Results 61 to 70 of about 17,630 (184)
Reinforcement Learning for Jump‐Diffusions, With Financial Applications
Mathematical Finance, EarlyView.ABSTRACT We study continuous‐time reinforcement learning (RL) for stochastic control in which system dynamics are governed by jump‐diffusion processes. We formulate an entropy‐regularized exploratory control problem with stochastic policies to capture the exploration–exploitation balance essential for RL.
Xuefeng Gao, Lingfei Li, Xun Yu Zhou
wiley +1 more source
Never, Ever Getting Started: On Prospect Theory Without Commitment
Mathematical Finance, EarlyView.ABSTRACT Prospect theory is arguably the most prominent alternative to expected utility theory. We study the investment or gambling behavior of a prospect theory decision maker who is aware of his time‐inconsistency but lacks commitment. For the empirically relevant prospect theory specifications, we obtain the extreme prediction that such a decision ...
Sebastian Ebert, Philipp Strack
wiley +1 more source
Equilibrium Reward for Liquidity Providers in Automated Market Makers
Mathematical Finance, EarlyView.ABSTRACT We find the equilibrium contract that an automated market maker (AMM) offers to their strategic liquidity providers (LPs) in order to maximize the order flow that gets processed by the venue. Our model is formulated as a leader–follower stochastic game, where the venue is the leader and a representative LP is the follower.
Alif Aqsha +2 more
wiley +1 more source
Random Carbon Tax Policy and Investment Into Emission Abatement Technologies
Mathematical Finance, EarlyView.ABSTRACT We analyze the problem of a profit‐maximizing electricity producer, subject to carbon taxes, who decides on investments into CO2$\rm CO_2$ abatement technologies. We assume that the carbon tax policy is random and that the investment in the abatement technology is divisible, irreversible, and subject to transaction costs.
Katia Colaneri +2 more
wiley +1 more source
Stochastic integrals of point processes and the decomposition of two-parameter martingales
Journal of Multivariate Analysis, 1989 Let M be a two-parameter square-integrable martingale with trajectories that have limits in all quadrants and are continuous from the right. The paper gives the decomposition of M into four orthogonal components: \(M=\sum^{4}_{i=1}M_ i,\) where \(M_ 1\) is continuous, \(M_ 2\) is purely discontinuous, \(M_ 3\) and \(M_ 4\) are continuous in one ...
openaire +1 more source
A Model of Strategic Sustainable Investment
Mathematical Finance, EarlyView.ABSTRACT We study a problem of optimal irreversible investment and emission reduction formulated as a nonzero‐sum dynamic game between an investor with environmental preferences and a firm. The game is set in continuous‐time on an infinite‐time horizon.
Tiziano De Angelis +2 more
wiley +1 more source
r-variations for two-parameter continuous martingales and itô's formula
Stochastic Processes and their Applications, 1989 Let \(M=\{M_ z;z\in [0,1]^ 2\}\) be a two-parameter continuous martingale bounded in \(L^ 4\), and suppose that f is a real-valued function of class \(C^ 4\) such that \(f(0)=0\). The aim of this paper is to establish an Itô's formula of the type \[ f(M_ z)=\sum^{4}_{r=1}(r!)^{-1}\int_{[0,z]}f^{(r)}(M_ u)d\mu^ r_ M(u), \] where the processes \(\mu^ r_ ...
openaire +2 more sources
Inference on the Attractor Space via Functional Approximation
Oxford Bulletin of Economics and Statistics, EarlyView.ABSTRACT This paper discusses semiparametric inference on hypotheses on the cointegration and the attractor spaces for I(1)$$ I(1) $$ linear processes with moderately large cross‐sectional dimension. The approach is based on sample canonical correlations and functional approximation of Brownian motions, and it can be applied both to the whole system ...
Massimo Franchi, Paolo Paruolo
wiley +1 more source
Orthogonal projections on martingale $H^{1}$ spaces of two parameters
Illinois Journal of Mathematics, 1994 Given natural numbers \(n\) and \(i\) so that \(1\leq i\leq 2^ n\), let \((n, i)\) denote the ``dyadic interval'' \(\left[{i- 1\over 2^ n}, {i\over 2^ n}\right]\), \(h_{(n, i)}\) the \(L^ \infty\)-normalized Haar function supported on \((n, i)\), and \(h_{(n, i)\times (m, j)}(s, t)= h_{(n, i)}(s) h_{(m, j)}(t)\) (for \(s,t\in [0, 1]\)). If \(F= \sum c_{
openaire +3 more sources
Statistical inference with exchangeability and martingales. [PDF]
Philos Trans A Math Phys Eng Sci, 2023 Holmes CC, Walker SG.
europepmc +1 more source