem Predicting cryptocurrency price movements due to volatility and external factors.
method Dynamic Bayesian Networks (DBN) approach to identify causal relationships among features.
result DBN significantly outperforms baseline models in predicting cryptocurrency prices.
Transformer predicts Ethereum prices using cross-currency correlation and sentiment analysis.
problem Predicting Ethereum cryptocurrency prices with limited data.
method Transformer-based neural network with cross-currency correlation and sentiment analysis.
result Transformer model outperforms other models on some parameters.
Trend following in cryptocurrencies yields high returns, similar to commodities.
problem Investing in cryptocurrencies using trend following strategies.
method A decade of data analysis on cryptocurrency markets and trend following strategies.
result Cryptocurrencies offer strong returns and diversification against traditional equities.
Optimizes cryptocurrency portfolios using MNTS GARCH model.
problem Optimizing cryptocurrency portfolios with non-Gaussian return dynamics.
method Multivariate normal tempered stable (MNTS) GARCH model for non-Gaussian returns, Foster-Hart risk optimization.
result Foster-Hart optimization yields a more profitable portfolio with better risk-return balance.
Cryptocurrency and NFT prices are highly correlated, mirroring historical bubbles.
problem Evaluating the wealth effect of cryptocurrency prices on real estate.
method Exploiting metaverse LAND and cryptocurrencies to track correlations and causality.
result Cryptocurrency prices Granger cause NFT LAND prices, similar to historical bubbles.
Thousands of cryptocurrencies have been issued and publicly exchanged since Bitcoin was invented in 2008. The total cryptocurrency market value exceeds 300 billion US dollars as of 2019. This paper analyzes the prices, volumes, blockchain transactions, coin difficulties and public opinion popularities of 3607 actively …
This study examines non-performing assets and cryptocurrencies in Japan.
problem Economic downturn led to non-performing loans, affecting financial institutions.
method Literature analysis to summarize development, issuance, supervision, etc.
result Cryptocurrencies are being regulated in Japan despite non-performing loans.
GRF models predict cryptocurrency VaR better than other methods.
problem Predicting Value at Risk (VaR) for volatile cryptocurrencies.
method Generalized Random Forests (GRF) adapted for quantile prediction.
result GRF models outperform other methods in cryptocurrency VaR predictions.
Cryptocurrencies evolve through survival of the fittest, modeled with evolutionary finance.
problem Understanding the dynamics of cryptocurrency markets.
method Evolutionary finance concepts applied to toy models of cryptocurrency data.
result Survival of the fittest in cryptofinance is explained through scaling laws.
This study attempts to analyze patterns in cryptocurrency markets using a special type of deep neural networks, namely a convolutional autoencoder. The method extracts the dominant features of market behavior and classifies the 40 studied cryptocurrencies into several classes for twelve 6-month periods starting from 15…
Investigate the evolving structure of cryptocurrency interactions using high-frequency returns.
problem Evolution of cryptocurrency interactions
method Construct directed and weighted networks from Granger causal relationships between cryptocurrency log-returns.
result Normalized returns exhibit heavy-tailed distributions.
A time-varying network reveals community structure in cryptocurrencies.
problem Investing in cryptocurrencies from different communities can diversify risk.
method Dynamic covariate-assisted spectral clustering method.
result Investors can earn 1.08% daily return by diversifying across communities.
Cryptocurrencies have heavy-tailed return distributions, requiring diversification.
problem Cryptocurrency returns do not follow Gaussian distributions.
method Applied econophysics and entropy measures to analyze returns.
result Portfolio diversification reduces return uncertainty.
Network analysis reveals changing cryptocurrency market leaders.
problem Understanding evolving cryptocurrency market leaders and their influence.
method Hourly-resolution data and Kendall's Tau correlation for network analysis.
result Pearson's correlation underestimates market dynamics; FTT and FTX were key during the 2021 bull run.
The cryptocurrency market surpassed the barrier of \$100 billion market capitalization in June 2017, after months of steady growth. Despite its increasing relevance in the financial world, however, a comprehensive analysis of the whole system is still lacking, as most studies have focused exclusively on the behaviour o…
Cryptocurrency time-series predictability is low, resembling Brownian noise.
problem Low predictability of cryptocurrency exchange rates.
method Complexity and model predictions of Litecoin, Binance Coin, Bitcoin, Ethereum, and XRP exchange rates.
result Simpler models outperform complex ones in cryptocurrency forecasting.
We study the problem of predicting whether the price of the 21 most popular cryptocurrencies (according to coinmarketcap.com) will go up or down on day d, using data up to day d-1. Our C2P2 algorithm is the first algorithm to consider the fact that the price of a cryptocurrency c might depend not only on historical pri…
CryptoGAT improves cryptocurrency price prediction by treating it as a graph problem.
problem Cryptocurrency price prediction challenges due to extreme volatility.
method CryptoGAT, a Graph Attention Network, redefines cryptocurrency prediction as a cross-asset graph problem.
result CryptoGAT outperforms state-of-the-art methods in cryptocurrency price prediction.
We investigate the behaviour of cryptocurrencies using data for bitcoin, ethereum and ripple which account for over 70% of the cryptocurrency market. We demonstrate that α-stable distribution is an appropriately sufficient model for highly speculative cryptocurrencies which outperforms other heavy tailed distribution…
Cryptocurrencies are becoming more linked in their returns and volatilities.
problem Understanding the increasing interconnectivity of cryptocurrencies.
method Examined market linkages using returns and volatilities, applied various methodologies.
result Significant increase in market linkages for both returns and volatilities.