This paper sets out to provide a general framework for the pricing of average-type options via lower and upper bounds. This class of options includes Asian, basket and options on the volume-weighted average price. We demonstrate that in cases under discussion lower bounds allow for the dimensionality of the problem to …
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48 results for “Volume weighted average price”
The paper modifies asset pricing models using Taylor series expansions and market-based averages.
problem Improving asset pricing models to better reflect market dynamics.
method Derives new pricing equations using Taylor series expansions and market-based averages.
result New expressions for asset prices and volatilities derived from market data.
We study the problem of optimal execution of a trading order under Volume Weighted Average Price (VWAP) benchmark, from the point of view of a risk-averse broker. The problem consists in minimizing mean-variance of the slippage, with quadratic transaction costs. We devise multiple ways to solve it, in particular we stu…
Modeling price-mediated contagion in financial systems with capital requirements.
problem Understanding and quantifying the cost of capital requirements on financial stability.
method Developed a two-tier pricing structure and conditions for clearing prices, providing sensitivity analysis.
result Quantified the cost of regulation and value of bailouts in financial systems.
Volume weighted average price (VWAP) options are a popular security type in many countries, but despite their popularity very few pricing models have been developed so far for VWAP options. This can be explained by the fact that the VWAP pricing problem is set in an incomplete market since there is no underlying with w…
Market-based asset price probability depends on trade volumes and values, improving forecasts and reliability.
problem Limited accuracy of frequency-based asset price statistical moments.
method Derive market-based variance and 3rd statistical moment from trade values and volumes, accounting for trade volume randomness.
result Market-based statistical moments improve price probability forecasts and reliability.
Optimal energy trading strategy for intraday markets using Hawkes processes.
problem Optimal execution in intraday energy markets with specific trading patterns.
method Calibrated Hawkes process model with transient price impact.
result Substantial cost reductions in TWAP and VWAP benchmarks.
In this short note, we study an optimization problem of expected implementation shortfall (IS) cost under general shaped market impact functions. In particular, we find that an optimal strategy is a VWAP (volume weighted average price) execution strategy when the market model is a Black-Scholes type with stochastic clo…
Optimizes large stock order execution with LSTM neural networks.
problem Minimizing transaction costs in large stock order execution.
method Trained LSTM neural network to minimize transaction costs.
result LSTM strategy outperforms TWAP and VWAP strategies.
Market Impact Paradoxesq-fin.TR
The market impact (MI) of Volume Weighted Average Price (VWAP) orders is a convex function of a trading rate, but most empirical estimates of transaction cost are concave functions. How is this possible? We show that isochronic (constant trading time) MI is slightly convex, and isochoric (constant trading volume) MI is…
In this study, we introduce an explicit trading-volume process into the Almgren-Chriss model, which is a standard model for optimal execution. We propose a penalization method for deriving a verification theorem for an adaptive optimization problem. We also discuss the optimality of the volume-weighted average-price st…
TT-DAC-PS: Twin-Target Deterministic Actor-Critic with Policy Smoothing for Optimal Trade Executioncs.AI
TT-DAC-PS: A deterministic actor-critic approach for optimal trade execution
problem Optimal execution of large stock sell programs
method Twin-Target Deterministic Actor-Critic with Policy Smoothing
result Reduces mean implementation shortfall percentage
The volume weighted average price (VWAP) execution strategy is well known and widely used in practice. In this study, we explicitly introduce a trading volume process into the Almgren-Chriss model, which is a standard model for optimal execution. We then show that the VWAP strategy is the optimal execution strategy for…
Single Curve Collapse of the Price Impact Function for the New York Stock Exchangecond-mat.stat-mech
We study the average price impact of a single trade executed in the NYSE. After appropriate averaging and rescaling, the data for the 1000 most highly capitalized stocks collapse onto a single function, giving average price shift as a function of trade size. This function increases as a power that is the order of 1/2 f…
Study predicts intraday stock trading volume using ML models.
problem Predicting intraday trading volumes in equity markets.
method Used machine learning models with HF predictors.
result Intraday stock trading volume is highly predictable.
Deep Reinforcement Learning for Optimum Order Execution: Mitigating Risk and Maximizing Returnsq-fin.CP
Paper uses DRL to optimize trade execution, outperforming VWAP and TWAP.
problem Optimizing returns while minimizing risk in order execution.
method Deep Reinforcement Learning (DRL) for holistic optimization.
result DRL-based approach outperforms VWAP and TWAP in ROI and risk management.
The paper explores how market trade values and volumes affect price and return statistics.
problem Understanding the statistical properties of market trade, price, and return.
method Introduces secondary averaging procedure to describe statistical moments of market trades, price, and return.
result Predictions of market-based probabilities of price and return are limited by Gaussian distributions.
We present a formulation of the transaction cost analysis (TCA) in the Bayesian framework for the primary purpose of comparing broker algorithms using standardized benchmarks. Our formulation allows effective calculation of the expected value of trading benchmarks with only a finite sample of data relevant to practical…
Enhances trading signals using image analysis and weighted moving averages.
problem Improving price trend trading strategies in financial markets.
method Image-induced importance weights applied to weighted moving averages of trading signals.
result Significant enhancement of price trend trading signals with improved portfolio selection.
On the origin of power law tails in price fluctuationscond-mat.stat-mech
In a recent Nature paper, Gabaix et al. \cite{Gabaix03} presented a theory to explain the power law tail of price fluctuations. The main points of their theory are that volume fluctuations, which have a power law tail with exponent roughly -1.5, are modulated by the average market impact function, which describes the r…
The paper proposes a new order slicing strategy to reduce market impact in large-volume trading.
problem Significant market impact and slippage in large-volume trading.
method Volatility-volume-based order slicing strategy using Exponential Weighted Moving Average and Markov Chain Monte Carlo simulations.
result Improves trade execution efficiency and reduces market impact.
Introduces a new price measure and a second-order economic theory for volatility forecasting.
problem Forecasting price volatility in financial markets.
method Develops a new price measure and a second-order economic theory to model price volatility.
result Shows that second-order economic theory improves forecasting of price volatility.
A Volume-Price-Adjusted MACD Trading Strategy with Sensitivity Calibration for U.S. Equity Indicesq-fin.TR
Study improves MACD trading strategy with volume and price adjustments.
problem Signal lag and false signals in traditional MACD trading rules.
method Develops VP-MACD framework with sensitivity calibration.
result Proposed framework outperforms baseline MACD in profitability and risk-adjusted return.
We solve the problem of optimal liquidation with volume weighted average price (VWAP) benchmark when the market impact is linear and transient. Our setting is indeed more general as it considers the case when the trading interval is not necessarily coincident with the benchmark interval: Implementation Shortfall and Ta…
Improved financial predictions with OHLC data and timestamps.
problem Improving VWAP predictions in financial markets.
method Investigated the impact of timing features on machine learning models for VWAP prediction.
result Incorporating timing features consistently improves predictive performance across multiple ML architectures.
We introduce a microscopic model for the dynamics of the order book to study how the lack of liquidity influences price fluctuations. We use the average density of the stored orders (granularity ) as a proxy for liquidity. This leads to a Price Impact Surface which depends on both volume and . The dependence …
MPC framework reduces execution costs and schedule deviations in trading.
problem Executing large orders in markets under time and liquidity constraints.
method Model Predictive Control (MPC) framework balancing order completion, market impact, and opportunity cost.
result Significant reductions in slippage and schedule shortfall compared to benchmarks.
The study reveals traders' risk aversion and a new risk premium from market volumes.
problem Understanding traders' rationality and risk aversion from market volumes.
method Optimal Merton dynamics model to estimate average risk aversion and price of risk.
result Validation of the proposed trading strategy model on real data.
Detecting Crypto Pump-and-Dump Schemes: A Thresholding-Based Approach to Handling Market Noiseq-fin.ST
Detects crypto pump-and-dump schemes with a thresholding-based model.
problem Detecting genuine anomalies from minor trading fluctuations.
method Combining threshold-based criteria with EWMA and volatility measures.
result Balances high true-positive detection with minimal noise.
Equity auctions show linear price impact up to a large volume, then non-linear.
problem Understanding price impact in equity auctions.
method High-quality data analysis of Paris stock exchange auctions.
result Price impact is linear up to a large volume, then becomes non-linear.
New moving average adapts weight dynamically based on polynomial and wavefunction.
problem Lagging traditional moving averages in adjusting to changes in data.
method Develops a moving average with weight as a polynomial of a wavefunction from an eigenproblem.
result Immediate 'switch' without lag, adapting to changes in data.
The Subtle Interplay between Square-root Impact, Order Imbalance & Volatility: A Unifying Frameworkq-fin.TR
New framework explains market volatility and metaorder impact.
problem Reconciling contradictory observations in market microstructure.
method Introducing a new theoretical framework to describe metaorders with different signs, sizes, and durations.
result Price diffusion is ensured by long memory of cross-correlations between metaorders.
The paper extends the market price of risk for electricity swap contracts, incorporating jump risk.
problem Pricing electricity swap contracts with consideration of jump risk.
method Introducing a Merton type model with jumps and transferring to the physical measure, comparing arithmetic and geometric averaging.
result A decomposition of swap's market price of risk into classical and market price of risk components.
Pipeline for comparing trading algorithms in finance and crypto.
problem Disconnected research and applications in algorithmic trading.
method General pipeline for designing, programming, and evaluating trading strategies.
result Systematic comparison of trading algorithms in finance and crypto.
Microscopic Understanding of Cross-Responses between Stocks: a Two-Component Price Impact Modelq-fin.TR
We construct a price impact model between stocks in a correlated market. For the price change of a given stock induced by the short-run liquidity of this stock itself and of the information about other stocks, we introduce a self- and a cross-impact function of the time lag. We model the average cross-response function…
The paper introduces a new price model based on entropy that better fits high-frequency market data.
problem Understanding fair prices in high-frequency markets with bid-ask imbalance.
method A parametrized family of prices derived from the Maximum Entropy Principle, minimizing bias given volume imbalance.
result The model can generate higher kurtosis and heavy-tailed distributions compared to standard models.
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.…
RL agents optimize order execution in a realistic market simulation.
problem Optimal order execution challenges in a complex market.
method Multi-agent RL in a historical order book simulation.
result RL agents converge to TWAP strategies in some scenarios.
Study proposes deep learning for VWAP execution in crypto markets, outperforming traditional methods.
problem Challenges in achieving VWAP due to dynamic volume and price factors.
method Direct optimization of VWAP execution using deep learning, bypassing volume curve prediction.
result Deep learning approach consistently achieves lower VWAP slippage in volatile markets.
In order to understand the origin of stock price jumps, we cross-correlate high-frequency time series of stock returns with different news feeds. We find that neither idiosyncratic news nor market wide news can explain the frequency and amplitude of price jumps. We find that the volatility patterns around jumps and aro…
The paper explores features from orderbooks to improve intraday electricity price forecasting.
problem Improving probabilistic forecasting of intraday electricity prices.
method Extracted 384 features from orderbooks, selected powerful features, and benchmarked models across two countries and product types.
result Revealed an asymmetric generalization phenomenon in electricity price forecasting models.
We study the statistical properties of the recurrence intervals between successive trading volumes exceeding a certain threshold . The recurrence interval analysis is carried out for the 20 liquid Chinese stocks covering a period from January 2000 to May 2009, and two Chinese indices from January 2003 to April 2…
Paper uses Transformers to predict intraday volume ratio with high accuracy.
problem Accurate prediction of intraday volume ratio for VWAP strategies.
method Transformer architecture with log-normal transformation and external features.
result Probabilistic forecasting captures mean and standard deviation of volume ratios.
We develop a theory for the market impact of large trading orders, which we call metaorders because they are typically split into small pieces and executed incrementally. Market impact is empirically observed to be a concave function of metaorder size, i.e., the impact per share of large metaorders is smaller than that…
A new VWAP execution method using transformer and signature features.
problem Asset-specific model training and complex temporal dependencies.
method Combining transformer-based design with path signatures for capturing geometric features.
result GFT-Sig model achieves superior performance in VWAP loss metrics.
Study of the forecasting models using large scale microblog discussions and the search behavior data can provide a good insight for better understanding the market movements. In this work we collected a dataset of 2 million tweets and search volume index (SVI from Google) for a period of June 2010 to September 2011. We…
Deep learning optimizes VWAP strategy for lower transaction costs.
problem Designing an efficient VWAP strategy for dynamic markets.
method Hierarchical deep reinforcement learning (Macro-Meta-Micro Trader).
result Our approach achieves an average cost saving of 1.16 base points.
This research develops a dual-level reinforcement learning strategy to track daily VWAP accurately.
problem Inaccurate tracking of daily VWAP due to short trading horizons.
method Dual-level architecture using Transformer and LSTM models.
result Improves accuracy in approximating daily VWAP compared to previous models.