Risk management implications of time-inconsistency: Model updating and recalibration of no-arbitrage models

Buraschi, Andrea; Corielli, Francesco

doi:10.1016/j.jbankfin.2005.02.002

Cited by 13 publications

(4 citation statements)

References 32 publications

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“…Second, it is unnecessary to frequently recalibrate random field models. As pointed out in Buraschi and Corielli (2005),the finite-factor models completely determines the future trend of the yield curve and thus its possible shapes in the future. For instance, the d-factor models can at most fit d points on the yield curve and the volatility curve on a specific date.…”

Section: Interest Rate Modeling In the Random Field Settingmentioning

confidence: 99%

A Random Field LIBOR Market Model

Xu²

2014

Journal of Futures Markets

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A random field LIBOR market model (RFLMM) is proposed by extending the LIBOR market model, with interest rate uncertainties modeled via a random field. First, closed-form formulas for pricing caplet and swaption are derived. Then the random field LIBOR market model is integrated with the lognormal-mixture model to capture the implied volatility skew/smile. Finally, the model is calibrated to cap volatility surface and swaption volatilities. Numerical results show that the random field LIBOR market model can potentially outperform the LIBOR market model in capturing caplet volatility smile and the pricing of swaptions, in addition to possessing other advantages documented in the previous literature (no need of frequent recalibration or to specify the number of factors in advance).

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Section: Interest Rate Modeling In the Random Field Settingmentioning

confidence: 99%

A Random Field LIBOR Market Model

Xu²

2014

Journal of Futures Markets

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“…LTCM's trading strategies were based on the assumption of market reversions to historical means (see Lucas 2001). As the financial market is becoming more and more complicated, it is of great importance to introduce new methodologies to address model risks (see Beitz and Ehrhardt 2010, Buraschi and Corielli 2005, Sibbertsen et al. 2008).…”

Section: Research Frameworkmentioning

confidence: 99%

Operational Risk in Financial Services: A Review and New Research Opportunities

Mei

2017

Production and Operations Management

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We present a framework to describe and analyze operational risk in financial services from an operations management perspective, focusing in particular on process design, process management, and human behavior aspects. The financial services industry differs from other service industries in ways that affect the nature of the operational risks it is subject to. In recent decades, many books and papers have focused on operational risk in financial services; however, this literature has focused mainly on the conceptual and statistical aspects of operational risk management and not on its operational aspects. Operational risk in financial services has not received much attention from the operations management community. The framework presented here is based on the premise that operational risk in financial services can reap significant benefits from research done in the theory and practice of operations management in manufacturing industries as well as in other services industries. The objective of this study is to propose particular challenges and questions raised in the practice of operational risk management that may stimulate future research in this particular area of operations management.

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“…In a recent study De Rossi [13] applies consistency results to propose a consistent exponential dynamic model, and estimates it using data on LIBOR and UK swap rates. On the other hand, Buraschi and Corielli [12] add some results to this theoretical framework indicating that the use of inconsistent parametric families to obtain smooth interest rate curves violates the standard self financing arguments of replicating strategies, with direct consequences in risk management procedures.…”

Section: Introductionmentioning

confidence: 99%

On the calibration of a Gaussian Heath–Jarrow–Morton model using consistent forward rate curves

Falcó

Navarro

Nave

2011

Quantitative Finance

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In this paper we propose a calibration algorithm, by using a consistent family of yield curves, that fits a Gaussian Heath-Jarrow-Morton model jointly to the implied volatilities of caps and zero-coupon bond prices. The calibration approach is evaluated in terms of in-sample data fitting as well as stability of parameter estimates. Furthermore, the efficiency is tested against a non-consistent traditional method by using simulated and market data. Also we discuss the convergence of the algorithm by means of Monte Carlo simulations. 1

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Risk management implications of time-inconsistency: Model updating and recalibration of no-arbitrage models

Cited by 13 publications

References 32 publications

A Random Field LIBOR Market Model

A Random Field LIBOR Market Model

Operational Risk in Financial Services: A Review and New Research Opportunities

On the calibration of a Gaussian Heath–Jarrow–Morton model using consistent forward rate curves

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