arXiv research

This paper unifies risk-averse Thompson sampling for continuous risk functionals.

Improves risk and variability measures continuity and consistency.

Working in a continuous time setting, we extend to the general case of dynamic risk measures continuous from above the characterization of time consistency in terms of ``cocycle condition'' of the minimal penalty function. We prove also the supermartingale property for general time consistent dynamic risk measures. Whe…

Criterion extends identifiability for continuous mixtures of kernels.

The paper proves deep learning can be robust with certain loss functions.

The comparative statics of the optimal portfolios across individuals is carried out for a continuous-time complete market model, where the risky assets price process follows a joint geometric Brownian motion with time-dependent and deterministic coefficients. It turns out that the indirect utility functions inherit the…

Stochastic domains often involve risk-averse decision makers. While recent work has focused on how to model risk in Markov decision processes using risk measures, it has not addressed the problem of solving large risk-averse formulations. In this paper, we propose and analyze a new method for solving large risk-averse …

The risk of financial positions is measured by the minimum amount of capital to raise and invest in eligible portfolios of traded assets in order to meet a prescribed acceptability constraint. We investigate nondegeneracy, finiteness and continuity properties of these risk measures with respect to multiple eligible ass…

The curse of dimensionality affects neural network optimization, especially with smooth functions.

The Lebesgue property (order-continuity) of a monotone convex function on a solid vector space of measurable functions is characterized in terms of (1) the weak inf-compactness of the conjugate function on the order-continuous dual space, (2) the attainment of the supremum in the dual representation by order-continuous…

Nonparametric Thompson Sampling achieves optimal regret for risk-averse bandits with sub-Gaussian rewards.

Sharp bounds for distortion risk metrics under uncertain distributions.

We investigate optimal consumption problems for a Black-Scholes market under uniform restrictions on Value-at-Risk and Expected Shortfall for logarithmic utility functions. We find the solutions in terms of a dynamic strategy in explicit form, which can be compared and interpreted. This paper continues our previous wor…

In this paper we consider long-run risk sensitive average cost impulse control applied to a continuous-time Feller-Markov process. Using the probabilistic approach, we show how to get a solution to a suitable continuous-time Bellman equation and link it with the impulse control problem. The optimal strategy for the und…

This paper extends risk parity to continuous-time, solving risk budgeting problems.

Optimizes stock portfolios with profit, risk, and sustainability.

Scalar dynamic risk measures for univariate positions in continuous time are commonly represented as backward stochastic differential equations. In the multivariate setting, dynamic risk measures have been defined and studied as families of set-valued functionals in the recent literature. There are two possible extensi…

Characterizes continuity of monotone functionals in mixed topology.

New insights into risk aversion for complex decision models.

Study risk bounds for distributed ERM with general loss functions and hypothesis spaces.

In this paper we propose the notion of continuous-time dynamic spectral risk-measure (DSR). Adopting a Poisson random measure setting, we define this class of dynamic coherent risk-measures in terms of certain backward stochastic differential equations. By establishing a functional limit theorem, we show that DSRs may …

Study uses spectral risk for learning with heavy-tailed data.

$K$-NN classifier is one of the most famous classification algorithms, whose performance is crucially dependent on the distance metric. When we consider the distance metric as a parameter of $K$-NN, learning an appropriate distance metric for $K$-NN can be seen as minimizing the empirical risk of $K$-NN. In this paper,…

Classifies financial risk into three levels based on first passage times.

Unified framework for risk-aware policy learning in contextual bandits.

We study combinations of risk measures under no restrictive assumption on the set of alternatives. We develop and discuss results regarding the preservation of properties and acceptance sets for the combinations of risk measures. One of the main results is the representation of resulting risk measures from the properti…

This paper improves the robustness of risk estimation for financial positions.

This paper is a continuation of Ishitani and Kato (2015), in which we derived a continuous-time value function corresponding to an optimal execution problem with uncertain market impact as the limit of a discrete-time value function. Here, we investigate some properties of the derived value function. In particular, we …

A new RL framework for risk-sensitive decision-making using convex scoring functions.

Investment strategies for rank-dependent utility agents are derived in a continuous-time market.

Paper proposes robust risk measures for non-negative risks with partial information.

This paper introduces a method to incorporate risk sensitivity in RL using quadratic variation penalties.

Optimal trading strategy in Proof-of-Stake blockchain using continuous-time control.

Modeling business cycles via collective risk fluctuations in economic agents' risk space.

A new framework assesses financial and ESG risks for sustainable investing.

Models analyze strategic risk-taking in continuous action games.

Study shows financial value of weak information converges in discrete vs continuous markets.

Reinforcement learning for continuous-time risk-sensitive asset allocation

Develops new methods to estimate treatment effects in survival data with competing risks.

Dual representations for robust risk measures and uncertainty sets.

Improved DP SO with large Lipschitz parameters, handling outliers and heavy-tailed data.

In this paper, we explore several Fatou-type properties of risk measures. The paper continues to reveal that the strong Fatou property, which was introduced in [17], seems to be most suitable to ensure nice dual representations of risk measures. Our main result asserts that every quasiconvex law-invariant functional on…

The paper studies the convergence of SAA for systemic risk measures.

Develops new instance-optimality concepts in differential privacy.

In this paper we extend temporal difference policy evaluation algorithms to performance criteria that include the variance of the cumulative reward. Such criteria are useful for risk management, and are important in domains such as finance and process control. We propose both TD(0) and LSTD(lambda) variants with linear…

Market making is a fundamental trading problem in which an agent provides liquidity by continually offering to buy and sell a security. The problem is challenging due to inventory risk, the risk of accumulating an unfavourable position and ultimately losing money. In this paper, we develop a high-fidelity simulation of…

Paper studies second order tail probabilities in risk models.

This paper considers the problem of optimal liquidation of a position in a risky security in a financial market, where price evolution are risky and trades have an impact on price as well as uncertainty in the filling orders. The problem is formulated as a continuous time stochastic optimal control problem aiming at ma…