Price gap, defined as the logarithmic price difference between the first two occupied price levels on the same side of a limit order book (LOB), is a key determinant of market depth, which is one of the dimensions of liquidity. However, the properties of price gaps have not been thoroughly studied due to the less avail…
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48 results for “Price Gaps”
Gap Risk KVA and Repo Pricing: An Economic Capital Approach in the Black-Scholes-Merton Frameworkq-fin.PR
Although not a formal pricing consideration, gap risk or hedging errors are the norm of derivatives businesses. Starting with the gap risk during a margin period of risk of a repurchase agreement (repo), this article extends the Black-Scholes-Merton option pricing framework by introducing a reserve capital approach to …
Study shows cooperation can reduce investment risk and price gaps.
problem Investment risk and price gaps in cooperative markets.
method Introduced Collective Arbitrage and Collective Super-replication, established asset pricing theorems.
result Reduction of price intervals through collective super-replication.
Do Prediction Markets Match Option Prices? Bitcoin Threshold Evidence from Binance and Polymarketq-fin.TR
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.
What really causes large price changes?cond-mat.other
We study the cause of large fluctuations in prices in the London Stock Exchange. This is done at the microscopic level of individual events, where an event is the placement or cancellation of an order to buy or sell. We show that price fluctuations caused by individual market orders are essentially independent of the v…
We obtain option pricing formulas for stock price models in which the drift and volatility terms are functionals of a continuous history of the stock prices. That is, the stock dynamics follows a nonlinear stochastic functional differential equation. A model with full memory is obtained via approximation through a stoc…
"Digitwashing": The Gap between Words and Deeds in Digital Transformation and Stock Price Crash Riskq-fin.ST
Digitwashing gap boosts stock crash risk, study finds.
problem The gap between companies' digital promises and actual performance increases stock crash risk.
method Empirical analysis of Shanghai and Shenzhen A-share companies from 2010 to 2021, robustness tests conducted.
result GDT significantly increases stock price crash risk, confirmed by robust tests.
This paper develops a pricing model for data assets from the buyer's perspective.
problem Insufficient research on pricing data assets from the buyer's perspective.
method Develops a pricing model based on the informational value of data assets from the buyer's perspective, using an implicit function derived from value functions in investment-consumption problems under ambiguity markets.
result Derives general expressions and explicit pricing formulas for data assets under various conditions.
Determinants of immediate price impacts at the trade level in an emerging order-driven marketq-fin.ST
The common wisdom argues that, in general, large trades cause large price changes, while small trades cause small price changes. However, for extremely large price changes, the trade size and news play a minor role, while the liquidity (especially price gaps on the limit order book) is a more influencing factor. Hence,…
Constant Proportion Portfolio Insurance (CPPI) is a strategy designed to give participation in a risky asset while protecting the invested capital. Some gap risk due to extreme events is often kept by the issuer of the product: a put option on the CPPI strategy is included in the product. In this paper we present a new…
Data-driven Option Pricingq-fin.PR
Data-driven method for option pricing using historical asset prices.
problem Tackling the gap between historical asset prices and risk-neutral option pricing.
method Identifying a pricing kernel process, solving utility maximization and functional optimization problems using deep learning.
result Demonstrated the efficiency of the data-driven option pricing methodology.
We present the Integrated Size and Price Optimization Problem (ISPO) for a fashion discounter with many branches. Based on a two-stage stochastic programming model with recourse, we develop an exact algorithm and a production-compliant heuristic that produces small optimality gaps. In a field study we show that a distr…
Paper proposes a method to robustly estimate volatility from OTM options.
problem Accurately measuring volatility in real-world markets with limited option trading.
method Constructs an arbitrage-free continuous option pricing function from bid-ask spreads of OTM options.
result Robustly calculates volatility indices with theoretical consistency, even in low-liquidity markets.
Perpetual futures offer leverage without maturity, with prices influenced by funding rates.
problem Understanding and pricing perpetual futures with funding rates.
method Derive no-arbitrage prices and bounds in markets with trading costs. Empirically analyze deviations and Sharpe ratios of implied arbitrage strategies.
result Implied arbitrage strategies in crypto markets yield high Sharpe ratios, indicating significant pricing inefficiencies.
The paper studies pricing of insurance products focusing on the pricing of annuities under uncertainty. This pricing problem is crucial for financial decision making and was studied intensively, however, many open questions still remain. In particular, there is a so-called "annuity puzzle" related to certain inconsiste…
New framework uses trading volume instead of volatility for stock pricing.
problem Improving stock price dynamics understanding and market data gap.
method Proposes a new stock pricing model using trading volume instead of volatility, based on two hypotheses.
result The new framework can be applied to option pricing and points to a new direction in finance.
Study examines pricing strategies in competitive supply chains with discrete prices.
problem Inaccurate assumptions in traditional SC models for pricing decisions.
method Examines a SC model with one supplier and two manufacturers, considering customer demand segmentation and discrete price setting.
result Nash equilibria among manufacturers are not unique, and low denomination factors can lead to instability.
New algorithm reduces pricing error by a factor of T^2/3.
problem Optimal pricing under non-Lipschitz demand with unknown jumps and atoms.
method Conservative-Markdown Redirect-UCB Pricing, combining estimation, probing, and redirection.
result Achieves optimal regret of O(T^2/3), matching lower bounds.
Study reveals a hidden cost in derivatives markets through option-implied discount factors.
problem The hidden cost in derivatives markets, not visible in price space.
method Minute-level NBBO data on options, reduced-form specification linking carry gap to implementation risk, trading frictions, and financial conditions.
result An annualized carry gap exists, linked to implementation risk and financial conditions.
The importance of considering the volumes to analyze stock prices movements can be considered as a well-accepted practice in the financial area. However, when we look at the scientific production in this field, we still cannot find a unified model that includes volume and price variations for stock assessment purposes.…
We performed a comprehensive analysis on the price bounds of CDO tranche options, and illustrated that the CDO tranche option prices can be effectively bounded by the joint distribution of default time (JDDT) from a default time copula. Systemic and idiosyncratic factors beyond the JDDT only contribute a limited amount…
In this paper, we study the stochastic version of the one-sided full information bandit problem, where we have arms , and playing arm would gain reward from an unknown distribution for arm while obtaining reward feedback for all arms . One-sided full information bandit ca…
Unified framework for fair pricing in long-term insurance products.
problem Unclear generalization of fair pricing methods to long-term products.
method Reformulate multi-state transition models as Poisson regression problems.
result Direct application of existing fair pricing methods to long-term insurance products.
This paper extends a Kyle model to include price-responsive traders, revealing new dynamics and equilibria.
problem Real-world market dynamics involve price-responsive traders, affecting market equilibrium and insider profits.
method Developed a continuous-time Kyle model with two types of price-responsive traders (momentum and contrarian), leading to a forward-backward Riccati system for equilibrium.
result The model shows that feedback effects can lead to multiple equilibria and amplify price informativeness.
Generalizes insider trading model to multiple assets.
problem Modeling informed trading in a multi-asset context.
method Formulated an infinite-dimensional Bayesian trading game.
result Obtained a parsimonious equilibrium with closed-form solutions.
A new multi-factor model improves commodity pricing accuracy.
problem Enhancing accuracy in commodity pricing by integrating multiple risk factors.
method A four-factor model using Kalman filter for simultaneous estimation and state variable filtering.
result The four-factor model outperforms existing models in capturing futures term structures and crude oil pricing.
Study optimal auction formats for maximizing MEV on Ethereum.
problem Maximizing extractable value from Ethereum auctions.
method Empirical analysis of 2.2 million transactions, modeling affiliation among bidders.
result English and second-price sealed-bid auctions dominate other formats, with significant revenue losses.
LARP filters data to protect model performance across various learners.
problem Protecting model accuracy in public datasets with diverse learners.
method Formalizes and analyzes LARP, a robust data prefiltering method.
result LARP provides guarantees on worst-case loss over a set of learners, with some performance trade-off.
The Heston model is validated for option pricing using theoretical derivations and empirical market data.
problem Validating the Heston model for accurate option pricing.
method Theoretical derivations and empirical validations using Monte Carlo simulations and machine learning.
result The Heston model is robust and relevant for current financial markets.
Over the past few years, the futures market has been successfully developing in the North-West region. Futures markets are one of the most effective and liquid-visible trading mechanisms. A large number of buyers are forced to compete with each other and raise their prices. A large number of sellers make them reduce pr…
We provide analytical tools for pricing power options with exotic features (capped or log payoffs, gap options ...) in the framework of exponential Lévy models driven by one-sided stable or tempered stable processes. Pricing formulas take the form of fast converging series of powers of the log-forward moneyness and of …
Motivated by the desire to bridge the gap between the microscopic description of price formation (agent-based modeling) and the stochastic differential equations approach used classically to describe price evolution at macroscopic time scales, we present a mathematical study of the order book as a multidimensional cont…
Examines how central bank policies affect stock markets and asset prices.
problem Understanding the impact of monetary policy on stock markets and asset prices.
method Used Taylor rule equations to analyze data from 1990 to 2020 for US and UK, testing with various econometric methods.
result Monetary policy can explain asset price volatility and output gap better than just inflation rate.
Study stability of contingent claim solutions under probabilistic perturbations.
problem Stability of solutions to discrete-time contingent-claim problems under uncertainty.
method Use Rockafellian perturbations to analyze stability of solutions.
result Establishes convergence of dual problems and shadow prices.
In the present paper we fill an essential gap in the Convertible Bonds pricing world by deriving a Binary Tree based model for valuation subject to credit risk. This model belongs to the framework known as Equity to Credit Risk. We show that this model converges in continuous time to the model developed by Ayache, Fors…
In this paper we investigate model-independent bounds for exotic options written on a risky asset. Based on arguments from the theory of Monge-Kantorovich mass-transport we establish a dual version of the problem that has a natural financial interpretation in terms of semi-static hedging. In particular we prove that th…
Robust pricing and hedging under trading restrictions and the emergence of local martingale modelsq-fin.MF
We consider the pricing of derivatives in a setting with trading restrictions, but without any probabilistic assumptions on the underlying model, in discrete and continuous time. In particular, we assume that European put or call options are traded at certain maturities, and the forward price implied by these option pr…
The distribution of returns in financial time series exhibits heavy tails. In empirical studies, it has been found that gaps between the orders in the order book lead to large price shifts and thereby to these heavy tails. We set up an agent based model to study this issue and, in particular, how the gaps in the order …
A limit order book provides information on available limit order prices and their volumes. Based on these quantities, we give an empirical result on the relationship between the bid-ask liquidity balance and trade sign and we show that liquidity balance on best bid/best ask is quite informative for predicting the futur…
Novel approach predicts long-term seasonal component of electricity prices for improved forecasting.
problem Improving day-ahead electricity price forecasting accuracy.
method Extracts trend-seasonal pattern from extrapolated price series using autoregressive and LASSO models.
result Improves predictive accuracy by 3-15% in root mean squared error and 1% in profits.
The liberalization of electricity markets and the development of renewable energy sources has led to new challenges for decision makers. These challenges are accompanied by an increasing uncertainty about future electricity price movements. The increasing amount of papers, which aim to model and predict electricity pri…
TSFMs outperform traditional models in electricity price forecasting.
problem Accurate electricity price forecasting for effective decision-making.
method Benchmarking several TSFMs against traditional models using real-world data.
result MSTL model consistently outperforms TSFMs across countries and metrics.
New method for pricing American options in time-dependent models, improving accuracy and efficiency.
problem Pricing American options in time-dependent models with improved accuracy and efficiency.
method Semi-analytical pricing using a nonlinear Volterra integral equation and numerical methods.
result Improved accuracy and efficiency in pricing American options compared to forward finite difference solvers.
Paper shows re-solving heuristics have constant regret for price-based revenue management.
problem Optimal pricing policies for revenue management with time constraints.
method Proves re-solving heuristics have regret compared to optimal policies.
result Improved regret bound to from , complemented by gap with fluid model.
Modeling insider trading with transaction costs and fair pricing.
problem Maximizing profits for an informed trader in a market with transaction costs and fair pricing.
method Single auction model and continuous time analogue, analyzing equilibrium behavior with polynomial and differential equations.
result Equilibrium trading strategy and pricing rules are affected by transaction costs, revealing an information gap.
FinBERT-BiLSTM predicts cryptocurrency prices using sentiment analysis.
problem Predicting volatile cryptocurrency market prices.
method Hybrid model combining Bi-LSTM and FinBERT for sentiment analysis.
result Enhanced forecasting accuracy for volatile financial markets.
Study models weather index insurance pricing by insurers and farmers, finding flexible pricing kernels boost profits.
problem Monopoly pricing of weather index insurance with risk and flexibility considerations.
method Bowley-type sequential game with insurer and farmer, using neural networks for farmer's payoff.
result Flexible pricing kernels increase insurer profits closer to indemnity insurance levels.
New approach estimates vehicle and component prices without teardowns.
problem Uncertainty in traditional vehicle price estimation methods.
method Data-driven machine learning and game theory.
result Shows large gaps in manufacturer pricing strategies.