Bruno Remillard | HEC Montréal (Ecole des Hautes Etudes Commerciales) (original) (raw)
Papers by Bruno Remillard
arXiv (Cornell University), Apr 10, 2023
A convergence theorem for martingales with càdlàg trajectories (right continuous with left limits... more A convergence theorem for martingales with càdlàg trajectories (right continuous with left limits everywhere) is obtained in the sense of the weak dual topology on Hilbert space, under conditions that are much weaker than those required for any of the usual Skorohod topologies. Examples are provided to show that these conditions are also very easy to check and yield useful asymptotic results, especially when the limit is a mixture of stochastic processes with discontinuities.
Modern Economy, 2017
We propose optimal mean-variance dynamic hedging strategies in discrete time under a multivariate... more We propose optimal mean-variance dynamic hedging strategies in discrete time under a multivariate Gaussian regime-switching model. The methodology, which also performs pricing, is robust to time-varying and clustering risk observed in financial time series. As such, it overcomes the main theoretical drawbacks of the Black-Scholes model. To support our approach, we provide goodness-of-fit tests to validate the model and for choosing the appropriate number of regimes, and we illustrate the methodology using monthly S & P 500 vanilla options prices. Then, we present the associated out-of-sample hedging results in the context of harvesting the implied versus realized volatility premium. Using the proposed methodology, the Sharpe ratio derived from the strategy doubles over the Black-Scholes delta-hedging methodology.
arXiv (Cornell University), Jul 28, 2015
Parrondo's paradox is extended to regime switching random walks in random environments. The parad... more Parrondo's paradox is extended to regime switching random walks in random environments. The paradoxical behavior of the resulting random walk is explained by the effect of the random environment. Full characterization of the asymptotic behavior is achieved in terms of the dimensions of some random subspaces occurring in Oseledec's theorem. The regime switching mechanism gives our models a richer and more complex asymptotic behavior than the simple random walks in random environments appearing in the literature, in terms of transience and recurrence.
Journal of Computational Finance, Dec 1, 2011
A discretization scheme for nonnegative diffusion processes is proposed and the convergence of th... more A discretization scheme for nonnegative diffusion processes is proposed and the convergence of the corresponding sequence of approximate processes is proved using the martingale problem framework. Motivations for this scheme come typically from finance, especially for path-dependent option pricing. The scheme is simple: one only needs to find a nonnegative distribution whose mean and variance satisfy a simple condition to apply it. Then, for virtually any (path-dependent) payoff, Monte Carlo option prices obtained from this scheme will converge to the theoretical price. Examples of models and diffusion processes for which the scheme applies are provided.
arXiv (Cornell University), Jan 17, 2023
When the limiting compensator of a sequence of martingales is continuous, we obtain a weak conver... more When the limiting compensator of a sequence of martingales is continuous, we obtain a weak convergence theorem for the martingales; the limiting process can be written as a Brownian motion evaluated at the compensator and we find sufficient conditions for both processes to be independent. As examples of applications, we revisit some known results for the occupation times of Brownian motion and symmetric random walks. In the latter case, our proof is much simpler than the construction of strong approximations. Furthermore, we extend finite dimensional convergence of statistical estimators of financial volatility measures to convergence as stochastic processes.
Springer eBooks, 2012
ABSTRACT In this article we study the price of an American style option based on hedging the unde... more ABSTRACT In this article we study the price of an American style option based on hedging the underlying assets at discrete time. Like its European style analog, the value of the option is not given in general by an expectation with respect to an equivalent martingale measure. We provide the optimal solution that minimizes the hedging error variance. When the assets dynamics are Markovian or a component of a Markov process, the solution can be approximated easily by numerical methods already proposed for pricing American options. We proceed to a Monte Carlo experiment in which the hedging performance of the solution is evaluated. For assets returns that are either Gaussian or Variance Gamma, it is shown that the proposed solution results in lower root mean square hedging error than with traditional delta hedging.
Les Cahiers du GERAD, Sep 1, 2003
ABSTRACT Wang & Wells ["J. Amer. Statist. Assoc." 95 (2000) 62] describ... more ABSTRACT Wang & Wells ["J. Amer. Statist. Assoc." 95 (2000) 62] describe a non-parametric approach for checking whether the dependence structure of a random sample of censored bivariate data is appropriately modelled by a given family of Archimedean copulas. Their procedure is based on a truncated version of the Kendall process introduced by Genest & Rivest ["J. Amer. Statist. Assoc." 88 (1993) 1034] and later studied by Barbe "et al". ["J. Multivariate Anal." 58 (1996) 197]. Although Wang & Wells (2000) determine the asymptotic behaviour of their truncated process, their model selection method is based exclusively on the observed value of its "L"-super-2-norm. This paper shows how to compute asymptotic "p"-values for various goodness-of-fit test statistics based on a non-truncated version of Kendall's process. Conditions for weak convergence are met in the most common copula models, whether Archimedean or not. The empirical behaviour of the proposed goodness-of-fit tests is studied by simulation, and power comparisons are made with a test proposed by Shih ["Biometrika" 85 (1998) 189] for the gamma frailty family. Copyright 2006 Board of the Foundation of the Scandinavian Journal of Statistics..
Social Science Research Network, 2009
We propose an innovative approach for dynamic portfolio insurance that overcomes many of the limi... more We propose an innovative approach for dynamic portfolio insurance that overcomes many of the limitations of the earlier techniques. We transform the Payoff Distribution Model, originally introduced by Dybvig [J. Business, 1988, 61(3), 369-393] as a performance measure, into a fund management tool. This approach allows us to generate funds with pre-specified distributional properties. Specifically, we generate funds that are characterized by a Left Truncated Gaussian distribution and then demonstrate out of sample, using different performance and risk measures, that this approach to managing market exposure leads to a better risk control at a lower cost than more popular techniques such as the CPPI.
Les Cahiers du GERAD, Feb 1, 2005
Probability Theory and Related Fields, Jun 1, 1994
ABSTRACT
Comprehensive R Archive Network (CRAN), Oct 9, 2021
Journal of Multivariate Analysis, 2021
Statistical Science, 2021
Donald Andrew Dawson (Don Dawson) was born in 1937. He received a bachelor's degree in 1958 and a... more Donald Andrew Dawson (Don Dawson) was born in 1937. He received a bachelor's degree in 1958 and a master's degree in 1959 from McGill University and a Ph.D. in 1963 from M.I.T. under the supervision of Henry P. McKean, Jr. Following an appointment at McGill University as professor for 7 years, he joined Carleton University in 1970 where he remained for the rest of his career. Among his many contributions to the theory of stochastic processes, his work leading to the creation of the Dawson-Watanabe superprocess and the analysis of its remarkable properties in describing the evolution in space and time of populations, stand out as milestones of modern probability theory. His numerous papers span the whole gamut of contemporary hot areas, notably the study of stochastic evolution equations, measure-valued processes, McKean-Vlasov limits, hierarchical structures, super-Brownian motion, as well as branching, catalytic and historical processes. He has over 200 refereed publications and 8 monographs, with an impressive number of citations, more than 7000. He is elected Fellow of the Royal Society and of the Royal Society of Canada, as well as Gold medalist of the Statistical Society of Canada and elected Fellow of the Institute of Mathematical Statistics. We realized this interview to celebrate the outstanding contribution of Don Dawson to 50 years of Stochastics at Carleton University.
SSRN Electronic Journal, 2018
We consider several time series and for each of them, we fit an appropriate dynamic parametric mo... more We consider several time series and for each of them, we fit an appropriate dynamic parametric model. This produces serially independent error terms for each time series. The dependence between these error terms is then modeled by a regime-switching copula. The EM algorithm is used for estimating the parameters and a sequential goodness-of-fit procedure based on Cramer-von Mises statistics is proposed to select the appropriate number of regimes. Numerical experiments are performed to assess the validity of the proposed methodology. As an example of application, we evaluate a European put-on-max option on the returns of two assets. In order to facilitate the use of our methodology, we have built a R package HMMcopula available on CRAN.
The Annals of Applied Probability, 2019
This article is concerned with the fluctuation analysis and the stability properties of a class o... more This article is concerned with the fluctuation analysis and the stability properties of a class of one-dimensional Riccati diffusions. These onedimensional stochastic differential equations exhibit a quadratic drift function and a non-Lipschitz continuous diffusion function. We present a novel approach, combining tangent process techniques, Feynman-Kac path integration and exponential change of measures, to derive sharp exponential decays to equilibrium. We also provide uniform estimates with respect to the time horizon, quantifying with some precision the fluctuations of these diffusions around a limiting deterministic Riccati differential equation. These results provide a stronger and almost sure version of the conventional central limit theorem. We illustrate these results in the context of ensemble Kalman-Bucy filtering. To the best of our knowledge, the exponential stability and the fluctuation analysis developed in this work are the first results of this kind for this class of nonlinear diffusions.
ABSTRACT In view of applications to diagnostic tests of ARMA models, the asymptotic behavior of m... more ABSTRACT In view of applications to diagnostic tests of ARMA models, the asymptotic behavior of multivariate empirical and copula processes based on residuals of ARMA models is investigated. Multivariate empirical processes based on squared residuals and other functions of the residuals are also investigated. It is shown how these processes can be used to develop distribution free tests of change-point analysis and serial independence. It is also demonstrated that these empirical processes provide an easy mechanism for developing goodness-of-fit tests for the distribution of the innovations, and that the well-known Lilliefors test can be applied to the residuals of ARMA models without any change.
We consider several time series and for each of them, we fit an appropriate dynamic parametric mo... more We consider several time series and for each of them, we fit an appropriate dynamic parametric model. This produces serially independent error terms for each time series. The dependence between these error terms is then modeled by a regime-switching copula. The EM algorithm is used for estimating the parameters and a sequential goodness-of-fit procedure based on Cramér-von Mises statistics is proposed to select the appropriate number of regimes. Numerical experiments are performed to assess the validity of the proposed methodology. As an example of application, we evaluate a European put-on-max option on the returns of two assets. In order to facilitate the use of our methodology, we have built a R package HMMcopula available on CRAN.
Journal of Multivariate Analysis, 2017
The empirical checkerboard copula is a multilinear extension of the empirical copula, which plays... more The empirical checkerboard copula is a multilinear extension of the empirical copula, which plays a key role for inference in copula models. Weak convergence of the corresponding empirical process based on a random sample from the underlying multivariate distribution is established here under broad conditions which allow for arbitrary univariate margins. It is only required that the underlying checkerboard copula has continuous first-order partial derivatives on an open subset of the unit hypercube. This assumption is very weak and always satisfied when the margins are discrete. When the margins are continuous, one recovers the limit of the classical empirical copula process under conditions which are comparable to the weakest ones currently available in the literature. A multiplier bootstrap method is also proposed to replicate the limiting process and its validity is established. The empirical checkerboard copula is further shown to be a more precise estimator of the checkerboard copula than the empirical copula based on jittered data. Finally, the weak convergence of the empirical checkerboard copula process is shown to be sufficiently strong to derive the asymptotic behavior of a broad class of functionals that are directly relevant for the development of rigorous statistical methodology for copula models with arbitrary margins.
arXiv (Cornell University), Apr 10, 2023
A convergence theorem for martingales with càdlàg trajectories (right continuous with left limits... more A convergence theorem for martingales with càdlàg trajectories (right continuous with left limits everywhere) is obtained in the sense of the weak dual topology on Hilbert space, under conditions that are much weaker than those required for any of the usual Skorohod topologies. Examples are provided to show that these conditions are also very easy to check and yield useful asymptotic results, especially when the limit is a mixture of stochastic processes with discontinuities.
Modern Economy, 2017
We propose optimal mean-variance dynamic hedging strategies in discrete time under a multivariate... more We propose optimal mean-variance dynamic hedging strategies in discrete time under a multivariate Gaussian regime-switching model. The methodology, which also performs pricing, is robust to time-varying and clustering risk observed in financial time series. As such, it overcomes the main theoretical drawbacks of the Black-Scholes model. To support our approach, we provide goodness-of-fit tests to validate the model and for choosing the appropriate number of regimes, and we illustrate the methodology using monthly S & P 500 vanilla options prices. Then, we present the associated out-of-sample hedging results in the context of harvesting the implied versus realized volatility premium. Using the proposed methodology, the Sharpe ratio derived from the strategy doubles over the Black-Scholes delta-hedging methodology.
arXiv (Cornell University), Jul 28, 2015
Parrondo's paradox is extended to regime switching random walks in random environments. The parad... more Parrondo's paradox is extended to regime switching random walks in random environments. The paradoxical behavior of the resulting random walk is explained by the effect of the random environment. Full characterization of the asymptotic behavior is achieved in terms of the dimensions of some random subspaces occurring in Oseledec's theorem. The regime switching mechanism gives our models a richer and more complex asymptotic behavior than the simple random walks in random environments appearing in the literature, in terms of transience and recurrence.
Journal of Computational Finance, Dec 1, 2011
A discretization scheme for nonnegative diffusion processes is proposed and the convergence of th... more A discretization scheme for nonnegative diffusion processes is proposed and the convergence of the corresponding sequence of approximate processes is proved using the martingale problem framework. Motivations for this scheme come typically from finance, especially for path-dependent option pricing. The scheme is simple: one only needs to find a nonnegative distribution whose mean and variance satisfy a simple condition to apply it. Then, for virtually any (path-dependent) payoff, Monte Carlo option prices obtained from this scheme will converge to the theoretical price. Examples of models and diffusion processes for which the scheme applies are provided.
arXiv (Cornell University), Jan 17, 2023
When the limiting compensator of a sequence of martingales is continuous, we obtain a weak conver... more When the limiting compensator of a sequence of martingales is continuous, we obtain a weak convergence theorem for the martingales; the limiting process can be written as a Brownian motion evaluated at the compensator and we find sufficient conditions for both processes to be independent. As examples of applications, we revisit some known results for the occupation times of Brownian motion and symmetric random walks. In the latter case, our proof is much simpler than the construction of strong approximations. Furthermore, we extend finite dimensional convergence of statistical estimators of financial volatility measures to convergence as stochastic processes.
Springer eBooks, 2012
ABSTRACT In this article we study the price of an American style option based on hedging the unde... more ABSTRACT In this article we study the price of an American style option based on hedging the underlying assets at discrete time. Like its European style analog, the value of the option is not given in general by an expectation with respect to an equivalent martingale measure. We provide the optimal solution that minimizes the hedging error variance. When the assets dynamics are Markovian or a component of a Markov process, the solution can be approximated easily by numerical methods already proposed for pricing American options. We proceed to a Monte Carlo experiment in which the hedging performance of the solution is evaluated. For assets returns that are either Gaussian or Variance Gamma, it is shown that the proposed solution results in lower root mean square hedging error than with traditional delta hedging.
Les Cahiers du GERAD, Sep 1, 2003
ABSTRACT Wang & Wells ["J. Amer. Statist. Assoc." 95 (2000) 62] describ... more ABSTRACT Wang & Wells ["J. Amer. Statist. Assoc." 95 (2000) 62] describe a non-parametric approach for checking whether the dependence structure of a random sample of censored bivariate data is appropriately modelled by a given family of Archimedean copulas. Their procedure is based on a truncated version of the Kendall process introduced by Genest & Rivest ["J. Amer. Statist. Assoc." 88 (1993) 1034] and later studied by Barbe "et al". ["J. Multivariate Anal." 58 (1996) 197]. Although Wang & Wells (2000) determine the asymptotic behaviour of their truncated process, their model selection method is based exclusively on the observed value of its "L"-super-2-norm. This paper shows how to compute asymptotic "p"-values for various goodness-of-fit test statistics based on a non-truncated version of Kendall's process. Conditions for weak convergence are met in the most common copula models, whether Archimedean or not. The empirical behaviour of the proposed goodness-of-fit tests is studied by simulation, and power comparisons are made with a test proposed by Shih ["Biometrika" 85 (1998) 189] for the gamma frailty family. Copyright 2006 Board of the Foundation of the Scandinavian Journal of Statistics..
Social Science Research Network, 2009
We propose an innovative approach for dynamic portfolio insurance that overcomes many of the limi... more We propose an innovative approach for dynamic portfolio insurance that overcomes many of the limitations of the earlier techniques. We transform the Payoff Distribution Model, originally introduced by Dybvig [J. Business, 1988, 61(3), 369-393] as a performance measure, into a fund management tool. This approach allows us to generate funds with pre-specified distributional properties. Specifically, we generate funds that are characterized by a Left Truncated Gaussian distribution and then demonstrate out of sample, using different performance and risk measures, that this approach to managing market exposure leads to a better risk control at a lower cost than more popular techniques such as the CPPI.
Les Cahiers du GERAD, Feb 1, 2005
Probability Theory and Related Fields, Jun 1, 1994
ABSTRACT
Comprehensive R Archive Network (CRAN), Oct 9, 2021
Journal of Multivariate Analysis, 2021
Statistical Science, 2021
Donald Andrew Dawson (Don Dawson) was born in 1937. He received a bachelor's degree in 1958 and a... more Donald Andrew Dawson (Don Dawson) was born in 1937. He received a bachelor's degree in 1958 and a master's degree in 1959 from McGill University and a Ph.D. in 1963 from M.I.T. under the supervision of Henry P. McKean, Jr. Following an appointment at McGill University as professor for 7 years, he joined Carleton University in 1970 where he remained for the rest of his career. Among his many contributions to the theory of stochastic processes, his work leading to the creation of the Dawson-Watanabe superprocess and the analysis of its remarkable properties in describing the evolution in space and time of populations, stand out as milestones of modern probability theory. His numerous papers span the whole gamut of contemporary hot areas, notably the study of stochastic evolution equations, measure-valued processes, McKean-Vlasov limits, hierarchical structures, super-Brownian motion, as well as branching, catalytic and historical processes. He has over 200 refereed publications and 8 monographs, with an impressive number of citations, more than 7000. He is elected Fellow of the Royal Society and of the Royal Society of Canada, as well as Gold medalist of the Statistical Society of Canada and elected Fellow of the Institute of Mathematical Statistics. We realized this interview to celebrate the outstanding contribution of Don Dawson to 50 years of Stochastics at Carleton University.
SSRN Electronic Journal, 2018
We consider several time series and for each of them, we fit an appropriate dynamic parametric mo... more We consider several time series and for each of them, we fit an appropriate dynamic parametric model. This produces serially independent error terms for each time series. The dependence between these error terms is then modeled by a regime-switching copula. The EM algorithm is used for estimating the parameters and a sequential goodness-of-fit procedure based on Cramer-von Mises statistics is proposed to select the appropriate number of regimes. Numerical experiments are performed to assess the validity of the proposed methodology. As an example of application, we evaluate a European put-on-max option on the returns of two assets. In order to facilitate the use of our methodology, we have built a R package HMMcopula available on CRAN.
The Annals of Applied Probability, 2019
This article is concerned with the fluctuation analysis and the stability properties of a class o... more This article is concerned with the fluctuation analysis and the stability properties of a class of one-dimensional Riccati diffusions. These onedimensional stochastic differential equations exhibit a quadratic drift function and a non-Lipschitz continuous diffusion function. We present a novel approach, combining tangent process techniques, Feynman-Kac path integration and exponential change of measures, to derive sharp exponential decays to equilibrium. We also provide uniform estimates with respect to the time horizon, quantifying with some precision the fluctuations of these diffusions around a limiting deterministic Riccati differential equation. These results provide a stronger and almost sure version of the conventional central limit theorem. We illustrate these results in the context of ensemble Kalman-Bucy filtering. To the best of our knowledge, the exponential stability and the fluctuation analysis developed in this work are the first results of this kind for this class of nonlinear diffusions.
ABSTRACT In view of applications to diagnostic tests of ARMA models, the asymptotic behavior of m... more ABSTRACT In view of applications to diagnostic tests of ARMA models, the asymptotic behavior of multivariate empirical and copula processes based on residuals of ARMA models is investigated. Multivariate empirical processes based on squared residuals and other functions of the residuals are also investigated. It is shown how these processes can be used to develop distribution free tests of change-point analysis and serial independence. It is also demonstrated that these empirical processes provide an easy mechanism for developing goodness-of-fit tests for the distribution of the innovations, and that the well-known Lilliefors test can be applied to the residuals of ARMA models without any change.
We consider several time series and for each of them, we fit an appropriate dynamic parametric mo... more We consider several time series and for each of them, we fit an appropriate dynamic parametric model. This produces serially independent error terms for each time series. The dependence between these error terms is then modeled by a regime-switching copula. The EM algorithm is used for estimating the parameters and a sequential goodness-of-fit procedure based on Cramér-von Mises statistics is proposed to select the appropriate number of regimes. Numerical experiments are performed to assess the validity of the proposed methodology. As an example of application, we evaluate a European put-on-max option on the returns of two assets. In order to facilitate the use of our methodology, we have built a R package HMMcopula available on CRAN.
Journal of Multivariate Analysis, 2017
The empirical checkerboard copula is a multilinear extension of the empirical copula, which plays... more The empirical checkerboard copula is a multilinear extension of the empirical copula, which plays a key role for inference in copula models. Weak convergence of the corresponding empirical process based on a random sample from the underlying multivariate distribution is established here under broad conditions which allow for arbitrary univariate margins. It is only required that the underlying checkerboard copula has continuous first-order partial derivatives on an open subset of the unit hypercube. This assumption is very weak and always satisfied when the margins are discrete. When the margins are continuous, one recovers the limit of the classical empirical copula process under conditions which are comparable to the weakest ones currently available in the literature. A multiplier bootstrap method is also proposed to replicate the limiting process and its validity is established. The empirical checkerboard copula is further shown to be a more precise estimator of the checkerboard copula than the empirical copula based on jittered data. Finally, the weak convergence of the empirical checkerboard copula process is shown to be sufficiently strong to derive the asymptotic behavior of a broad class of functionals that are directly relevant for the development of rigorous statistical methodology for copula models with arbitrary margins.