This paper presents a machine learning driven framework for sectoral stress testing in the Indian financial market, focusing on financial services, information technology, energy, consumer goods, and pharmaceuticals. Initially, we address the limitations observed in conventional stress testing through dimensionality reduction and latent factor modeling via Principal Component Analysis and Autoencoders. Building on this, we extend the methodology using V ariational Autoencoders, which introduces a probabilistic structure to the latent space. This enables Monte Carlo-based scenario generation, allowing for more nuanced, distribution-aware simulation of stressed market conditions. The proposed framework captures complex non-linear dependencies and supports risk estimation through V alue-at-Risk and Expected Shortfall. Together, these pipelines demonstrate the potential of Machine Learning approaches to improve the flexibility, robustness, and realism of financial stress testing. Stress testing is a fundamental component of financial risk management, designed to evaluate the resilience of financial systems or portfolios under adverse market conditions [1]. By simulating extreme but plausible scenarios, stress tests provide insight into potential vulnerabilities, that may not be evident during normal periods of market operation. Regulatory institutions such as the Basel Committee on Banking Supervision (BCBS) [2] and the Reserve Bank of India (RBI) [3] have emphasized the critical role of stress testing in ensuring financial stability, particularly in the aftermath of global financial crises of 2008.

The relationship between conditional expected returns, conditional risk, and asset characteristics has been a central topic in financial economics for decades. Yet, inference in this domain remains constrained by the unbalanced and high-dimensional nature of real-world data. In this paper, we address these challenges by introducing a nonparametric, kernelbased framework for the joint estimation of conditional mean and covariance matrices, providing a powerful and tractable solution to the econometric inference problem highlighted by Cochrane (2011). Our framework is specifically designed to deliver positive semidefinite covariance matrices across any state and for cross sections of varying sizes, filling a significant gap in the literature.

The risk of bankruptcy in a publicly traded firm is of major interest to shareholders, creditors, and employees. Prior literature has investigated the predictive performance of different forecasting models, mainly the discriminant analysis with accounting information (Altman, 1968), the distance to default structural model (Bharath and Shumway, 2008), and the hazard model with accounting and market information (Shumway, 2001; Chava and Jarrow, 2004). In this paper we investigate the benefits of applying high dimensional machine learning (ML) methods to bankruptcy prediction. We use a comprehensive sample of bankruptcies for U.S. publicly traded companies from 1969 to 2019 with financial, market, macro, and text based predictors. We study the performance of eight ML algorithms: the hazard model of Shumway (2001) and Chava and Jarrow (2004) enhanced with a penalty function (LASSO and Ridge), bagged trees (random forest and survival random forest), gradient boosted trees (XG Boost and LightGBM), and two specifications of neural networks (one shallower and one deeper).

The Panel Vector Autoregressive (PVAR) model is a popular tool for macroeconomic forecasting and structural analysis in multi-country applications since it allows for spillovers between countries in a very flexible fashion. However, this flexibility means that the number of parameters to be estimated can be enormous leading to over-parameterization concerns. Bayesian global-local shrinkage priors, such as the Horseshoe prior used in this paper, can overcome these concerns, but they require the use of Markov Chain Monte Carlo (MCMC) methods rendering them computationally infeasible in high dimensions. In this paper, we develop computationally efficient Bayesian methods for estimating PVARs using an integrated rotated Gaussian approximation (IRGA). This exploits the fact that whereas own country information is often important in PVARs, information on other countries is often unimportant. Using an IRGA, we split the the posterior into two parts: one involving own country coefficients, the other involving other country coefficients. Fast methods such as approximate message passing or variational Bayes can be used on the latter and, conditional on these, the former are estimated with precision using MCMC methods. In a forecasting exercise involving PVARs with up to $18$ variables for each of $38$ countries, we demonstrate that our methods produce good forecasts quickly.

As a world class tourist destination, Spain is considered to have it all; from a rich vibrant culture, world heritage sights, and a culinary scene fit for any foodie, Spain has something for everyone. On the tech front, Spain is a rising star as its startup scene is becoming the country's most flourishing sector. However, let us rewind approximately 10 years to the global financial crisis that took the world by storm. Spain was heavily hit by the 2008 global financial crisis, when the housing market crashed, leaving half-finished projects scattered from the suburbs of Madrid to the shores of the Mediterranean coastline. The sense of revival in Spain is clearer than the waters off Barcelona's coastline.

Foreign fourth-generation descendants of Japanese will be able to work in Japan for up to five years under a preferential visa program to be introduced this summer, the Justice Ministry said Friday. The new program applies to ethnic Japanese between 18 and 30 who have basic Japanese skills equivalent to the N4 level of the Japanese Language Proficiency Test. Applicants will also be required to have support from residents they know in Japan, such as family members or employers, who can get in touch with them at least once a month. Among those planning to apply are people who spent their childhoods in Japan with their parents before losing their jobs during the 2008 global financial crisis. Some of their parents later returned to Japan, but their grown-up fourth-generation offspring could not because the visa system only grants preferential full-time working rights and semi-permanent status to second- and third-generation descendants.