Description: The article explores a closed-form forecasting method that combines Exponentially Weighted Moving Average (EWMA), weighted linear regression, and ridge regression (L2 regularization). The approach addresses two key challenges in time-series forecasting: adapting to nonstationarity and mitigating overfitting due to multicollinearity. EWMA assigns more weight to recent observations, while ridge regression penalizes large coefficients to stabilize estimates. A cross-validation strategy across rolling time windows selects optimal hyperparameters: the EWMA decay factor (α) and the ridge penalty (λ). The method is tested using repeated regressions of stock returns, showing that most hyperparameter combinations outperform ordinary least squares (OLS) under the (λ=1, α=0) setting.

Description: ALM keeps adapting. From duration-matching before 2008, to stress testing in the prolonged low-rate 2010s, to disintermediation risks in the sharp 2021–2023 rate hikes, each shock has reshaped the playbook. In my new paper, I argue for a practical way forward: matching the first 5–7 years of asset and liability cash flows and holding liquidity buffers, a balance between rigid full matching and simple duration management.

Description: Introduces the concept of total risk analysis, a methodology used to answer questions like "what are the chances we will lose more than $1 million"

Description: This article offers a comprehensive approach for “lean” model risk management (MRM), i.e., the management of an MRM program with few or possibly one employee devoted to the role. It explores key objectives of an MRM program, essential process elements, tips for efficiency, and examine the use of artificial intelligence (AI) in the models themselves and as a tool to increase efficiency of the program.