arXiv research

Optimal trading strategy with unobservable pricing errors for co-integrated assets.

Introduces an unobservable intrinsic electricity price to link storage theory with risk premium.

iTimER learns from reconstruction errors to represent irregularly sampled time series.

This paper examines a heterogeneous beliefs model in which there is a process that is only partially observed by the agents. The economy contains a risky asset producing dividends continuously in time. The dividends are observed by the agents. The dividends are assumed to be a known function of some other unobserved pr…

New method recovers predictions from unobservable source subpopulation in binary classification.

Adapts causal inference for high-dimensional treatments like text strings.

The paper tackles robust domain generalization by accounting for unobserved confounders.

Study tackles causal structure learning in linear models with unobserved variables and measurement error.

Study uses a bivariate model to price crude oil futures.

We estimate prices of exotic options in a discrete-time model-free setting when the trader has access to market prices of a rich enough class of exotic and vanilla options. This is achieved by estimating an unobservable quantity called "implied expected signature" from such market prices, which are used to price other …

We use the expectation of the range of an arithmetic Brownian motion and the method of moments on the daily high, low, opening and closing prices to estimate the volatility of the stock price. The daily price jump at the opening is considered to be the result of the unobserved evolution of an after-hours virtual tradin…

We tackle causal discovery in linear systems with measurement error and unobserved causes.

A one-factor asset pricing model with an Ornstein--Uhlenbeck process as its state variable is studied under partial information: the mean-reverting level and the mean-reverting speed parameters are modeled as hidden/unobservable stochastic variables. No-arbitrage pricing formulas for derivative securities written on a …

CDVAE estimates treatment effects over time by accounting for unobserved variables.

Develops methods for dynamic pricing in incomplete data settings.

New method detects causal relationships from noisy measurements.

New methods improve inference after prediction without strong model assumptions.

This paper studies the pricing of European-style Asian options when the price dynamics of the underlying risky asset are assumed to follow a Markov- modulated geometric Brownian motion; that is, the appreciation rate and the volatility of the underlying risky asset depend on unobservable states of the economy described…

This paper investigates the pricing of European-style lookback options when the price dynamics of the underlying risky asset are assumed to follow a Markov-modulated Geo-metric Brownian motion; that is, the appreciation rate and the volatility of the underlying risky asset depend on unobservable states of the economy d…

The problem of state estimation for unobservable distribution systems is considered. A deep learning approach to Bayesian state estimation is proposed for real-time applications. The proposed technique consists of distribution learning of stochastic power injection, a Monte Carlo technique for the training of a deep ne…

Optimal insurance contracts are designed to screen risk preferences and risk types under asymmetric information.

Simplifies pricing options in jump-diffusion models using gauge transformations.

In this paper, we consider a stochastic asset price model where the trend is an unobservable Ornstein Uhlenbeck process. We first review some classical results from Kalman filtering. Expectedly, the choice of the parameters is crucial to put it into practice. For this purpose, we obtain the likelihood in closed form, a…

New method for robust policy evaluation in offline reinforcement learning with sequentially exogenous unobserved confounders.

We discuss the pricing of defaultable assets in an incomplete information model where the default time is given by a first hitting time of an unobservable process. We show that in a fairly general Markov setting, the indicator function of the default has an absolutely continuous compensator. Given this compensator we t…

This paper develops a valuation model for private companies.

The paper introduces a model to measure ASR fairness, addressing key issues.

We maximize the expected utility from terminal wealth for an HARA investor when the market price of risk is an unobservable random variable. We compute the optimal portfolio explicitly and explore the effects of learning by comparing it with the corresponding myopic policy. In particular, we show that, for a market pri…

Stochastic volatility models describe asset prices $S_t$ as driven by an unobserved process capturing the random dynamics of volatility $σ_t$. Here, we quantify how much information about $σ_t$ can be inferred from asset prices $S_t$ in terms of Shannon's mutual information $I(S_t : σ_t)$. This motivates a careful nume…

Model optimizes trading strategy with unobservable toxicity.

In this paper, we propose a minimal model beyond geometric Brownian motion that aims to describe price actions with market inefficiency. From simple financial theory considerations, we arrive at a simple two-variable hidden Markovian time series model, with one of the variable entirely unobserved. Then, we analyze the …

Motivated by the application of real-time pricing in e-commerce platforms, we consider the problem of revenue-maximization in a setting where the seller can leverage contextual information describing the customer's history and the product's type to predict her valuation of the product. However, her true valuation is un…

Paper identifies unobserved variables from observable data.

Fast, reliable, and error-bounded option pricing with neural networks

Multivariate Bernoulli autoregressive (BAR) processes model time series of events in which the likelihood of current events is determined by the times and locations of past events. These processes can be used to model nonlinear dynamical systems corresponding to criminal activity, responses of patients to different med…

New method uses machine learning to optimize Fourier pricing methods.

New framework estimates demand responses across multiple contexts with limited price variation.

New method reduces Monte Carlo error in option pricing and Greeks estimation.

DecoR estimates causal effects in confounded time series data.

A new FFT method for Heston model option pricing with explicit error bounds.

Conventional financial models fail to explain the economic and monetary properties of cryptocurrencies due to the latter's dual nature: their usage as financial assets on the one side and their tight connection to the underlying blockchain structure on the other. In an effort to examine both components via a unified ap…

We consider an investor faced with the utility maximization problem in which the risky asset price process has pure-jump dynamics affected by an unobservable continuous-time finite-state Markov chain, the intensity of which can also be controlled by actions of the investor. Using the classical filtering theory, we redu…

Estimates price elasticity from autocorrelated time series using causal graphs.

KRCD detects unobserved confounders in nonlinear observational data.

Estimates domain truncation error for option pricing PDEs.

The paper addresses fairness in dynamic pricing for strategic buyers.

This paper analyzes hedge errors in Black-Scholes models using finite difference techniques.

Study provides error estimates for approximating game options with diffusion asset prices.