594 research outputs found
Elicitability and backtesting: Perspectives for banking regulation
Conditional forecasts of risk measures play an important role in internal
risk management of financial institutions as well as in regulatory capital
calculations. In order to assess forecasting performance of a risk measurement
procedure, risk measure forecasts are compared to the realized financial losses
over a period of time and a statistical test of correctness of the procedure is
conducted. This process is known as backtesting. Such traditional backtests are
concerned with assessing some optimality property of a set of risk measure
estimates. However, they are not suited to compare different risk estimation
procedures. We investigate the proposal of comparative backtests, which are
better suited for method comparisons on the basis of forecasting accuracy, but
necessitate an elicitable risk measure. We argue that supplementing traditional
backtests with comparative backtests will enhance the existing trading book
regulatory framework for banks by providing the correct incentive for accuracy
of risk measure forecasts. In addition, the comparative backtesting framework
could be used by banks internally as well as by researchers to guide selection
of forecasting methods. The discussion focuses on three risk measures,
Value-at-Risk, expected shortfall and expectiles, and is supported by a
simulation study and data analysis
Backtesting VaR Models: An Expected Shortfall Approach
Academics and practitioners have extensively studied Value-at-Risk (VaR) to propose a unique risk management technique that generates accurate VaR estimations for long and short trading positions and for all types of financial assets. However, they have not succeeded yet as the testing frameworks of the proposals developed, have not been widely accepted. A two-stage backtesting procedure is proposed to select a model that not only forecasts VaR but also predicts the losses beyond VaR. Numerous conditional volatility models that capture the main characteristics of asset returns (asymmetric and leptokurtic unconditional distribution of returns, power transformation and fractional integration of the conditional variance) under four distributional assumptions (normal, GED, Student-t, and skewed Student-t) have been estimated to find the best model for three financial markets, long and short trading positions, and two confidence levels. By following this procedure, the risk manager can significantly reduce the number of competing models that accurately predict both the VaR and the Expected Shortfall (ES) measures.Value-at-Risk, Expected Shortfall, Volatility Forecasting, Arch Models
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