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.
NP-PROV separates mean and variance spaces to improve function uncertainty.
This paper addresses the problem of segmenting a time-series with respect to changes in the mean value or in the variance. The first case is when the time data is modeled as a sequence of independent and normal distributed random variables with unknown, possibly changing, mean value but fixed variance. The main assumpt…
A new method reduces data valuation variance for more trustworthy data trading.
The paper extends consistency results for sequential design strategies to vector-valued Gaussian processes.
The paper solves MMV and MV problems with random coefficients and finds shared optimal strategies.
VA-OPE improves OPE by incorporating variance information, achieving tighter error bounds.
Study tight offline learning bounds for linear MDPs using variance information.
Optimizes embedding accuracy for data variance and error.
New method quantifies uncertainty in reinforcement learning models.
This paper calculates worst-case target semi-variances for uncertain losses.
We present a set of log-price integrated variance estimators, equal to the sum of open-high-low-close bridge estimators of spot variances within subsequent time-step intervals. The main characteristics of some of the introduced estimators is to take into account the information on the occurrence times of the high a…
Develops abstention procedure for nonparametric regression via variance testing.
New estimator accurately estimates mean of real-valued distributions without variance knowledge.
Paper solves a control problem with robust methods.
A new estimator for evaluating policies in unknown environments.
Paper tackles bias-variance trade-off in missing data, proposing a dynamic framework.
Policy gradient methods are very attractive in reinforcement learning due to their model-free nature and convergence guarantees. These methods, however, suffer from high variance in gradient estimation, resulting in poor sample efficiency. To mitigate this issue, a number of variance-reduction approaches have been prop…
With a new deprivation (or poverty) function, in this paper, we theoretically study the changes in poverty with respect to the `global' mean and variance of the income distribution using Indian survey data. We show that when the income obeys a log-normal distribution, a rising mean income generally indicates a reductio…
QFIL improves offline RL by filtering data to reduce bias and variance.
We compute the value of a variance swap when the underlying is modeled as a Markov process time changed by a Lévy subordinator. In this framework, the underlying may exhibit jumps with a state-dependent Lévy measure, local stochastic volatility and have a local stochastic default intensity. Moreover, the Lévy subordina…
This work tackles risk-sensitive deep RL by optimizing policies with variance constraints.
Instability and variability of Deep Reinforcement Learning (DRL) algorithms tend to adversely affect their performance. Averaged-DQN is a simple extension to the DQN algorithm, based on averaging previously learned Q-values estimates, which leads to a more stable training procedure and improved performance by reducing …
This paper is devoted to study the effects arising from imposing a value-at-risk (VaR) constraint in mean-variance portfolio selection problem for an investor who receives a stochastic cash flow which he/she must then invest in a continuous-time financial market. For simplicity, we assume that there is only one investm…
To improve the efficient frontier of the classical mean-variance model in continuous time, we propose a varying terminal time mean-variance model with a constraint on the mean value of the portfolio asset, which moves with the varying terminal time. Using the embedding technique from stochastic optimal control in conti…
We investigate the accuracy of the two most common estimators for the maximum expected value of a general set of random variables: a generalization of the maximum sample average, and cross validation. No unbiased estimator exists and we show that it is non-trivial to select a good estimator without knowledge about the …
Optimizes option portfolios for skewed-t returns using VaR and variance measures.
The paper proposes a new portfolio optimization model that includes VaR risk measure.
New method optimizes portfolio weights as functions, outperforming traditional approaches.
New method estimates optimal Q-values with better accuracy for specific problems.
Improved LLM pre-training performance through better weight and variance control.
Quadratic hedging of option payoffs generates the variance optimal martingale measure. When an option features an exercise policy and its cash flows are hedged according to this approach, it may be tempting to optimize such a policy under this measure. Because the variance optimal martingale measure may not be an equiv…
Improved KernelSHAP via linear regression for ML model interpretation.
This paper describes an empirical study of shortfall optimization with Barra Extreme Risk. We compare minimum shortfall to minimum variance portfolios in the US, UK, and Japanese equity markets using Barra Style Factors (Value, Growth, Momentum, etc.). We show that minimizing shortfall generally improves performance ov…
Study compares imputation methods' effects on IML confidence intervals.
Market-based asset price probability depends on trade volumes and values, improving forecasts and reliability.
The paper analyzes optimal investment strategies for life insurance contracts using mean-variance optimization.
Stochastic dividend discount models (Hurley and Johnson, 1994 and 1998, Yao, 1997) present expressions for the expected value of stock prices when future dividends evolve according to some random scheme. In this paper we try to offer a more precise view on this issue proposing a closed-form formula for the variance of …
New method improves deep policy gradient algorithms by learning relative state values.
Off-policy learning exhibits greater instability when compared to on-policy learning in reinforcement learning (RL). The difference in probability distribution between the target policy () and the behavior policy (b) is a major cause of instability. High variance also originates from distributional mismatch. The var…
OSIRIS reduces variance in off-policy evaluation by omitting irrelevant states.
The vast majority of works on option pricing operate on the assumption of risk neutral valuation, and consequently focus on the expected value of option returns, and do not consider risk parameters, such as variance. We show that it is possible to give explicit formulae for the variance of European option returns (vani…
This paper proposes swaps on two important new measures of generalized variance, namely the maximum eigen-value and trace of the covariance matrix of the assets involved. We price these generalized variance swaps for financial markets with Markov-modulated volatilities. We consider multiple assets in the portfolio for …
A new permutation method improves two-sample testing power.
The breakthrough of deep Q-Learning on different types of environments revolutionized the algorithmic design of Reinforcement Learning to introduce more stable and robust algorithms, to that end many extensions to deep Q-Learning algorithm have been proposed to reduce the variance of the target values and the overestim…
We consider the mean-variance hedging problem under partial Information. The underlying asset price process follows a continuous semimartingale and strategies have to be constructed when only part of the information in the market is available. We show that the initial mean variance hedging problem is equivalent to a ne…
A large portfolio of independent returns is optimized under the variance risk measure with a ban on short positions. The no-short selling constraint acts as an asymmetric regularizer, setting some of the portfolio weights to zero and keeping the out of sample estimator for the variance bounded, avoiding the di…
New framework tests mean-variance spanning in high dimensions.
Paper explores two methods for optimal portfolio selection in financial markets.