arXiv subjects

Jaehyung Choi

Publications and source records attributed to Jaehyung Choi.

At least 19 recordsLinked to original sources

arXiv2026

Entropic Value-at-Risk parity for tempered stable returns

We develop Entropic Value-at-Risk (EVaR) parity for tempered stable returns. EVaR-based inverse risk parity (IRP) and equal risk contribution (ERC) portfolios are constructed using multivariate normal tempered stable models and independent component analysis with tempered stable components. We derive the corresponding asset-level EVaR and EVaR-deviation contributions and use the latter to separate the fitted location term from EVaR risk contributions. Under Gaussian returns, EVaR-deviation IRP and ERC recover conventional volatility IRP and ERC weights. We evaluate the resulting portfolios in three investment universes. Empirically, EVaR-based ERC portfolios achieve positive Sharpe differences relative to equal weight across the universes.

q-fin.PM↗

arXiv2026

Information geometry of tempered stable processes

We derive the information geometry of tempered stable processes. We first compute the $α$-divergence between two tempered stable processes. From the divergence, we obtain the Fisher information matrix and the $α$-connection on the statistical manifold. The generalized, classical, and rapidly-decreasing tempered stable geometries are dually flat, with potential functions and canonical divergences expressed in terms of the tempering parameters. Moreover, their $α$-curvature tensors vanish for every $α$. The geometric results are also applied to bias reduction in maximum likelihood estimation and Bayesian predictive priors.

math.DG↗

arXiv2026

Entropic Value-at-Risk portfolio optimization for tempered stable Lévy processes

We develop parametric Entropic Value-at-Risk (EVaR) portfolio optimization for tempered stable Lévy returns. We derive portfolio cumulant-generating functions and weight-dependent admissible moment-generating-function domains under two multivariate constructions: a multivariate normal tempered stable approach and an independent component factorization. These expressions allow portfolio EVaR to be evaluated from fitted asset- or component-level parameters without repeated portfolio-level distribution fitting. We construct minimum-EVaR portfolios and two entropic reward--risk portfolios. We test the portfolios in a rolling 2000 to 2026 out-of-sample U.S. sector ETF allocation. In this universe, several entropic portfolios have higher realized Sharpe ratios than their matched CVaR portfolios or standard allocation benchmarks.

q-fin.PM↗

arXiv2026

Information geometry of Lévy processes and financial models

We develop the information geometry of Lévy processes. Deriving $α$-divergences directly in terms of the Lévy triplets of the Lévy processes, we identify Fisher information matrix and $α$-connection on the statistical manifold. In addition, we discuss statistical implications of this information geometry, including bias reduction estimation and Bayesian predictive priors. Several Lévy processes, broadly used for financial modeling such as tempered stable processes, the CGMY model, variance gamma processes, and the Merton model, are investigated through their differential-geometric structures as illustrative examples.

math.ST↗

arXiv2025

Hyperbolic decomposition of Dirichlet distance for ARMA models

We investigate the hyperbolic decomposition of the Dirichlet norm and distance between autoregressive moving average (ARMA) models. With the Kähler information geometry of linear systems in Hardy spaces and weighted Hardy spaces, we demonstrate that the Dirichlet norm and distance of ARMA models, corresponding to the mutual information between the past and future, are decomposed into functions of the hyperbolic distances between the poles and zeros of the ARMA models. Moreover, the distance is also expressed with separate terms from AR parts, MA parts, and AR-MA cross terms. Furthermore, the hyperbolic decomposition is helpful for the model order reduction of ARMA models.

cs.IT↗

arXiv2025

Kähler information manifolds of signal processing filters in weighted Hardy spaces

We extend the framework of Kähler information manifolds for complex-valued signal processing filters by introducing weighted Hardy spaces and smooth transformations of transfer functions. We demonstrate that the Riemannian geometry induced from weighted Hardy norms for the smooth transformations of its transfer function is a Kähler manifold. In this setting, the Kähler potential of the linear system geometry corresponds to the squared weighted Hardy norm of the composite transfer function. With the inherent structure of Kähler manifolds, geometric quantities on the manifold of linear systems in weighted Hardy spaces can be computed more efficiently and elegantly. Moreover, this generalized framework unifies a variety of well-known information manifolds within the structure of Kähler information manifolds for signal filters. Several illustrative examples from time series models are provided, wherein the metric tensor, Levi-Civita connection, and Kähler potentials are explicitly expressed in terms of polylogarithmic functions of the poles and zeros of transfer functions parameterized by weight vectors.

cs.IT↗

arXiv2023

Deep Calibration With Artificial Neural Network: A Performance Comparison on Option Pricing Models

This paper explores Artificial Neural Network (ANN) as a model-free solution for a calibration algorithm of option pricing models. We construct ANNs to calibrate parameters for two well-known GARCH-type option pricing models: Duan's GARCH and the classical tempered stable GARCH that significantly improve upon the limitation of the Black-Scholes model but have suffered from computation complexity. To mitigate this technical difficulty, we train ANNs with a dataset generated by Monte Carlo Simulation (MCS) method and apply them to calibrate optimal parameters. The performance results indicate that the ANN approach consistently outperforms MCS and takes advantage of faster computation times once trained. The Greeks of options are also discussed.

q-fin.MF↗

arXiv2022

Diversified reward-risk parity in portfolio construction

We introduce diversified risk parity embedded with various reward-risk measures and more generic allocation rules for portfolio construction. We empirically test the proposed reward-risk parity strategies and compare their performance with an equally-weighted risk portfolio in various asset universes. The reward-risk parity strategies we tested exhibit consistent outperformance evidenced by higher average returns, Sharpe ratios, and Calmar ratios. The alternative allocations also reflect less downside risks in Value-at-Risk, conditional Value-at-Risk, and maximum drawdown. In addition to the enhanced performance and reward-risk profile, transaction costs can be reduced by lowering turnover rates. The diversified reward-risk parity allocations gain superior performance in the Carhart four-factor analysis.

q-fin.PM↗

arXiv2021

Maximum drawdown, recovery, and momentum

We empirically test predictability on asset price by using stock selection rules based on maximum drawdown and its consecutive recovery. In various equity markets, monthly momentum- and weekly contrarian-style portfolios constructed from these alternative selection criteria are superior not only in forecasting directions of asset prices but also in capturing cross-sectional return differentials. In monthly periods, the alternative portfolios ranked by maximum drawdown measures exhibit outperformance over other alternative momentum portfolios including traditional cumulative return-based momentum portfolios. In weekly time scales, recovery-related stock selection rules are the best ranking criteria for detecting mean-reversion. For the alternative portfolios and their ranking baskets, improved risk profiles in various reward-risk measures also imply more consistent prediction on the direction of assets in future. In the Carhart four-factor analysis, higher factor-neutral intercepts for the alternative strategies are another evidence for the robust prediction by the alternative stock selection rules.

q-fin.GN↗

arXiv2018

Robust Deep Multi-modal Learning Based on Gated Information Fusion Network

The goal of multi-modal learning is to use complimentary information on the relevant task provided by the multiple modalities to achieve reliable and robust performance. Recently, deep learning has led significant improvement in multi-modal learning by allowing for the information fusion in the intermediate feature levels. This paper addresses a problem of designing robust deep multi-modal learning architecture in the presence of imperfect modalities. We introduce deep fusion architecture for object detection which processes each modality using the separate convolutional neural network (CNN) and constructs the joint feature map by combining the intermediate features from the CNNs. In order to facilitate the robustness to the degraded modalities, we employ the gated information fusion (GIF) network which weights the contribution from each modality according to the input feature maps to be fused. The weights are determined through the convolutional layers followed by a sigmoid function and trained along with the information fusion network in an end-to-end fashion. Our experiments show that the proposed GIF network offers the additional architectural flexibility to achieve robust performance in handling some degraded modalities, and show a significant performance improvement based on Single Shot Detector (SSD) for KITTI dataset using the proposed fusion network and data augmentation schemes.

cs.CV↗

arXiv2015

Reward-risk momentum strategies using classical tempered stable distribution

We implement momentum strategies using reward-risk measures as ranking criteria based on classical tempered stable distribution. Performances and risk characteristics for the alternative portfolios are obtained in various asset classes and markets. The reward-risk momentum strategies with lower volatility levels outperform the traditional momentum strategy regardless of asset class and market. Additionally, the alternative portfolios are not only less riskier in risk measures such as VaR, CVaR and maximum drawdown but also characterized by thinner downside tails. Similar patterns in performance and risk profile are also found at the level of each ranking basket in the reward-risk portfolios. Higher factor-neutral returns achieved by the reward-risk momentum strategies are statistically significant and large portions of the performances are not explained by the Carhart four-factor model.

q-fin.PM↗

arXiv2015

Kählerian information geometry for signal processing

We prove the correspondence between the information geometry of a signal filter and a Kähler manifold. The information geometry of a minimum-phase linear system with a finite complex cepstrum norm is a Kähler manifold. The square of the complex cepstrum norm of the signal filter corresponds to the Kähler potential. The Hermitian structure of the Kähler manifold is explicitly emergent if and only if the impulse response function of the highest degree in $z$ is constant in model parameters. The Kählerian information geometry takes advantage of more efficient calculation steps for the metric tensor and the Ricci tensor. Moreover, $α$-generalization on the geometric tensors is linear in $α$. It is also robust to find Bayesian predictive priors, such as superharmonic priors, because Laplace-Beltrami operators on Kähler manifolds are in much simpler forms than those of the non-Kähler manifolds. Several time series models are studied in the Kählerian information geometry.

math.DG↗

arXiv2015

Geometric shrinkage priors for Kählerian signal filters

We construct geometric shrinkage priors for Kählerian signal filters. Based on the characteristics of Kähler manifolds, an efficient and robust algorithm for finding superharmonic priors which outperform the Jeffreys prior is introduced. Several ansätze for the Bayesian predictive priors are also suggested. In particular, the ansätze related to Kähler potential are geometrically intrinsic priors to the information manifold of which the geometry is derived from the potential. The implication of the algorithm to time series models is also provided.

math.ST↗

arXiv2015

Application of Kähler manifold to signal processing and Bayesian inference

We review the information geometry of linear systems and its application to Bayesian inference, and the simplification available in the Kähler manifold case. We find conditions for the information geometry of linear systems to be Kähler, and the relation of the Kähler potential to information geometric quantities such as $α$-divergence, information distance and the dual $α$-connection structure. The Kähler structure simplifies the calculation of the metric tensor, connection, Ricci tensor and scalar curvature, and the $α$-generalization of the geometric objects. The Laplace--Beltrami operator is also simplified in the Kähler geometry. One of the goals in information geometry is the construction of Bayesian priors outperforming the Jeffreys prior, which we use to demonstrate the utility of the Kähler structure.

math.DG↗

arXiv2014

Physical approach to price momentum and its application to momentum strategy

We introduce various quantitative and mathematical definitions for price momentum of financial instruments. The price momentum is quantified with velocity and mass concepts originated from the momentum in physics. By using the physical momentum of price as a selection criterion, the weekly contrarian strategies are implemented in South Korea KOSPI 200 and US S&P 500 universes. The alternative strategies constructed by the physical momentum achieve the better expected returns and reward-risk measures than those of the traditional contrarian strategy in weekly scale. The portfolio performance is not understood by the Fama-French three-factor model.

q-fin.GN↗

arXiv2012

Momentum universe shrinkage effect in price momentum

We test the price momentum effect in the Korean stock markets under the momentum universe shrinkage to subuniverses of the KOSPI 200. Performance of the momentum strategy is not homogeneous with respect to change of the momentum universe. It is found that some submarkets generate the higher momentum returns than other universes do but large-size companies such as the KOSPI 50 components hinder the performance of the momentum strategy. The observation is also cross-checked with size portfolios and liquidity portfolios. Transactions by investor groups, in particular, the trading patterns by foreign investors can be a source of the momentum universe shrinkage effect in the momentum returns.

q-fin.GN↗

arXiv2012

Spontaneous symmetry breaking of arbitrage

We introduce the concept of spontaneous symmetry breaking to arbitrage modeling. In the model, the arbitrage strategy is considered as being in the symmetry breaking phase and the phase transition between arbitrage mode and no-arbitrage mode is triggered by a control parameter. We estimate the control parameter for momentum strategy with real historical data. The momentum strategy aided by symmetry breaking shows stronger performance and has a better risk measure than the naive momentum strategy in U.S. and South Korean markets.

q-fin.GN↗

arXiv2008

Superconformal Indices for Orbifold Chern-Simons Theories

We calculate the superconformal indices of recently discovered three-dimensional N=4,5 Chern-Simons-matter theories and compare them with the corresponding indices of supergravity on AdS4 times orbifolds of S7. We find perfect agreement in the large N and large k limit, provided that the twisted sector contributions at the fixed loci of the orbifolds are properly taken into account. We also discuss the index for the so-called "dual ABJM" proposal.

hep-th↗