Numerical Simulation of Exchange Option with Finite Liquidity:
Controlled Variate Model
- URL: http://arxiv.org/abs/2006.07771v1
- Date: Sun, 14 Jun 2020 02:19:18 GMT
- Title: Numerical Simulation of Exchange Option with Finite Liquidity:
Controlled Variate Model
- Authors: Kevin S. Zhang and Traian A. Pirvu
- Abstract summary: Trading in our market model has a direct impact on the asset's price.
Two-dimensional Milstein scheme is implemented to simulate the pair of assets prices.
Time complexity of these numerical schemes is included.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper we develop numerical pricing methodologies for European style
Exchange Options written on a pair of correlated assets, in a market with
finite liquidity. In contrast to the standard multi-asset Black-Scholes
framework, trading in our market model has a direct impact on the asset's
price. The price impact is incorporated into the dynamics of the first asset
through a specific trading strategy, as in large trader liquidity model.
Two-dimensional Milstein scheme is implemented to simulate the pair of assets
prices. The option value is numerically estimated by Monte Carlo with the
Margrabe option as controlled variate. Time complexity of these numerical
schemes are included. Finally, we provide a deep learning framework to
implement this model effectively in a production environment.
Related papers
- Modelling financial volume curves with hierarchical Poisson processes [0.8624680612413765]
A common strategy is to trade a desired quantity across many orders in line with the expected volume curve throughout the day.
We introduce a hierarchical Poisson process model for the intensity functions of admixtures of inhomogenous Poisson processes, which represent the trading times of the stock throughout the day.
We demonstrate the method on datasets from the Trade and Quote repository maintained by Wharton Research Data Services.
arXiv Detail & Related papers (2024-06-01T12:03:57Z) - Deep Hedging with Market Impact [0.20482269513546458]
We propose a novel general market impact dynamic hedging model based on Deep Reinforcement Learning (DRL)
The optimal policy obtained from the DRL model is analysed using several option hedging simulations and compared to commonly used procedures such as delta hedging.
arXiv Detail & Related papers (2024-02-20T19:08:24Z) - COPlanner: Plan to Roll Out Conservatively but to Explore Optimistically
for Model-Based RL [50.385005413810084]
Dyna-style model-based reinforcement learning contains two phases: model rollouts to generate sample for policy learning and real environment exploration.
$textttCOPlanner$ is a planning-driven framework for model-based methods to address the inaccurately learned dynamics model problem.
arXiv Detail & Related papers (2023-10-11T06:10:07Z) - Diffusion Variational Autoencoder for Tackling Stochasticity in
Multi-Step Regression Stock Price Prediction [54.21695754082441]
Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility.
Current solutions to multi-step stock price prediction are mostly designed for single-step, classification-based predictions.
We combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction.
Our model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance.
arXiv Detail & Related papers (2023-08-18T16:21:15Z) - Quantum computational finance: martingale asset pricing for incomplete
markets [69.73491758935712]
We show that a variety of quantum techniques can be applied to the pricing problem in finance.
We discuss three different methods that are distinct from previous works.
arXiv Detail & Related papers (2022-09-19T09:22:01Z) - Regime-based Implied Stochastic Volatility Model for Crypto Option
Pricing [0.0]
Existing methodologies fail to cope with the volatile nature of the emerging Digital Assets (DAs)
We leverage recent advances in market regime (MR) clustering with the Implied volatility Model (ISVM)
ISVM can incorporate investor expectations in each of the sentiment-driven periods by using implied volatility (IV) data.
We demonstrate that MR-ISVM contributes to overcome the burden of complex adaption to jumps in higher order characteristics of option pricing models.
arXiv Detail & Related papers (2022-08-15T15:31:42Z) - Estimating risks of option books using neural-SDE market models [6.319314191226118]
We use an arbitrage-free neural-SDE market model to produce realistic scenarios for the joint dynamics of multiple European options on a single underlying.
We show that our models are more computationally efficient and accurate for evaluating the Value-at-Risk (VaR) of option portfolios, with better coverage performance and less procyclicality than standard filtered historical simulation approaches.
arXiv Detail & Related papers (2022-02-15T02:39:42Z) - Multi-Asset Spot and Option Market Simulation [52.77024349608834]
We construct realistic spot and equity option market simulators for a single underlying on the basis of normalizing flows.
We leverage the conditional invertibility property of normalizing flows and introduce a scalable method to calibrate the joint distribution of a set of independent simulators.
arXiv Detail & Related papers (2021-12-13T17:34:28Z) - Universal Trading for Order Execution with Oracle Policy Distillation [99.57416828489568]
We propose a novel universal trading policy optimization framework to bridge the gap between the noisy yet imperfect market states and the optimal action sequences for order execution.
We show that our framework can better guide the learning of the common policy towards practically optimal execution by an oracle teacher with perfect information.
arXiv Detail & Related papers (2021-01-28T05:52:18Z) - Stock2Vec: A Hybrid Deep Learning Framework for Stock Market Prediction
with Representation Learning and Temporal Convolutional Network [71.25144476293507]
We have proposed to develop a global hybrid deep learning framework to predict the daily prices in the stock market.
With representation learning, we derived an embedding called Stock2Vec, which gives us insight for the relationship among different stocks.
Our hybrid framework integrates both advantages and achieves better performance on the stock price prediction task than several popular benchmarked models.
arXiv Detail & Related papers (2020-09-29T22:54:30Z) - TPLVM: Portfolio Construction by Student's $t$-process Latent Variable
Model [3.5408022972081694]
We propose the Student's $t$-process latent variable model (TPLVM) to describe non-Gaussian fluctuations of financial timeseries by lower dimensional latent variables.
By comparing these portfolios, we confirm the proposed portfolio outperforms that of the existing Gaussian process latent variable model.
arXiv Detail & Related papers (2020-01-29T02:02:02Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.