Many risk measures can be defined through the quantile function of the underlying loss variable (e.g., a class of distortion risk measures). When the loss variable is discrete or mixed, however, the definition of risk measures has to be broadened, which makes statistical inference trickier. To...

In this paper, we propose a two-step less volatile value-at-risk (LVaR) estimation using the generalized nearly-isotonic regression (GNIR) model. The first step of our LVaR estimation is to produce a VaR sequence under the generalized autoregressive conditional heteroskedasticity (GARCH)...

This paper examines the optimal allocation of risk across generations whose savings mix is subject to illiquidity in the form of uncertain trading costs. We use a stylized two-period OLG framework, where each generation makes a portfolio allocation decision for retirement, and show that...

In this paper, we study an optimal investment problem under joint initial-time and intermediate-time Value-at-Risk regulations and a portfolio insurance (PI) constraint on terminal wealth faced by a defined-contribution pension fund manager. The objective is to maximize the expected utility from...

We examine the role of acquirers’ familiarity with targets’ significant downside risks in M&A. We introduce a measure of a firm’s familiarity with another firm’s business downsides using similarity of their risk factor descriptions. We first show that this measure is distinct from...

VaR remains important in market risk management as Basel keeps most of the backtesting based on 1% VaR. Comparative backtesting as practiced in the current literature suffers from a major double problem. On the one hand, the score functions, although strictly consistent, may assign very good or...

We study the estimation and prediction of the risk measure Value at Risk for Cryptocurrencies. Using Generalized Random Forests (GRF) (Athey et al., 2019) that can be adapted to specifically fit the framework of quantile prediction, we show their superior performance over other established...

This study finds that downside tail risk , estimated from Korean corporate bond market data, predicts the excess returns of publicly listed investment-grade bonds. In addition to (normal) value at risk, estimated assuming a normal return distribution, abnormal risk , defined as the portion of...