Data Challenges in High-Performance Risk Analytics

Risk Analytics is important to quantify, manage and analyse risks from the manufacturing to the financial setting. In this paper, the data challenges in the three stages of the high-performance risk analytics pipeline, namely risk modelling, portfolio risk management and dynamic financial analysis is presented

Conditional Volatility and Correlations of Weekly Returns and the VaR Analysis of 2008 Stock Market Crash

Modelling of conditional volatilities and correlations across asset returns is an integral part of portfolio decision making and risk management. Over the past three decades there has been a trend towards increased asset return correlations across markets, a trend which has been accentuated during the recent financial crisis. We shall examine the nature of asset return correlations using weekly returns on futures markets and investigate the extent to which multivariate volatility models proposed in the literature can be used to formally characterize and quantify market risk. In particular, we ask how adequate these models are for modelling market risk at times of financial crisis. In doing so we consider a multivariate t version of the Gaussian dynamic conditional correlation (DCC) model proposed by Engle (2002), and show that the t-DCC model passes the usual diagnostic tests based on probability integral transforms, but fails the value at risk (VaR) based diagnostics when applied to the post 2007 period that includes the recent financial crisis.volatilities and correlations, weekly returns, multivariate t, financial interdependence, VaR diagnostics, 2008 stock market crash

Understanding, Modeling and Managing Longevity Risk: Key Issues and Main Challenges

This article investigates the latest developments in longevity risk modelling, and explores the key risk management challenges for both the financial and insurance industries. The article discusses key definitions that are crucial for the enhancement of the way longevity risk is understood; providing a global view of the practical issues for longevity-linked insurance and pension products that have evolved concurrently with the steady increase in life expectancy since 1960s. In addition, the article frames the recent and forthcoming developments that are expected to action industry-wide changes as more effective regulation, designed to better assess and efficiently manage inherited risks, is adopted. Simultaneously, the evolution of longevity is intensifying the need for capital markets to be used to manage and transfer the risk through what are known as Insurance-Linked Securities (ILS). Thus, the article will examine the emerging scenarios, and will finally highlight some important potential developments for longevity risk management from a financial perspective with reference to the most relevant modelling and pricing practices in the banking industry.Longevity Risk ; securitization ; risk transfer ; incomplete market ; life insurance ; stochastic mortality ; pensions ; long term interest rate ; regulation ; population dynamics

Uncovering Long Memory in High Frequency UK Futures

Accurate volatility modelling is paramount for optimal risk management practices. One stylized feature of financial volatility that impacts the modelling process is long memory explored in this paper for alternative risk measures, observed absolute and squared returns for high frequency intraday UK futures. Volatility series for three different asset types, using stock index, interest rate and bond futures are analysed. Long memory is strongest for the bond contract. Long memory is always strongest for the absolute returns series and at a power transformation of kLong Memory, APARCH, High Frequency Futures

Uncovering Long Memory in High Frequency UK Futures

Accurate volatility modelling is paramount for optimal risk management practices. One stylized feature of financial volatility that impacts the modelling process is long memory explored in this paper for alternative risk measures, observed absolute and squared returns for high frequency intraday UK futures. Volatility series for three different asset types, using stock index, interest rate and bond futures are analysed. Long memory is strongest for the bond contract. Long memory is always strongest for the absolute returns series and at a power transformation of kLong Memory, APARCH, High Frequency Futures

Bank Model Risks Incorporated into the Operational Risk Management Process

The global economic and financial crisis substantiated the recognition that the mathematical and statistical models applied in the financial sector may lead to costly decision mistakes. The need for managing the risks associated with modelling also arises from the regulatory side. Since European regulations refer to modelling risks among operational risks, this article examines the process of evaluating and managing model risks and the possibility of integrating it into the operational risk management process. Based on practical experiences and the specificities of model risks, the basis of risk management in the case of model risks should be, instead of a capital cover, the formulation of a process replete with adequate controls. Moreover, a seamlessly functioning model risk management system can be designed within the process of operational risk management through the shared loss database, risk self-assessment and the definition of key risk indicators

Systematic risk analysis: first steps towards a new definition of beta

We suggest a new model-free definition of the beta coefficient, which plays an important rôle in systematic risk management. This setting, which is based on the existence of trends for financial time series via nonstandard analysis (Fliess M., Join C.: A mathematical proof of the existence of trends in financial time series, Proc. Int. Conf. Systems Theory: Modelling, Analysis and Control, Fes, 2009, online: http://hal.inria.fr/inria-00352834/en/) leads to convincing computer experiments which are easily implementable.Quantitative finance; risk analysis; beta; alpha; trends; technical analysis; estimation techniques; forecasting; abrupt changes; nonstandard analysis.

FINANCIAL MODELLING AND DECISIONAL PROCESSES IN RISK MANAGEMENT

vedasi SERIE NOTE N.13