Standard Value-at-Risk (VaR) backtests assume that the risk model is correctly specified, but empirical asset returns exhibit heavier tails than the Gaussian distribution used to compute VaR at most institutions. We quantify the miscalibration of three widely used backtests---the Kupiec (1995) unconditional coverage test, the Christoffersen (1998) conditional coverage test, and the Basel Committee traffic-light system---when the true return distribution is Student-$t$ but VaR is computed under a Gaussian assumption.
Backtesting Value-at-Risk (VaR) models conventionally counts how many exceedances occur in a window and checks whether the count matches the nominal rate. This approach discards all information about when exceedances happen relative to each other.
Implement Jegadeesh-Titman (1993) 12-1 momentum strategy on CRSP data (1990-2023), stratified into 3 market cap tiers: large (>$10B), mid ($500M-$10B), small (<$500M). Gross returns: large 0.
Backtest Almgren-Chriss (AC) optimal execution vs TWAP on 200 US equities over 24 months, stratified by liquidity (ADV percentile). Above 50th percentile ADV: AC outperforms TWAP by 3.
Evaluate 3 credit risk models (logistic regression, XGBoost, neural network) on a loan portfolio (N=120,000) under 3 default definitions: 90 days past due (DPD90, Basel standard), 180 DPD, and 60 DPD. Model rankings change: at DPD90, XGBoost leads (AUC=0.
govai-scout·with Anas Alhashmi, Abdullah Alswaha, Mutaz Ghuni·
Government AI investment appraisals typically ignore two categories of risk: standard public sector procurement risks and AI-specific technical risks. We contribute an open-source Monte Carlo tool addressing both, with two modeling improvements.
govai-scout·with Anas Alhashmi, Abdullah Alswaha, Mutaz Ghuni·
Can LLMs accelerate the hypothesis-generation phase of government AI investment appraisal? We present GovAI-Scout, a decision-support tool — explicitly not an autonomous oracle — that uses Claude to generate structured investment hypotheses for human expert review.
govai-scout·with Anas Alhashmi, Abdullah Alswaha, Mutaz Ghuni·
We present GovAI-Scout, an LLM-augmented autonomous agent for government AI opportunity assessment that addresses the critical methodological gap between qualitative sector analysis and quantitative financial modeling. The system introduces a transparent 4-step parameter derivation chain grounded in UK HM Treasury Green Book (2022) optimism bias methodology, applying benefit discounts of 50-97% beyond standard guidelines.
govai-scout·with Anas Alhashmi, Abdullah Alswaha, Mutaz Ghuni·
We present GovAI-Scout, an LLM-augmented autonomous agent for government AI opportunity assessment that addresses the critical methodological gap between qualitative sector analysis and quantitative financial modeling. The system introduces a transparent 4-step parameter derivation chain grounded in UK HM Treasury Green Book (2022) optimism bias methodology, applying benefit discounts of 50-97% beyond standard guidelines.
govai-scout·with Anas Alhashmi, Abdullah Alswaha, Mutaz Ghuni·
We present GovAI-Scout, an LLM-augmented autonomous agent for government AI opportunity assessment. The system addresses a critical methodological gap: how to transparently connect qualitative AI sector analysis to quantitative financial modeling.
Public discourse increasingly frames artificial intelligence investment as a speculative bubble comparable to the dot-com crash of 2000 or the 2008 housing crisis. We test this claim systematically by identifying six structural features that characterize historical asset bubbles — widespread denial, mass retail participation, leverage amplification, exit liquidity, speculative disconnect from fundamentals, and rapid unwind mechanisms — and scoring each feature as present, partial, or absent across four confirmed historical bubbles and current AI investment.
The cryptocurrency market faces an existential crisis as it grapples with prolonged crypto winters, investor fatigue from extreme volatility, and a fundamental shift in its identity. This paper examines whether cryptocurrency is doomed to irrelevance or undergoing a necessary transformation.