1,721,032 research outputs found
The measure of model risk in credit capital requirements
Full text linkCredit capital requirements in Internal Rating Based approaches require the calibration of two key parameters: the probability of default and the loss-given-default. This letter considers the uncertainty about these two parameters and models this uncertainty in an elementary way: it shows how this estimation risk can be computed and properly taken into account in regulatory capital.
We analyse two standard real datasets: one composed by all corporates rated by Moody’s and one limited only to the speculative grade ones. We statistically test model hypotheses on the two key parameters and we observe that parameter dependency raises substantially the tail risk in capital requirements. The results are striking with a required increase in regulatory capital in the range 38% - 66%. This study draws also a clear policy implication: we suggest to reintroduce the scaling factor in credit capital requirements – factor that has been removed in Basel III Accord – to take into account model risk
Vol-Bond: an analytical solution
No full textWe find an analytical solution of the Vol-Bond according to the multi-factor Gaussian Heath-Jarrow-Morton model. We show how to calibrate the model with market data. This solution allows complete (and fast) control of this class of derivatives and of their sensitivities.
A simple solution for sticky cap and sticky floor
No full textWe show an analytical approach to sticky cap and sticky floor according to the Bond Market Model, a recently introduced version of the multi-factor Gaussian Heath-Jarrow-Morton model that is particularly easy to manage and calibrate. This solution allows having a comprehensive approach even for this class of Interest Rates' exotic derivatives that are fully path-dependent.Sticky cap; Sticky floor, Bond Market Model,
Short-time implied volatility of additive normal tempered stable processes
Full text linkEmpirical studies have emphasized that the equity implied volatility is characterized by a negative skew inversely proportional to the square root of the time-to-maturity. We examine the short-time-to-maturity behavior of the implied volatility smile for pure jump exponential additive processes. An excellent calibration of the equity volatility surfaces has been achieved by a class of these additive processes with power-law scaling. The two power-law scaling parameters are beta, related to the variance of jumps, and delta, related to the smile asymmetry. It has been observed, in option market data, that beta = 1 and delta = -1/2. In this paper, we prove that the implied volatility of these additive processes is consistent, in the short-time, with the equity market empirical characteristics if and only if beta = 1 and delta = -1/2
Neural network middle-term probabilistic forecasting of daily power consumption
Full text linkMiddle-term horizon (months to a year) power consumption prediction is a major challenge in the energy sector, particularly when probabilistic forecasting is considered. We propose a new modeling approach that incorporates trend, seasonality and weather conditions as explicative variables in a shallow neural network with an autoregressive feature. Applying it to the daily power consumption in New England, we obtain excellent results for the density forecast on the one-year test set. We verified the quality of the power consumption probabilistic forecasting achieved not only by comparing the results with other standard models for density forecasting but also by considering measures that are frequently used in the energy sector, such as the pinball loss function and confidence interval backtesting
A method that reveals the multi-level ultrametric tree hidden in p -spin-glass-like systems
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