arXiv research

Paper analyzes high-dimensional portfolio risks and finds empirical out-of-sample relative loss is more reliable.

RandALO speeds up risk estimation for large datasets.

New model optimizes portfolios over multiple periods using predictive control.

The paper analyzes LOCV for high-dimensional risk estimation, proving error bounds.

Study identifies key ESG variables for assessing financial risk.

Downsampling can improve generalization in ridgeless linear regression, especially with optimal sketching size.

Paper analyzes cyber risk classifications for forecasting performance.

ASRI index detects crypto market risks with high precision and lead time.

A study finds that only a few factors explain corporate bond risk, rendering extensive bond factor literature redundant.

The paper proposes a new approach to portfolio selection that maximizes diversification and return.

Proposes a bond portfolio solution for managing interest rate risk.

New method optimises worst-case risk under model uncertainty.

Managing a portfolio to a risk model can tilt the portfolio toward weaknesses of the model. As a result, the optimized portfolio acquires downside exposure to uncertainty in the model itself, what we call "second order risk." We propose a risk measure that accounts for this bias. Studies of real portfolios, in asset-by…

The paper uses machine learning to forecast macroeconomic outcomes with high-dimensional data.

The study evaluates financial risk using copulas and statistical tests.

The paper develops diverse risk models for US stock portfolios.

This study compares three portfolio design approaches for stock selection.

Study uses vine copulas to optimize financial portfolios during and after the financial crisis.

High-performing equity factor with Sharpe ratio above 13 out-of-sample.

The paper analyzes the generalization performance of spectral clustering algorithms and proposes new methods to improve their effectiveness.

Study finds traditional technical indicators underperform in high-frequency trading, suggesting risk management over prediction.

The paper optimizes portfolios using a new GARCH model with regime switching and tempered stable innovations.

Develops a Bayesian framework for portfolio choice with a new posterior distribution.

MRCs minimize worst-case expected 0-1 loss and provide performance guarantees.

We study the consistency of sample mean-variance portfolios of arbitrarily high dimension that are based on Bayesian or shrinkage estimation of the input parameters as well as weighted sampling. In an asymptotic setting where the number of assets remains comparable in magnitude to the sample size, we provide a characte…

New methods improve portfolio risk minimization by estimating covariance matrix more accurately.

Bagging stabilizes linear interpolators, improving their generalization performance.

Any optimization algorithm based on the risk parity approach requires the formulation of portfolio total risk in terms of marginal contributions. In this paper we use the independence of the underlying factors in the market to derive the centered moments required in the risk decomposition process when the modified vers…

Improves test set performance and reduces out-of-sample disappointment for unstable models.

A new deep learning model improves asset pricing predictions.

New insights into ridge regression with correlated data, improving risk prediction.

Shrunk sample covariance matrix is a factor model of a special form combining some (typically, style) risk factor(s) and principal components with a (block-)diagonal factor covariance matrix. As such, shrinkage, which essentially inherits out-of-sample instabilities of the sample covariance matrix, is not an alternativ…

New method certifies deep graph classifiers with tighter risk bounds.

This paper proposes RiskRank as a joint measure of cyclical and cross-sectional systemic risk. RiskRank is a general-purpose aggregation operator that concurrently accounts for risk levels for individual entities and their interconnectedness. The measure relies on the decomposition of systemic risk into sub-components …

In this paper we estimate the mean-variance portfolio in the high-dimensional case using the recent results from the theory of random matrices. We construct a linear shrinkage estimator which is distribution-free and is optimal in the sense of maximizing with probability $1$ the asymptotic out-of-sample expected utilit…

New tool detects 'fleeting modes' causing excess risk in financial markets.

We give a complete algorithm and source code for constructing what we refer to as heterotic risk models (for equities), which combine: i) granularity of an industry classification; ii) diagonality of the principal component factor covariance matrix for any sub-cluster of stocks; and iii) dramatic reduction of the facto…

The "double descent" risk curve was proposed to qualitatively describe the out-of-sample prediction accuracy of variably-parameterized machine learning models. This article provides a precise mathematical analysis for the shape of this curve in two simple data models with the least squares/least norm predictor. Specifi…

Study improves understanding of non-differentiable penalties in high-dimensional settings.

Risk aversion is a key element of utility maximizing hedge strategies; however, it has typically been assigned an arbitrary value in the literature. This paper instead applies a GARCH-in-Mean (GARCH-M) model to estimate a time-varying measure of risk aversion that is based on the observed risk preferences of energy hed…

Markowitz' celebrated optimal portfolio theory generally fails to deliver out-of-sample diversification. In this note, we propose a new portfolio construction strategy based on symmetry arguments only, leading to "Eigenrisk Parity" portfolios that achieve equal realized risk on all the principal components of the covar…

New volatility model for option pricing with time-varying risk premium.

Improved portfolio optimization using machine learning and hierarchical clustering.

We found that factors decay over time, with momentum fitting best.

Paper analyzes systematic jump risk around the clock using news narratives.

Overfitting can make models vulnerable to adversarial attacks even if they are robust to standard risks.

Improved Hawkes model forecasts extreme financial returns more accurately.

A robust machine learning approach forecasts U.S. Treasury yields, reducing risk for investors.