Pavel V. Shevchenko

Superconductor Digital-RF Receiver Systems

Digital superconductor electronics has been experiencing rapid maturation with the emergence of smaller-scale, lower-cost communications applications which became the major technology drivers. These applications are primarily in the area of wireless communications, radar, and surveillance as well as in imaging and sensor systems. In these areas, the fundamental advantages of superconductivity translate into system benefits through novel Digital-RF architectures with direct digitization of wide band, high frequency radio frequency (RF) signals. At the same time the availability of relatively small 4K cryocoolers has lowered the foremost market barrier for cryogenically-cooled digital electronic systems. Recently, we have achieved a major breakthrough in the development, demonstration, and successful delivery of the cryocooled superconductor digital-RF receivers directly digitizing signals in a broad range from kilohertz to gigahertz. These essentially hybrid-technology systems combine a variety of superconductor and semiconductor technologies packaged with two-stage commercial cryocoolers: cryogenic Nb mixed-signal and digital circuits based on Rapid Single Flux Quantum (RSFQ) technology, room-temperature amplifiers, FPGA processing and control circuitry. The demonstrated cryocooled digital-RF systems are the worlds first and fastest directly digitizing receivers operating with live satellite signals in X-band and performing signal acquisition in HF to L-band at ∼30GHz clock frequencies.

The Structural Modelling of Operational Risk via Bayesian inference: Combining Loss Data with Expert Opinions

To meet the Basel II regulatory requirements for the Advanced Measurement Approaches, the bank’s internal model must include the use of internal data, relevant external data, scenario analysis and factors reflecting the business environment and internal control systems. Quantification of operational risk cannot be based only on historical data but should involve scenario analysis. Historical internal operational risk loss data have limited ability to predict future behaviour moreover, banks do not have enough internal data to estimate low frequency high impact events adequately. Historical external data are difficult to use due to different volumes and other factors. In addition, internal and external data have a survival bias, since typically one does not have data of all collapsed companies. The idea of scenario analysis is to estimate frequency and severity of risk events via expert opinions taking into account bank environment factors with reference to events that have occurred (or may have occurred) in other banks. Scenario analysis is forward looking and can reflect changes in the banking environment. It is important to not only quantify the operational risk capital but also provide incentives to business units to improve their risk management policies, which can be accomplished through scenario analysis. By itself, scenario analysis is very subjective but combined with loss data it is a powerful tool to estimate operational risk losses. Bayesian inference is a statistical technique well suited for combining expert opinions and historical data. In this paper, we present examples of the Bayesian inference methods for operational risk quantification.

The Quantification of Operational Risk Using Internal Data, Relevant External Data and Expert Opinion

To quantify an operational risk capital charge under Basel II, many banks adopt a Loss Distribution Approach. Under this approach, quantification of the frequency and severity distributions of operational risk involves the banks internal data, expert opinions and relevant external data. In this paper we suggest a new approach, based on a Bayesian inference method, that allows for a combination of these three sources of information to estimate the parameters of the risk frequency and severity distributions.

Model Uncertainty in Claims Reserving within Tweedie's Compound Poisson Models

In this paper we examine the claims reserving problem using Tweedies compound Poisson model. We develop the maximum likelihood and Bayesian Markov chain Monte Carlo simulation approaches to fit the model and then compare the estimated models under different scenarios. The key point we demonstrate relates to the comparison of reserving quantities with and without model uncertainty incorporated into the prediction. We consider both the model selection problem and the model averaging solutions for the predicted reserves. As a part of this process we also consider the sub problem of variable selection to obtain a parsimonious representation of the model being fitted.

Cryocooled wideband digital channelizing radio-frequency receiver based on low-pass ADC

We have demonstrated a digital receiver performing direct digitization of radio-frequency signals over a wide frequency range from kilohertz to gigahertz. The complete system, consisting of a cryopackaged superconductor all-digital receiver (ADR) chip followed by room-temperature interface electronics and a field programmable gate array (FPGA) based post-processing module, has been developed. The ADR chip comprises a low-pass analog-to-digital converter (ADC) delta modulator with phase modulation–demodulation architecture together with digital in-phase and quadrature mixer and a pair of digital decimation filters. The chip is fabricated using a 4. 5k A cm −2 process and is cryopackaged using a commercial-off-the-shelf cryocooler. Experimental results in HF, VHF, UHF and L bands and their analysis, proving consistent operation of the cryopackaged ADR chip up to 24.32 GHz clock frequency, are presented and discussed.

Calculation of aggregate loss distributions

Estimation of the operational risk capital under the Loss Distribution Approach requires evaluation of aggregate (compound) loss distributions which is one of the classic problems in risk theory. Closed-form solutions are not available for the distributions typically used in operational risk. However with modern computer processing power, these distributions can be calculated virtually exactly using numerical methods. This paper reviews numerical algorithms that can be successfully used to calculate the aggregate loss distributions. In particular Monte Carlo, Panjer recursion and Fourier transformation methods are presented and compared. Also, several closed-form approximations based on moment matching and asymptotic result for heavy-tailed distributions are reviewed.

Dynamic operational risk: modeling dependence and combining different sources of information

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The t copula with multiple parameters of degrees of freedom: bivariate characteristics and application to risk management

The t copula is often used in risk management as it allows for modeling the tail dependence between risks and it is simple to simulate and calibrate. However, the use of a standard t copula is often criticized due to its restriction of having a single parameter for the degrees of freedom (dof) that may limit its capability to model the tail dependence structure in a multivariate case. To overcome this problem, the grouped t copula was proposed recently, where risks are grouped a priori in such a way that each group has a standard t copula with its specific dof parameter. In this paper we propose the use of a generalized grouped t copula, where each group consists of one risk factor only, so that a priori grouping is not required. The copula characteristics in the bivariate case are studied. We explain simulation and calibration procedures, including a simulation study on the finite sample properties of the maximum likelihood estimators and Kendalls tau approximation. This new copula is significantly different from the standard t copula in terms of risk measures such as tail dependence, value at risk and expected shortfall.

Chain ladder method: Bayesian bootstrap versus classical bootstrap

The intention of this paper is to estimate a Bayesian distribution-free chain ladder (DFCL) model using approximate Bayesian computation (ABC) methodology. We demonstrate how to estimate quantities of interest in claims reserving and compare the estimates to those obtained from classical and credibility approaches. In this context, a novel numerical procedure utilizing a Markov chain Monte Carlo (MCMC) technique, ABC and a Bayesian bootstrap procedure was developed in a truly distribution-free setting. The ABC methodology arises because we work in a distribution-free setting in which we make no parametric assumptions, meaning we cannot evaluate the likelihood point-wise or in this case simulate directly from the likelihood model. The use of a bootstrap procedure allows us to generate samples from the intractable likelihood without the requirement of distributional assumptions; this is crucial to the ABC framework. The developed methodology is used to obtain the empirical distribution of the DFCL model parameters and the predictive distribution of the outstanding loss liabilities conditional on the observed claims. We then estimate predictive Bayesian capital estimates, the value at risk (VaR) and the mean square error of prediction (MSEP). The latter is compared with the classical bootstrap and credibility methods.

Computing Tails of Compound Distributions Using Direct Numerical Integration

An efficient adaptive direct numerical integration (DNI) algorithm is developed for computing high quantiles and conditional Value at Risk (CVaR) of compound distributions using characteristic functions. A key innovation of the numerical scheme is an effective tail integration approximation that reduces the truncation errors significantly with little extra effort. High precision results of the 0.999 quantile and CVaR were obtained for compound losses with heavy tails and a very wide range of loss frequencies using the DNI, Fast Fourier Transform (FFT) and Monte Carlo (MC) methods. These results, particularly relevant to operational risk modelling, can serve as benchmarks for comparing different numerical methods. We found that the adaptive DNI can achieve high accuracy with relatively coarse grids. It is much faster than MC and competitive with FFT in computing high quantiles and CVaR of compound distributions in the case of moderate to high frequencies and heavy tails.