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.
The article studies aggregating algorithms for long-term forecasting.
New algorithm reduces online regression error in RKHS.
Optimal online linear regression in dynamic environments using discounted Vovk-Azoury-Warmuth forecaster.
Since Hobson's seminal paper [D. Hobson: Robust hedging of the lookback option. In: Finance Stoch. (1998)] the connection between model-independent pricing and the Skorokhod embedding problem has been a driving force in robust finance. We establish a general pricing-hedging duality for financial derivatives which are s…
Survey of algorithms to correct past mistakes in prediction.
Mixability of a loss is known to characterise when constant regret bounds are achievable in games of prediction with expert advice through the use of Vovk's aggregating algorithm. We provide a new interpretation of mixability via convex analysis that highlights the role of the Kullback-Leibler divergence in its definit…
Extends online linear regression to handle multivariate data.
Paper introduces a new outer measure for continuous price paths with instant enforcement.
We give an exposition and numerical studies of upper hedging prices in multinomial models from the viewpoint of linear programming and the game-theoretic probability of Shafer and Vovk. We also show that, as the number of rounds goes to infinity, the upper hedging price of a European option converges to the solution of…
This appendix proves CORN's universal consistency. One of Bin's PhD thesis examiner (Special thanks to Vladimir Vovk from Royal Holloway, University of London) suggested that CORN is universal and provided sketch proof of Lemma 1.6, which is the key of this proof. Based on the proof in Gyprfi et al. [2006], we thus pro…
We present a universal algorithm for online trading in Stock Market which performs asymptotically at least as good as any stationary trading strategy that computes the investment at each step using a fixed function of the side information that belongs to a given RKHS (Reproducing Kernel Hilbert Space). Using a universa…
In this paper we propose an investing strategy based on neural network models combined with ideas from game-theoretic probability of Shafer and Vovk. Our proposed strategy uses parameter values of a neural network with the best performance until the previous round (trading day) for deciding the investment in the curren…
We propose a betting strategy based on Bayesian logistic regression modeling for the probability forecasting game in the framework of game-theoretic probability by Shafer and Vovk (2001). We prove some results concerning the strong law of large numbers in the probability forecasting game with side information based on …
Using Vovk's outer measure, which corresponds to a minimal superhedging price, the existence of quadratic variation is shown for "typical price paths" in the space of càdlàg functions possessing a mild restriction on the jumps directed downwards. In particular, this result includes the existence of quadratic variation …
Paper optimizes combining expert predictions using CRPS loss.
We study multistep Bayesian betting strategies in coin-tossing games in the framework of game-theoretic probability of Shafer and Vovk (2001). We show that by a countable mixture of these strategies, a gambler or an investor can exploit arbitrary patterns of deviations of nature's moves from independent Bernoulli trial…
The paper proves that certain price paths with jumps have consistent quadratic variation.
A new method for portfolio allocation in continuous-time markets.
We derive some results on contrarian and one-sided strategies by Skeptic for the fair-coin game in the framework of the game-theoretic probability of Shafer and Vovk \cite{sv}. In particular, concerning the rate of convergence of the strong law of large numbers (SLLN), we prove that Skeptic can force that the convergen…
SA algorithms control dynamic regret in non-stationary settings with strong convexity or exp-concavity.
We give an overview of two approaches to probability theory where lower and upper probabilities, rather than probabilities, are used: Walley's behavioural theory of imprecise probabilities, and Shafer and Vovk's game-theoretic account of probability. We show that the two theories are more closely related than would be …
In this expository paper we illustrate the generality of game theoretic probability protocols of Shafer and Vovk (2001) in finite-horizon discrete games. By restricting ourselves to finite-horizon discrete games, we can explicitly describe how discrete distributions with finite support and the discrete pricing formulas…
Improved online convex optimization with delayed feedback using curvature.
We study capital process behavior in the fair-coin game and biased-coin games in the framework of the game-theoretic probability of Shafer and Vovk (2001). We show that if Skeptic uses a Bayesian strategy with a beta prior, the capital process is lucidly expressed in terms of the past average of Reality's moves. From t…
New methods improve cross-conformal prediction's prediction sets without sacrificing coverage guarantees.
We provide a model-free pricing-hedging duality in continuous time. For a frictionless market consisting of risky assets with continuous price trajectories, we show that the purely analytic problem of finding the minimal superhedging price of a path dependent European option has the same value as the purely probabi…
Conformal prediction uses past experience to determine precise levels of confidence in new predictions. Given an error probability , together with a method that makes a prediction of a label , it produces a set of labels, typically containing , that also contains with probability . Con…
Conformal predictors, introduced by Vovk et al. (2005), serve to build prediction intervals by exploiting a notion of conformity of the new data point with previously observed data. In the present paper, we propose a novel method for constructing prediction intervals for the response variable in multivariate linear mod…
Unified framework for generalized Venn and Venn-Abers calibration for reliable prediction.
The speed with which a learning algorithm converges as it is presented with more data is a central problem in machine learning --- a fast rate of convergence means less data is needed for the same level of performance. The pursuit of fast rates in online and statistical learning has led to the discovery of many conditi…
We introduce a new formulation of asset trading games in continuous time in the framework of the game-theoretic probability established by Shafer and Vovk (Probability and Finance: It's Only a Game! (2001) Wiley). In our formulation, the market moves continuously, but an investor trades in discrete times, which can dep…
Develops a new theory of loss functions for statistical machine learning.
New betting strategy reduces regret to ln(ln n) with protection against adversarial data.
This paper shows hedging algorithms improve performance in repeated matrix games.
Examines algorithmic modeling across three cultures.
Meta-algorithm selection aims to choose the best algorithm selector for a given problem instance.
Proposes CLRS benchmark to evaluate algorithmic reasoning.
Combines multiple bandit algorithms to create a nearly optimal single algorithm.
New algorithms improve stochastic optimization and online learning efficiency.
The exchange algorithm is studied for its convergence and asymptotic variance.
New algorithms optimize algorithm parameters in online settings with reduced computational costs.
Bayesian networks (BN) are used in a big range of applications but they have one issue concerning parameter learning. In real application, training data are always incomplete or some nodes are hidden. To deal with this problem many learning parameter algorithms are suggested foreground EM, Gibbs sampling and RBE algori…
Parallel algorithm finds sparse solutions for nonconvex problems.
No algorithm outperforms uniform sampling in A/B testing.
Improves algorithm selection for thousands of candidates using dyadic features.
New algorithms decode Markov chains with near-optimal performance, even with small latency.
Combines online learning algorithms to achieve better performance.
New ELM algorithms reduce computation time and complexity.