A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Unified model for market dynamics, linking price and order flow.
Efficient method for pricing European and American options using Markov switching stochastic volatility model.
SHHK Stock Connect increases A-H share price premium, more for less efficient markets.
At the ultra high frequency level, the notion of price of an asset is very ambiguous. Indeed, many different prices can be defined (last traded price, best bid price, mid price,...). Thus, in practice, market participants face the problem of choosing a price when implementing their strategies. In this work, we propose …
We construct an utility-based dynamic asset pricing model for a limit order market. The price is nonlinear in volume and subject to market impact. We solve an optimal hedging problem under the market impact and derive the dynamics of the efficient price, that is, the asset price when a representative liquidity demander…
New formula for efficient spread option pricing in copula markets.
Efficient method for lookback option pricing under Markov models.
This study investigates empirically whether the degree of stock market efficiency is related to the prediction power of future price change using the indices of twenty seven stock markets. Efficiency refers to weak-form efficient market hypothesis (EMH) in terms of the information of past price changes. The prediction …
The Hull-White one factor model is used to price interest rate options. The parameters of the model are often calibrated to simple liquid instruments, in particular European swaptions. It is therefore very important to have very efficient pricing formula for simple instruments. Such a formula is proposed here for Europ…
Study on time-zero efficiency of European power derivatives markets using statistical tests and trading rules.
New data improves market impact estimation methods.
Contrary to the common view that exact pricing is prohibitive owing to the curse of dimensionality, this study proposes an efficient and unified method for pricing options under multivariate Black-Scholes-Merton (BSM) models, such as the basket, spread, and Asian options. The option price is expressed as a quadrature i…
This work presents an asset pricing model that under rational expectation equilibrium perspective shows how, depending on risk aversion and noise volatility, a risky-asset has one equilibrium price that differs in term of efficiency: an informational efficient one (similar to Campbell and Kyle (1993)), and another one …
Deep RNNs compute American option prices and deltas efficiently.
Study shows registration regime improves IPO pricing efficiency in China.
Quantum computer method for pricing rainbow options efficiently.
Efficient method for pricing multi-asset options with local volatility.
A new method for pricing European options in changing market conditions.
We propose an efficient lattice procedure which permits to obtain European and American option prices under the Black and Scholes model for digital options with barrier features. Numerical results show the accuracy of the proposed method.
UAMM uses external market prices to improve AMM efficiency and reduce liquidity provider risk.
Framework for pricing waterfall structures using simulation and uncertainty modeling.
Bayesian framework explains price formation with learning and market impact.
The Efficient Market Hypothesis has been a staple of economics research for decades. In particular, weak-form market efficiency -- the notion that past prices cannot predict future performance -- is strongly supported by econometric evidence. In contrast, machine learning algorithms implemented to predict stock price h…
A new deep learning method for option pricing in rough volatility models.
We explore a decomposition in which returns on a large class of portfolios relative to the market depend on a smooth non-negative drift and changes in the asset price distribution. This decomposition is obtained using general continuous semimartingale price representations, and is thus consistent with virtually any ass…
Statistical test rejects market efficiency using entropy from price returns.
Deep learning models predict stock prices with high accuracy and speed.
Unified framework for pricing various debt securities.
We discuss the pricing methodology for Bonus Certificates and Barrier Reverse-Convertible Structured Products. Pricing for a European barrier condition is straightforward for products of both types and depends on an efficient interpolation of observed market option pricing. Pricing products We discuss the pricing metho…
We describe a model for evolving commodity forward prices that incorporates three important dynamics which appear in many commodity markets: mean reversion in spot prices and the resulting Samuelson effect on volatility term structure, decorrelation of moves in different points on the forward curve, and implied volatil…
Global supply networks in agriculture, manufacturing, and services are a defining feature of the modern world. The efficiency and the distribution of surpluses across different parts of these networks depend on choices of intermediaries. This paper conducts price formation experiments with human subjects located in lar…
Photonic chip speeds up option pricing with GAN for financial efficiency.
We present a comprehensive theory of homogeneous volatility (and variance) estimators of arbitrary stochastic processes that fully exploit the OHLC (open, high, low, close) prices. For this, we develop the theory of most efficient point-wise homogeneous OHLC volatility estimators, valid for any price processes. We intr…
AI simplifies trading strategies, potentially making markets more efficient.
We propose a new forward-backward stochastic differential equation solver for high-dimensional derivatives pricing problems by combining deep learning solver with least square regression technique widely used in the least square Monte Carlo method for the valuation of American options. Our numerical experiments demonst…
Efficiently computes robust option prices using multi-marginal martingale transport.
This paper uses neural networks to predict stock prices more accurately.
P-Trees improve investment performance by optimizing the efficient frontier.
This paper develops a model of liquidity provision in financial markets by adapting the Madhavan, Richardson, and Roomans (1997) price formation model to realistic order books with quote discretization and liquidity rebates. We postulate that liquidity providers observe a fundamental price which is continuous, efficien…
The paper tackles auction market design flaws by randomizing closing times and optimizing transaction fees.
This paper uses spectrum analysis to understand price behavior in the Indian stock market.
We solve in closed-form an equilibrium model in which a finite number of exponential investors continuously consume and trade with price-impact. Compared to the analogous Pareto-efficient equilibrium model, price-impact has an amplification effect on risk-sharing distortions that helps resolve the interest rate puzzle …
Tensor networks improve exotic option pricing efficiency.
We study the informational efficiency of a market with a single traded asset. The price initially differs from the fundamental value, about which the agents have noisy private information (which is, on average, correct). A fraction of traders revise their price expectations in each period. The price at which the asset …
A new data-driven model forecasts electricity prices efficiently.
In finance, the weak form of the Efficient Market Hypothesis asserts that historic stock price and volume data cannot inform predictions of future prices. In this paper we show that, to the contrary, future intra-day stock prices could be predicted effectively until 2009. We demonstrate this using two different profita…
Paper uses RL to optimize insurance pricing on PCWs, improving efficiency and adaptability.
This paper examines Bitcoin's price predictability, finding inefficiencies under certain conditions.