Measuring and managing the credit exposure of derivatives portfolios

CONCLUSION The analysis of the exposure measurement problem has shown that the proper measurement of counterparty exposure for portfolios of derivatives transactions is a complex task that cannot be performed without making a lot of simplifying assumptions. Because of the complicated interaction of correlation effects and offsettings from different transactions, the single transaction framework which is currently used by most banks is definitely not capable of accurately determining the portfolio credit risk. When simulation techniques are applied to estimate exposure, the accuracy of exposure estimations can be increased significantly. However, a lot of modelling choices has to be made concerning the valuation of transactions and the stochastic model of underlying market rates. Because the system has to make projections of market rates into the far future, the choice of an appropriate stochastic model for market rate dynamics is crucial in order to prevent unreasonable scenarios. The predominant application of models based on Brownian Motion in today’s bank risk management therefore leads to questionable results in respect to derivatives exposure evaluation

Practical volatility and correlation modeling for financial market risk management

What do academics have to offer market risk management practitioners in financial institutions? Current industry practice largely follows one of two extremely restrictive approaches: historical simulation or RiskMetrics. In contrast, we favor flexible methods based on recent developments in financial econometrics, which are likely to produce more accurate assessments of market risk. Clearly, the demands of real-world risk management in financial institutions - in particular, real-time risk tracking in very high-dimensional situations - impose strict limits on model complexity. Hence we stress parsimonious models that are easily estimated, and we discuss a variety of practical approaches for high-dimensional covariance matrix modeling, along with what we see as some of the pitfalls and problems in current practice. In so doing we hope to encourage further dialog between the academic and practitioner communities, hopefully stimulating the development of improved market risk management technologies that draw on the best of both worlds

Semiparametric estimation for a class of time-inhomogenous diffusion processes

Copyright @ 2009 Institute of Statistical Science, Academia SinicaWe develop two likelihood-based approaches to semiparametrically estimate a class of time-inhomogeneous diffusion processes: log penalized splines (P-splines) and the local log-linear method. Positive volatility is naturally embedded and this positivity is not guaranteed in most existing diffusion models. We investigate different smoothing parameter selections. Separate bandwidths are used for drift and volatility estimation. In the log P-splines approach, different smoothness for different time varying coefficients is feasible by assigning different penalty parameters. We also provide theorems for both approaches and report statistical inference results. Finally, we present a case study using the weekly three-month Treasury bill data from 1954 to 2004. We find that the log P-splines approach seems to capture the volatility dip in mid-1960s the best. We also present an application to calculate a financial market risk measure called Value at Risk (VaR) using statistical estimates from log P-splines

Unbiased estimate of dynamic term structure models

Affine dynamic term structure models (DTSMs) are the standard finance representation of the yield curve. However, the literature on DTSMs has ignored the coefficient bias that plagues estimated autoregressive models of persistent time series. We introduce new simulation-based methods for reducing or even eliminating small-sample bias in empirical affine Gaussian DTSMs. With these methods, we show that conventional estimates of DTSM coefficients are severely biased, which results in misleading estimates of expected future short-term interest rates and long-maturity term premia. Our unbiased DTSM estimates imply risk-neutral rates and term premia that are more plausible from a macro-finance perspective.Interest rates

Regulating Market Risks in Banks: A Comparison of Alternate Regulatory Regimes

Regulators have traditionally used simple models to measure the capital adequacy of banks. The growing internationalisation and universalisation of banking operations have meant that the same is no longer possible, as banks face increasing, and increasingly opaque, market risk. The significance of market risk has also been acknowledged in the New Capital Accord enunciated by the Basel Committee in 1999. The focus of the paper is on market risk, that is, any market related factor that affects the value of a position in the financial instrument or a portfolio of instruments. As it stands at present, the three commonly used approaches to regulating market risks in banks include the building block approach, internal model approach and precommitment approach. The paper evaluates the pros and cons of the various approaches and concludes with a discussion of the applicability of these models in the Indian context.VaR; banking; India; market risk

Demographic risk transfer: is it worth for annuity providers? ICER Working Paper

Longevity risk transfer is a popular choice for annuity providers such as pension funds. This paper formalizes the trade-off between the cost and the risk relief of such choice, when the annuity provider uses value-at-risk to assess risk. Using first-order approximations we show that, if the transfer is fairly priced and the aim of the fund is to maximize returns, the funds' alternatives can be represented in the plane expected return-VaR. We build a risk-return frontier, along which the optimal transfer choices of the fund are located and calibrated it to the 2010 UK annuity and bond market

Interest Rate Risk of Banking Accounts: Measurement Using the VaR Framework

In order to measure the interest rate risk of banking accounts such as deposits and loans, this paper extends the value at risk (hereafter, VaR) analysis framework, which is useful for the risk evaluation of trading accounts. In order to apply the VaR concept derived from trading accounts to banking accounts, we should take into account the following issues: (1) the longer risk evaluation period because of the inflexibility of adjustability of banking account positions; (2) the evaluation of risk included in the administered rates (the long-term prime rate and the short-term prime rate); and (3) the prepayment risk (associated with housing loans, etc.). Therefore, in this paper we first construct a VaR model including a term-structure model to express the stochastic process of market rate, the administered rate model, and the prepayment function model. Then, we perform a simulation using an imaginary portfolio to analyze the factors determining interest rate risk. In conclusion, it has been proved that the factor of administered rates increases interest rate risk both in single products and in a portfolio. Taking into account the behavior of customers who want better interest rate conditions, the factor of prepayment decreases the present value, which is itself the basis of calculating risk. Finally, we perform a sensitivity analysis of model parameters to show the magnitude of model risk.