This paper develops a Bayesian Generalised Pareto Regression (GPR) model to forecast extreme losses in Indian equity markets, with a focus on the Nifty 50 index. Extreme negative returns, though rare, can cause significant financial disruption, and accurate modelling of such events is essential for effective risk management. Traditional Generalised Pareto Distribution (GPD) models often ignore market conditions; in contrast, our framework links the scale parameter to covariates using a log-linear function, allowing tail risk to respond dynamically to market volatility. We examine four prior choices for Bayesian regularisation of regression coefficients: Cauchy, Lasso (Laplace), Ridge (Gaussian), and Zellner's g-prior. Simulation results suggest that the Cauchy prior delivers the best trade-off between predictive accuracy and model simplicity, achieving the lowest RMSE, AIC, and BIC values. Empirically, we apply the model to large negative returns (exceeding 5%) in the Nifty 50 index. Volatility measures from the Nifty 50, S&P 500, and gold are used as covariates to capture both domestic and global risk drivers. Our findings show that tail risk increases significantly with higher market volatility. In particular, both S&P 500 and gold volatilities contribute meaningfully to crash prediction, highlighting global spillover and flight-to-safety effects. The proposed GPR model offers a robust and interpretable approach for tail risk forecasting in emerging markets. It improves upon traditional EVT-based models by incorporating real-time financial indicators, making it useful for practitioners, policymakers, and financial regulators concerned with systemic risk and stress testing.

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.

This research aims to evaluate the performance of several Recurrent Neural Network (RNN) architectures including Simple RNN, Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM), compared to classic algorithms such as Random Forest and XGBoost in building classification models for early crash detection in ASEAN-5 stock markets. The study is examined using imbalanced data, which is common due to the rarity of market crashes. The study analyzes daily data from 2010 to 2023 across the major stock markets of the ASEAN-5 countries, including Indonesia, Malaysia, Singapore, Thailand, and Philippines. Market crash is identified as the target variable when the major stock price indices fall below the Value at Risk (VaR) thresholds of 5%, 2.5% and 1%. predictors involving technical indicators of major local and global markets as well as commodity markets. This study includes 213 predictors with their respective lags (5, 10, 15, 22, 50, 200) and uses a time step of 7, expanding the total number of predictors to 1491. The challenge of data imbalance is addressed with SMOTE-ENN. The results show that all RNN-Based architectures outperform Random Forest and XGBoost. Among the various RNN architectures, Simple RNN stands out as the most superior, mainly due to the data characteristics that are not overly complex and focus more on short-term information. This study enhances and extends the range of phenomena observed in previous studies by incorporating variables like different geographical zones and time periods, as well as methodological adjustments.

Historically, the economic recession often came abruptly and disastrously. For instance, during the 2008 financial crisis, the SP 500 fell 46 percent from October 2007 to March 2009. If we could detect the signals of the crisis earlier, we could have taken preventive measures. Therefore, driven by such motivation, we use advanced machine learning techniques, including Random Forest and Extreme Gradient Boosting, to predict any potential market crashes mainly in the US market. Also, we would like to compare the performance of these methods and examine which model is better for forecasting US stock market crashes. We apply our models on the daily financial market data, which tend to be more responsive with higher reporting frequencies. We consider 75 explanatory variables, including general US stock market indexes, SP 500 sector indexes, as well as market indicators that can be used for the purpose of crisis prediction. Finally, we conclude, with selected classification metrics, that the Extreme Gradient Boosting method performs the best in predicting US stock market crisis events.

The current COVID-19 situation has alerted the world about the impending global financial crisis that we may face at the starting of this decade. It also reminded us of the Great Depression, triggered by the Wall Street crash of 1929 and last till 1939, and economic meltdown of 2007-2008 which was caused due to the collapse of Lehman Brothers (one of the biggest investment banks in the world). While such seismic scale emergencies on the economic front are rare, the financial crisis is always unfortunate. This is why experts and leaders are looking for solutions using modern technologies like artificial intelligence (AI) to mitigate any future occurrences. Since most of these financial crises happened because of stock market crashes and loosened credit lending standards, AI can play an instrumental role in the early forecast of potential market crashes and detecting faulty lending standards.

With an unavoidable recession in the cards, what can we expect in the coming months? How quickly can markets, societies and small businesses recover? The past decade was successful overall, showcasing phenomenal four-times S&P growth and close to seven-times Nasdaq growth. After crashing in March 2009, both rose in the face of adversity by mid-February 2020. But that all changed by March 2020.

Last month I covered Virtualitics' $7 million Series B and how VR and AR data visualization stands to fix some of our most pervasive big data challenges. The tech enables not only enterprises and organizations, but anyone, to use their spatial intelligence to spot patterns and make connections that breakthrough the tangled clutter of big data in a way that has been out of reach even with traditional 2D analytics. In the case of Atomic Fund, the crypto investment fund wanted to explore a dataset of a recent Bitcoin market crash that took place on February 5th, 2018. So they queried the data from the blockchain.info API and uploaded a 10-minute slice onto 3Data, a leading immersive data visualization platform, in order to map it into an interactive microcosm of wallet transactions as you can view below.