Working Paper: NBER ID: w11069
Authors: Torben G. Andersen; Tim Bollerslev; Peter F. Christoffersen; Francis X. Diebold
Abstract: What do academics have to offer market risk management practitioners in financial institutions? Current industry practice largely follows one of two extremely restrictive approaches: historical simulation or RiskMetrics. In contrast, we favor flexible methods based on recent developments in financial econometrics, which are likely to produce more accurate assessments of market risk. Clearly, the demands of real-world risk management in financial institutions -- in particular, real-time risk tracking in very high-dimensional situations -- impose strict limits on model complexity. Hence we stress parsimonious models that are easily estimated, and we discuss a variety of practical approaches for high-dimensional covariance matrix modeling, along with what we see as some of the pitfalls and problems in current practice. In so doing we hope to encourage further dialog between the academic and practitioner communities, hopefully stimulating the development of improved market risk management technologies that draw on the best of both worlds.
Keywords: Market Risk Management; Volatility Modeling; Correlation Modeling; Financial Econometrics
JEL Codes: G1
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| traditional methods like historical simulation (HS) and RiskMetrics (C58) | inadequate incorporation of conditionality into Value-at-Risk (VaR) forecasts (G17) |
| inadequate incorporation of conditionality into Value-at-Risk (VaR) forecasts (G17) | underestimation of risk during periods of market stress (G41) |
| employing parsimonious dynamic models like GARCH (C22) | improved risk management (G38) |
| GARCH models (C58) | capture of mean reversion and long-memory effects in volatility (C22) |
| using high-frequency data (C58) | enhancement of measurement of volatilities and correlations (C58) |
| realized volatility and correlation measures derived from high-frequency data (C58) | more accurate forecasts than traditional methods (C53) |
| employing conditional density models (C51) | improved accuracy of risk predictions (C52) |
| advanced econometric modeling (C51) | enhanced risk management effectiveness (G38) |