We use machine learning methods to predict stock return volatility. Our out-of-sample prediction of realised volatility for a large cross-section of US stocks over the sample period from 1992 to 2016 is on average 44.1% against the actual realised volatility of 43.8% with an R2 being as high as...

We provide a comprehensive study on the cross-sectional predictability of corporate bond returns using big data and machine learning. We examine whether a large set of equity and bond characteristics drive the expected returns on corporate bonds. Using either set of characteristics, we find that...

We present a general framework for portfolio risk management in discrete time, based on a replicating martingale. This martingale is learned from a finite sample in a supervised setting. The model learns the features necessary for an effective low-dimensional representation, overcoming the curse...

This paper deals with identification and inference on the unobservable conditional factor space and its dimension in large unbalanced panels of asset returns. The model specification is nonparametric regarding the way the loadings vary in time as functions of common shocks and individual...

These notes aim at giving a broad skill set to the actuarial profession in insurance pricing and data science. We start from the classical world of generalized linear models, generalized additive models and credibility theory. These methods form the basis of the deeper statistical understanding....

We introduce a simulation method for dynamic portfolio valuation and risk management building on machine learning with kernels. We learn the dynamic value process of a portfolio from a finite sample of its cumulative cash flow. The learned value process is given in closed form thanks to a...

Making use of a structural model that allows for optimal liquidity management, we study the role that repos play in a bank's financing structure. In our model the bank's assets consist of illiquid loans and liquid reserves and are financed by a combination of repos, long–term debt, deposits...

We develop a dynamic model of banking to assess the effects of liquidity and leverage requirements on banks' insolvency risk. In this model, banks face taxation, flotation costs of securities, and default costs and maximize shareholder value by making their financing, liquid asset holdings, and...

We propose a methodology for measuring the market-implied capital of banks by subtracting from the market value of equity (market capitalization) a credit-spread-based correction for the value of shareholders' default option. We show that without such a correction, the estimated impact of a...

We develop a methodology to measure the capital shortfall of commercial banks in a market downturn, which we call stressed expected loss (SEL). We simulate a market downturn as a negative shock on interest rate and credit market risk factors that reflect the banks' market-sensitive assets. We...