What Happened to Risk Management During the 2008-09 Financial Crisis?

When dealing with market risk under the Basel II Accord, variation pays in the form of lower capital requirements and higher profits. Typically, GARCH type models are chosen to forecast Value-at-Risk (VaR) using a single risk model. In this paper we illustrate two useful variations to the standard mechanism for choosing forecasts, namely: (i) combining different forecast models for each period, such as a daily model that forecasts the supremum or infinum value for the VaR; (ii) alternatively, select a single model to forecast VaR, and then modify the daily forecast, depending on the recent history of violations under the Basel II Accord. We illustrate these points using the Standard and Poor’s 500 Composite Index. In many cases we find significant decreases in the capital requirements, while incurring a number of violations that stays within the Basel II Accord limits.risk management;violations;conservative risk strategy;aggressive risk strategy;value-at-risk forecast

GFC-Robust Risk Management Strategies under the Basel Accord

A risk management strategy is proposed as being robust to the Global Financial Crisis (GFC) by selecting a Value-at-Risk (VaR) forecast that combines the forecasts of different VaR models. The robust forecast is based on the median of the point VaR forecasts of a set of conditional volatility models. This risk management strategy is GFC-robust in the sense that maintaining the same risk management strategies before, during and after a financial crisis would lead to comparatively low daily capital charges and violation penalties. The new method is illustrated by using the S&P500 index before, during and after the 2008-09 global financial crisis. We investigate the performance of a variety of single and combined VaR forecasts in terms of daily capital requirements and violation penalties under the Basel II Accord, as well as other criteria. The median VaR risk management strategy is GFC-robust as it provides stable results across different periods relative to other VaR forecasting models. The new strategy based on combined forecasts of single models is straightforward to incorporate into existing computer software packages that are used by banks and other financial institutions.Value-at-Risk (VaR);daily capital charges;optimizing strategy;robust forecasts;violation penalties;global financial crisis;Basel II Accord;aggressive risk management strategy;conservative risk management strategy

A decision rule to minimize daily capital charges in forecasting value-at-risk

Under the Basel II Accord, banks and other Authorized Deposit-taking Institutions (ADIs) have to communicate their daily risk estimates to the monetary authorities at the beginning of the trading day, using a variety of Value-at-Risk (VaR) models to measure risk. Sometimes the risk estimates communicated using these models are too high, thereby leading to large capital requirements and high capital costs. At other times, the risk estimates are too low, leading to excessive violations, so that realised losses are above the estimated risk. In this paper we propose a learning strategy that complements existing methods for calculating VaR and lowers daily capital requirements, while restricting the number of endogenous violations within the Basel II Accord penalty limits. We suggest a decision rule that responds to violations in a discrete and instantaneous manner, while adapting more slowly in periods of no violations. We apply the proposed strategy to Standard & Poor’s 500 Index and show there can be substantial savings in daily capital charges, while restricting the number of violations to within the Basel II penalty limits.value-at-risk;daily capital charges;optimizing strategy;risk forecasts;endogenous violations;frequency of violations

Chaos synchronization by resonance of multiple delay times

Chaos synchronization may arise in networks of nonlinear units with delayed couplings. We study complete and sublattice synchronization generated by resonance of two large time delays with a specific ratio. As it is known for single-delay networks, the number of synchronized sublattices is determined by the greatest common divisor (GCD) of the network loop lengths. We demonstrate analytically the GCD condition in networks of iterated Bernoulli maps with multiple delay times and complement our analytic results by numerical phase diagrams, providing parameter regions showing complete and sublattice synchronization by resonance for Tent and Bernoulli maps. We compare networks with the same GCD with single and multiple delays, and we investigate the sensitivity of the correlation to a detuning between the delays in a network of coupled Stuart-Landau oscillators. Moreover, the GCD condition also allows detection of time-delay resonances, leading to high correlations in nonsynchronizable networks. Specifically, GCD-induced resonances are observed both in a chaotic asymmetric network and in doubly connected rings of delay-coupled noisy linear oscillators

Inclusive top-pair production phenomenology with TOPIXS

We discuss various aspects of inclusive top-quark pair production based on TOPIXS, a new, flexible program that computes the production cross section at the Tevatron and LHC at next-to-next-to-leading logarithmic accuracy in soft and Coulomb resummation, including bound-state effects and the complete next-to-next-to-leading order result in the q-qbar channel, which has recently become available. We present the calculation of the top-pair cross section in pp collisions at 8 TeV centre-of-mass energy, as well as the cross sections for hypothetical heavy quarks in extensions of the standard model. The dependence on the parton distribution input is studied. Further we investigate the impact of LHC top cross section measurements at sqrt(s)=7 TeV on global fits of the gluon distribution using the NNPDF re-weighting method.Comment: 27 pages, 5 figures; v2: corrected typos in Eqs. (2.8) and (6.2) and the text, added footnote on page 4, matches published versio

Risk Management of Risk Under the Basel Accord: A Bayesian Approach to Forecasting Value-at-Risk of VIX Futures

It is well known that the Basel II Accord requires banks and other Authorized Deposit-taking Institutions (ADIs) to communicate their daily risk forecasts to the appropriate monetary authorities at the beginning of each trading day, using one or more risk models, whether individually or as combinations, to measure Value-at-Risk (VaR). The risk estimates of these models are used to determine capital requirements and associated capital costs of ADIs, depending in part on the number of previous violations, whereby realised losses exceed the estimated VaR. Previous papers proposed a new approach to model selection for predicting VaR, consisting of combining alternative risk models, and comparing conservative and aggressive strategies for choosing between VaR models. This paper, using Bayesian and non-Bayesian combinations of models addresses the question of risk management of risk, namely VaR of VIX futures prices, and extends the approaches given in previous papers to examine how different risk management strategies performed during the 2008-09 global financial crisis (GFC). The use of time-varying weights using Bayesian methods, allows dynamic combinations of the different models to obtain a more accurate VaR forecasts than the estimates and forecasts that might be produced by a single model of risk. One of these dynamic combinations are endogenously determined by the pass performance in terms of daily capital charges of the individual models. This can improve the strategies to minimize daily capital charges, which is a central objective of ADIs. The empirical results suggest that an aggressive strategy of choosing the Supremum of single model forecasts, as compared with Bayesian and non-Bayesian combinations of models, is preferred to other alternatives, and is robust during the GFC

Cloud condensation nuclei in pristine tropical rainforest air of Amazonia: size-resolved measurements and modeling of atmospheric aerosol composition and CCN activity

Atmospheric aerosol particles serving as cloud condensation nuclei (CCN) are key elements of the hydrological cycle and climate. We have measured and characterized CCN at water vapor supersaturations in the range of <i>S</i>=0.10–0.82% in pristine tropical rainforest air during the AMAZE-08 campaign in central Amazonia. <br><br> The effective hygroscopicity parameters describing the influence of chemical composition on the CCN activity of aerosol particles varied in the range of &kappa;&asymp;0.1–0.4 (0.16&plusmn;0.06 arithmetic mean and standard deviation). The overall median value of &kappa;&asymp;0.15 was by a factor of two lower than the values typically observed for continental aerosols in other regions of the world. Aitken mode particles were less hygroscopic than accumulation mode particles (&kappa;&asymp;0.1 at <i>D</i>&asymp;50 nm; &kappa;&asymp;0.2 at <i>D</i>&asymp;200 nm), which is in agreement with earlier hygroscopicity tandem differential mobility analyzer (H-TDMA) studies. <br><br> The CCN measurement results are consistent with aerosol mass spectrometry (AMS) data, showing that the organic mass fraction (<i>f</i>_org) was on average as high as ~90% in the Aitken mode (<i>D</i>&le;100 nm) and decreased with increasing particle diameter in the accumulation mode (~80% at <i>D</i>&asymp;200 nm). The κ values exhibited a negative linear correlation with <i>f</i>_org (<i>R</i>²=0.81), and extrapolation yielded the following effective hygroscopicity parameters for organic and inorganic particle components: &kappa;_org&asymp;0.1 which can be regarded as the effective hygroscopicity of biogenic secondary organic aerosol (SOA) and &kappa;_inorg&asymp;0.6 which is characteristic for ammonium sulfate and related salts. Both the size dependence and the temporal variability of effective particle hygroscopicity could be parameterized as a function of AMS-based organic and inorganic mass fractions (&kappa;_p=&kappa;_org&times;<i>f</i>_org +&kappa;_inorg&times;<i>f</i>_inorg). The CCN number concentrations predicted with &kappa;_p were in fair agreement with the measurement results (~20% average deviation). The median CCN number concentrations at <i>S</i>=0.1–0.82% ranged from <i>N</i>_CCN,0.10&asymp;35 cm^&minus;3 to <i>N</i>_CCN,0.82&asymp;160 cm^&minus;3, the median concentration of aerosol particles larger than 30 nm was <i>N</i>_CN,30&asymp;200 cm^&minus;3, and the corresponding integral CCN efficiencies were in the range of <i>N</i>_CCN,0.10/<i>N</i>_CN,30&asymp;0.1 to <i>N</i>_CCN,0.82/<i>N</i>_CN,30&asymp;0.8. <br><br> Although the number concentrations and hygroscopicity parameters were much lower in pristine rainforest air, the integral CCN efficiencies observed were similar to those in highly polluted megacity air. Moreover, model calculations of <i>N</i>_CCN,<i>S</i> assuming an approximate global average value of &kappa;&asymp;0.3 for continental aerosols led to systematic overpredictions, but the average deviations exceeded ~50% only at low water vapor supersaturation (0.1%) and low particle number concentrations (&le;100 cm^&minus;3). Model calculations assuming a constant aerosol size distribution led to higher average deviations at all investigated levels of supersaturation: ~60% for the campaign average distribution and ~1600% for a generic remote continental size distribution. These findings confirm earlier studies suggesting that aerosol particle number and size are the major predictors for the variability of the CCN concentration in continental boundary layer air, followed by particle composition and hygroscopicity as relatively minor modulators. <br><br> Depending on the required and applicable level of detail, the information and parameterizations presented in this paper should enable efficient description of the CCN properties of pristine tropical rainforest aerosols of Amazonia in detailed process models as well as in large-scale atmospheric and climate models