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.
Study many-player investment-consumption games with power FPPs, finding market-risk preference affects consumption.
In the presence of ambiguity on the driving force of market randomness, we consider the dynamic portfolio choice without any predetermined investment horizon. The investment criteria is formulated as a robust forward performance process, reflecting an investor's dynamic preference. We show that the market risk premium …
We consider thin incomplete financial markets, where traders with heterogeneous preferences and risk exposures have motive to behave strategically regarding the demand schedules they submit, thereby impacting prices and allocations. We argue that traders relatively more exposed to market risk tend to submit more elasti…
Risk measures for multivariate financial positions are studied in a utility-based framework. Under a certain incomplete preference relation, shortfall and divergence risk measures are defined as the optimal values of specific set minimization problems. The dual relationship between these two classes of multivariate ris…
K-means algorithm improves financial market risk prediction accuracy.
Different approaches to defining dynamic market risk measures are available in the literature. Most are focused or derived from probability theory, economic behavior or dynamic programming. Here, we propose an approach to define and implement dynamic market risk measures based on recursion and state economy representat…
Generative neural networks improve insurance market risk modeling.
Study examines market risks on pension system sustainability.
Study optimal consumption and investment for investors with Epstein-Zin preferences.
CFM fee income is insufficient to hedge market risk, study finds.
Introduces an asymmetric model for measuring market risk.
Model predicts S&P500 volatility more accurately than existing models.
Method to decompose portfolio performance into FX, interest rate, carry, and residual market risks.
Quantum algorithms accelerate financial risk computation.
Agentic LLMs improve trading by estimating market risk.
We consider the problem of simulating loss probabilities and conditional excesses for linear asset portfolios under the t-copula model. Although in the literature on market risk management there are papers proposing efficient variance reduction methods for Monte Carlo simulation of portfolio market risk, there is no pa…
Hybrid ML ensemble predicts market risk and generates alpha.
The paper analyzes market risk factors for a mining company using a VAR model with stable distribution.
A new trading system learns to minimize risk and maximize returns in real markets.
DBNs improve ES and SES estimation for market risk, but tail behavior remains challenging.
Model predicts jump risk premia influencing cryptocurrency futures and option performance.
Study tests if equity factors explain Bitcoin's risk and returns.
The use of absolute return volatility has many modelling benefits says John Cotter. An illustration is given for the market risk measure, minimum capital requirements.
This paper proposes a new integrated variance estimator based on order statistics within the framework of jump-diffusion models. Its ability to disentangle the integrated variance from the total process quadratic variation is confirmed by both simulated and empirical tests. For practical purposes, we introduce an itera…
Paper develops a robust hedging framework to reduce market risk and uncertainty.
This study develops a multi-factor framework where not only market risk is considered but also potential changes in the investment opportunity set. Although previous studies find no clear evidence about a positive and significant relation between return and risk, favourable evidence can be obtained if a non-linear rela…
This work presents an asset pricing model that under rational expectation equilibrium perspective shows how, depending on risk aversion and noise volatility, a risky-asset has one equilibrium price that differs in term of efficiency: an informational efficient one (similar to Campbell and Kyle (1993)), and another one …
Investment and consumption strategy for risk-averse agents with Epstein-Zin utility.
Hedging methods to mitigate the exposure of variable annuity products to market risks require the calculation of market risk sensitivities (or "Greeks"). The complex, path-dependent nature of these products means these sensitivities typically must be estimated by Monte Carlo simulation. Standard market practice is to m…
The paper tackles fVaR prediction methods in finance.
Develops a statistical framework for coherent risk estimation.
Optimal liquidation strategy for a risk-averse investor in a one-sided limit order book driven by a Levy process.
We show that some specific market risk measures implied by current international capital regulation (the Basel Accords and the Capital Adequacy Directive of the European Union) violate the obvious requirement of convexity in some regions in the space of portfolio weights.
Generative Adversarial Net (GAN) has been proven to be a powerful machine learning tool in image data analysis and generation. In this paper, we propose to use Conditional Generative Adversarial Net (CGAN) to learn and simulate time series data. The conditions can be both categorical and continuous variables containing…
In the paper we develop mathematical tools of quantile hedging in incomplete market. Those could be used for two significant applications: o calculating the \textbf{optimal capital requirement imposed by Solvency II} (Directive 2009/138/EC of the European Parliament and of the Council) when the market and non-market ri…
The study finds no evidence of stochastic arbitrage opportunities in S&P 500 index options.
Paper constructs a CRRIX index to assess cryptocurrency market risks from regulatory changes.
A model-free hedging method using stock crowding scores.
Investigates optimal withdrawal strategies in VA contracts with tax and ratchet mechanisms.
We study historical correlations and lead-lag relationships between individual stock risk (volatility of daily stock returns) and market risk (volatility of daily returns of a market-representative portfolio) in the US stock market. We consider the cross-correlation functions averaged over all stocks, using 71 stock pr…
This study uses local Gaussian correlation to analyze stock return tails, revealing more sensitive network properties.
In this paper we propose a novel Bayesian methodology for Value-at-Risk computation based on parametric Product Partition Models. Value-at-Risk is a standard tool to measure and control the market risk of an asset or a portfolio, and it is also required for regulatory purposes. Its popularity is partly due to the fact …
Optimizes molecular generation for chemist preferences.
New method adapts to user preferences dynamically, improving recommendation models.
This letter uses the Block Maxima Extreme Value approach to quantify catastrophic risk in international equity markets. Risk measures are generated from a set threshold of the distribution of returns that avoids the pitfall of using absolute returns for markets exhibiting diverging levels of risk. From an application t…
Optimal timing for converting savings into annuities considering mortality risk.
Many real-world engineering problems rely on human preferences to guide their design and optimization. We present PrefOpt, an open source package to simplify sequential optimization tasks that incorporate human preference feedback. Our approach extends an existing latent variable model for binary preferences to allow f…
On June 26th, 2004, Central bank governors and the heads of bank supervisory authorities in the Group of Ten (G10) countries issued a press release and endorsed the publication of "International Convergence of Capital Measurement and Capital Standards: a Revised Framework", the new capital adequacy framework commonly k…