arXiv research

Adaptive t-distribution estimates nonstationary time series using moving moments.

We introduce two operations named biflip and puzzle-move on simple polytopes producing polytopes with diffeomorphic moment-angle manifolds.

In several recently proposed stochastic optimization methods (e.g. RMSProp, Adam, Adadelta), parameter updates are scaled by the inverse square roots of exponential moving averages of squared past gradients. Maintaining these per-parameter second-moment estimators requires memory equal to the number of parameters. For …

Adaptive estimation of alpha-Stable distribution and Hurst exponent for nonstationary time series.

This paper analyzes the skewness of momentum trading strategies.

Study compares Kähler quotients of torus actions under varying moment maps.

This paper provides an insight to the time-varying dynamics of the shape of the distribution of financial return series by proposing an exponential weighted moving average model that jointly estimates volatility, skewness and kurtosis over time using a modified form of the Gram-Charlier density in which skewness and ku…

One of the more memorable moments of last summer's credit crunch came when the CFO of Goldman Sachs, David Viniar, announced in August that Goldman's flagship GEO hedge fund had lost 27% of its value since the start of the year. As Mr. Viniar explained, "We were seeing things that were 25-standard deviation moves, seve…

New homogeneous special Lagrangian submanifolds discovered in nearly Kähler CP3.

New knot invariant from 3-braids and 6-valent graphs.

A new memory-efficient Adam variant reduces second moments when feasible.

A new method for uncertainty estimation in neural networks using existing optimization steps.

Algorithm tackles large-scale portfolio optimization with higher moments, improving computational efficiency.

A new model for generating point processes with complex geometries.

Training deep neural networks requires intricate initialization and careful selection of learning rates. The emergence of stochastic gradient optimization methods that use adaptive learning rates based on squared past gradients, e.g., AdaGrad, AdaDelta, and Adam, eases the job slightly. However, such methods have also …

Develops a novel fast bootstrap for dependent data with higher-order accuracy.

This paper proposes a novel model of financial prices where: (i) prices are discrete; (ii) prices change in continuous time; (iii) a high proportion of price changes are reversed in a fraction of a second. Our model is analytically tractable and directly formulated in terms of the calendar time and price impact curve. …

Dynamic trading strategies, in the spirit of trend-following or mean-reversion, represent an only partly understood but lucrative and pervasive area of modern finance. Assuming Gaussian returns and Gaussian dynamic weights or signals, (e.g., linear filters of past returns, such as simple moving averages, exponential we…

The Kalman filter and Heston model are used to estimate asset prices and trading performance.

Gradient filters track moving parameters under noisy data and misspecification.

Proposes a new model for simulating electricity prices and their correlation structure.

New framework adds trend information to Adam-type optimizers for faster convergence.

A new knot move preserves pass-move equivalence and differs in count.

New algorithm catches moving subspaces in bandit problems.

A new method calculates fractional moments using the moment-generating function.

This paper presents an automatic approach for selecting optimal meta-models for sensitivity analysis in complex systems.

Study compares weak and homotopy moment maps in multisymplectic geometry.

New algorithms solve complex multi-level optimization problems with improved efficiency.

It is well known that any two diagrams representing the same oriented link are related by a finite sequence of Reidemeister moves O1, O2 and O3. Depending on orientations of fragments involved in the moves, one may distinguish 4 different versions of each of the O1 and O2 moves, and 8 versions of the O3 move. We introd…

In this paper, we introduce an equivalence relation on the set of local moves and classify local moves, called the extended $ST$-moves, up to the equivalence. Moreover, by inducing a binary relation on the set of equivalence classes of local moves, we show that an extended $ST$-move realizes the crossing change or the …

Minimal sets of moves for isotopic knots and trivalent graphs identified.

For a GJR-GARCH specification with a generic innovation distribution we derive analytic expressions for the first four conditional moments of the forward and aggregated returns and variances. Moment for the most commonly used GARCH models are stated as special cases. We also the limits of these moments as the time hori…

This paper identifies and bounds ICE central moments using PO marginal central moments.

We tackle causal inference under conditional moment restrictions using importance weighting.

Revisits Lee's Moment Formula, relaxing moment assumptions for implied volatility.

The H(n)-move simplifies virtual and welded knots and links.

We prove that the classical set of moves for standard spines of 3-manifolds (i.e. the MP-move and the V-move) does not suffice to relate to each other any two standard skeleta of a 3-manifold with marked boundary. We also describe a condition on the 3-manifold with marked boundary that tells whether the generalised set…

Developed moment estimators for affine stochastic volatility models.

Stiefel-Whitney classes of moment-angle manifolds are trivial.

A new method for estimating causal parameters from observables reduces the need for finite moment conditions.

Introduces generalized moment maps for almost Hermitian settings.

Proposes Moment Exchange to use moments in image recognition models, improving generalization.

New KCM tests improve specification testing via RKHS.

Constructs a moment map flow for isotropic maps on surfaces.

The (ordinary) unknotting-number of 1-dimensional knots, which is defined by using the crossing-change, is a very basic and important invariant. It is very natural to consider the `unknotting-number' associated with other local-moves on n-dimensional knots, where n is a natural number. In this paper we prove the follow…

A new method of moments estimator goes beyond data reweighting.

Moment Pooling reduces latent space dimensions in machine learning models.

Classifies virtual links up to a specific move.