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A semiparametric model for Generalized Pareto regression based on a dimension reduction assumption Hambuckers, julien ; Heuchenne, Cédric ; E-print/Working paper (2015) In this paper, we consider a regression model in which the tail of the conditional distribution of the response can be approximated by a Generalized Pareto distribution. Our model is based on a ... [more ▼] In this paper, we consider a regression model in which the tail of the conditional distribution of the response can be approximated by a Generalized Pareto distribution. Our model is based on a semiparametric single-index assumption on the conditional tail index; while no further assumption on the conditional scale parameter is made. The underlying dimension reduction assumption allows the procedure to be of prime interest in the case where the dimension of the covariates is high, in which case the purely nonparametric techniques fail while the purely parametric ones are too rough to correctly fit to the data. We derive asymptotic normality of the estimators that we define, and propose an iterative algorithm in order to perform their practical implementation. Our results are supported by some simulations and a practical application on a public database of operational losses. [less ▲] Detailed reference viewed: 53 (8 ULg)On the Importance of Quality, Liquidity Level and Liquidity Risk: A Markov-Switching Regime Approach Bazgour, Tarik ; Heuchenne, Cédric ; Sougné, Danielle Conference (2014, December 16) Detailed reference viewed: 49 (6 ULg)Identifying the best technical trading rule: a .632 bootstrap approach. Hambuckers, julien ; Heuchenne, Cédric Conference (2014, December 07) In this paper, we estimate the out-of-sample predictive ability of a set of trading rules. Usually, this ability is estimated using a rolling-window sample-splitting scheme, true out-of-sample data being ... [more ▼] In this paper, we estimate the out-of-sample predictive ability of a set of trading rules. Usually, this ability is estimated using a rolling-window sample-splitting scheme, true out-of-sample data being rarely available. We argue that this method makes a poor use of the available information and creates data mining possibilities. Instead, we introduce an alternative bootstrap approach, based on the .632 bootstrap principle. This method enables to build in-sample and out-of-sample bootstrap data sets that do not overlap and exhibit the same time dependencies. We illustrate our methodology on IBM and Microsoft daily stock prices, where we compare 11 trading rules specifications. For the data sets considered, two different filter rule specifications have the highest out-of-sample mean excess returns. However, all tested rules cannot beat a simple buy-and-hold strategy when trading at a daily frequency. [less ▲] Detailed reference viewed: 60 (10 ULg)Estimating the out-of-sample predictive ability of trading rules: a robust bootstrap approach Hambuckers, julien ; Heuchenne, Cédric E-print/Working paper (2014) In this paper, we estimate the out-of-sample predictive ability of a set of trading rules. Usually, this ability is estimated using a rolling-window sample-splitting scheme, true out-of-sample data being ... [more ▼] In this paper, we estimate the out-of-sample predictive ability of a set of trading rules. Usually, this ability is estimated using a rolling-window sample-splitting scheme, true out-of-sample data being rarely available. We argue that this method makes a poor use of the available information and creates data mining possibilities. Instead, we introduce an alternative bootstrap approach, based on the .632 bootstrap principle. This method enables to build in-sample and out-of-sample bootstrap data sets that do not overlap and exhibit the same time dependencies. We illustrate our methodology on IBM and Microsoft daily stock prices, where we compare 11 trading rules specifications. For the data sets considered, two different filter rule specifications have the highest out-of-sample mean excess returns. However, all tested rules cannot beat a simple buy-and-hold strategy when trading at a daily frequency. [less ▲] Detailed reference viewed: 67 (18 ULg)The application of the NSGA-II optimization method in designing control charts Faraz, Alireza ; Heuchenne, Cédric ; Conference (2014, June 04) The problem of designing control chart is formulated as a multi-objective optimization problem with the adjusted average time to signal as the statistical objective and the expected cost per hour as the ... [more ▼] The problem of designing control chart is formulated as a multi-objective optimization problem with the adjusted average time to signal as the statistical objective and the expected cost per hour as the economic objective. Then we try to find the Pareto-optimal designs in which the two objectives are minimized simultaneously by using the elitist non-dominated sorting genetic algorithm method. Through an illustrative example, the advantages of the proposed approach is shown by providing a list of viable optimal solutions and graphical representations, thereby bolding the advantage of flexibility and adaptability. [less ▲] Detailed reference viewed: 32 (3 ULg)The variable parameters T2 chart with run rules Faraz, Alireza ; ; Heuchenne, Cédric et al in Statistical Papers (2014), 55(4), 933-950 The Hotelling’s T2 control chart with variable parameters (VP T2) has been shown to have better statistical performance than other adaptive control schemes in detecting small to moderate process mean ... [more ▼] The Hotelling’s T2 control chart with variable parameters (VP T2) has been shown to have better statistical performance than other adaptive control schemes in detecting small to moderate process mean shifts. In this paper, we investigate the statistical performance of the VP T2 control chart coupled with run rules. We consider two well-known run rules schemes. Statistical performance is evaluated by using a Markov chain modeling the random shock mechanism of the monitored process. The in-control time interval of the process is assumed to follow an exponential distribution. A Genetic Algorithm has been designed to select the optimal chart design parameters. We provide an extensive numerical analysis indicating that the VP T2 control chart with run rules outperforms other charts for small sizes of the mean shift expressed through the Mahalanobis distance. [less ▲] Detailed reference viewed: 139 (14 ULg)On the importance of Quality, Liquidity Level and Liquidity Risk: A Markov-Switching Regime Approach Bazgour, Tarik ; Sougné, Danielle ; Heuchenne, Cédric Conference (2014, May 23) Detailed reference viewed: 30 (8 ULg)A new methodological approach for error distributions selection in Finance Hambuckers, julien ; Heuchenne, Cédric E-print/Working paper (2014) In this article, we propose a robust methodology to select the most appropriate error distribution candidate, in a classical multiplicative heteroscedastic model. In a first step, unlike to the ... [more ▼] In this article, we propose a robust methodology to select the most appropriate error distribution candidate, in a classical multiplicative heteroscedastic model. In a first step, unlike to the traditional approach, we don't use any GARCH-type estimation of the conditional variance. Instead, we propose to use a recently developed nonparametric procedure (Mercurio and Spokoiny, 2004): the Local Adaptive Volatility Estimation (LAVE). The motivation for using this method is to avoid a possible model misspecification for the conditional variance. In a second step, we suggest a set of estimation and model selection procedures (Berk-Jones tests, kernel density-based selection, censored likelihood score, coverage probability) based on the so-obtained residuals. These methods enable to assess the global fit of a given distribution as well as to focus on its behavior in the tails. Finally, we illustrate our methodology on three time series (UBS stock returns, BOVESPA returns and EUR/USD exchange rates). [less ▲] Detailed reference viewed: 68 (28 ULg)A new methodological approach for error distributions selection in Finance Hambuckers, julien ; Heuchenne, Cédric Conference (2014, April) In this article, we propose a robust methodology to select the most appropriate error distribution candidate, in a classical multiplicative heteroscedastic model. In a first step, unlike to the ... [more ▼] In this article, we propose a robust methodology to select the most appropriate error distribution candidate, in a classical multiplicative heteroscedastic model. In a first step, unlike to the traditional approach, we don't use any GARCH-type estimation of the conditional variance. Instead, we propose to use a recently developed nonparametric procedure (Mercurio and Spokoiny, 2004): the Local Adaptive Volatility Estimation (LAVE). The motivation for using this method is to avoid a possible model misspecification for the conditional variance. In a second step, we suggest a set of estimation and model selection procedures (Berk-Jones tests, kernel density-based selection, censored likelihood score, coverage probability) based on the so-obtained residuals. These methods enable to assess the global fit of a given distribution as well as to focus on its behavior in the tails. Finally, we illustrate our methodology on three time series (UBS stock returns, BOVESPA returns and EUR/USD exchange rates). [less ▲] Detailed reference viewed: 74 (31 ULg)Cox proportional hazard cure models with time-varying covariates Heuchenne, Cédric Conference (2014) Detailed reference viewed: 17 (1 ULg)Parametric conditional variance estimation in location-scale models with censored data Heuchenne, Cédric Conference (2014) Detailed reference viewed: 11 (1 ULg)Nonparametric estimation of conditional moments with right-censored selection biased data Heuchenne, Cédric Conference (2014) Detailed reference viewed: 10 (0 ULg)Double Objective Economic Statistical Design of the VPT2 Control Chart: Wald’s identity approach Faraz, Alireza ; Heuchenne, Cédric ; et al in Journal of Statistical Computation & Simulation (2014), 84 Recent studies have shown that applying the control chart by using a variable parameters (VP) scheme yields more rapid detection of assignable causes than the classical method of taking fixed sample sizes ... [more ▼] Recent studies have shown that applying the control chart by using a variable parameters (VP) scheme yields more rapid detection of assignable causes than the classical method of taking fixed sample sizes at fixed intervals of time. In this paper, the problem of economical statistical design of the VP T2 control chart is considered as a double-objective minimization problem with the statistical objective adjusted average time to signal and the economic objective expected cost per hour. Then we strive to find the Pareto-optimal designs in which the two objectives are met simultaneously by using a multi-objective Genetic Algorithm or GA. Through an illustrative example, we show that relatively large benefits accrue to the VP method relative to the classical policy; further another advantage of our approach is to provide a list of alternative solutions that can be explored graphically. This then ensures flexibility and adaptability, an important attribute of contemporary control chart design. [less ▲] Detailed reference viewed: 68 (10 ULg)Likelihood based inference for semi-competing risks Heuchenne, Cédric ; Laurent, Stéphane ; et al in Communications in Statistics : Simulation & Computation (2014), 43(5), 1112-1132 Detailed reference viewed: 61 (7 ULg)Estimation of the error distribution in nonparametric regression with cross-sectional data Heuchenne, Cédric ; Laurent, Géraldine E-print/Working paper (2014) In this article, we study the nonparametric regression model Y=m(X)+varepsilon where m(x)=E[Y|X=x] and sigma²(x)=Var[varepsilon|X=x] are unknown smooth functions, and the error varepsilon has zero mean ... [more ▼] In this article, we study the nonparametric regression model Y=m(X)+varepsilon where m(x)=E[Y|X=x] and sigma²(x)=Var[varepsilon|X=x] are unknown smooth functions, and the error varepsilon has zero mean and finite variance conditionally on X=x. The problem consists in estimating the cumulative distribution function of the error in a nonparametric way when the couple (X,Y) is obtained by cross-sectional sampling while the positive response Y can be right-censored. We propose a new estimator for the error distribution function based on the estimators of m(.) and sigma²(.) described in Heuchenne and Laurent 2014. A bootstrap procedure is developed to solve the critical problem of the smoothing parameter choice. We assess the performance of the proposed estimator through simulations. Finally, a data set based on the mortality of diabetics is analyzed. (Heuchenne Cédric and Laurent Géraldine, Nonparametric regression with cross-sectional data: an alternative to conditional product-limit estimators, 2014) [less ▲] Detailed reference viewed: 41 (10 ULg)Parametric conditional variance estimation in location-scale models with censored data Heuchenne, Cédric ; Laurent, Géraldine E-print/Working paper (2014) Suppose the random vector (X,Y) satisfies the regression model Y=m(X)+sigma(X)*varepsilon, where m(.)=E(Y|.), sigma²(.)=Var(Y|.) belongs to some parametric class {sigma _theta(.): theta in Theta} and ... [more ▼] Suppose the random vector (X,Y) satisfies the regression model Y=m(X)+sigma(X)*varepsilon, where m(.)=E(Y|.), sigma²(.)=Var(Y|.) belongs to some parametric class {sigma _theta(.): theta in Theta} and varepsilon is independent of X. The response Y is subject to random right censoring and the covariate X is completely observed. A new estimation procedure is proposed for sigma_theta(.) when m(.) is unknown. It is based on nonlinear least squares estimation extended to conditional variance in the censored case. The consistency and asymptotic normality of the proposed estimator are established. The estimator is studied via simulations and an important application is devoted to fatigue life data analysis. [less ▲] Detailed reference viewed: 42 (3 ULg)Nonparametric regression with cross-sectional data: an alternative to conditional product-limit estimators Heuchenne, Cédric ; Laurent, Géraldine E-print/Working paper (2014) Suppose the random vector (X,Y) satisfies the nonparametric regression model Y=m(X)+varepsilon, where m(x)=E[Y|X=x] and sigma²(x)=Var[varepsilon|X=x] are unknown smooth functions and the error varepsilon ... [more ▼] Suppose the random vector (X,Y) satisfies the nonparametric regression model Y=m(X)+varepsilon, where m(x)=E[Y|X=x] and sigma²(x)=Var[varepsilon|X=x] are unknown smooth functions and the error varepsilon has zero mean and finite variance conditionally on X=x. The pair (X,Y) is obtained by cross-sectional sampling involving left-truncated and right-censored responses. The considered model is completely nonparametric but the conditional truncation distribution is assumed to be known. The novelty of this work is twofold: first, it extends the results on cross-sectional data to the conditional case and second, it generalizes the length bias results in the conditional case to right censoring and to any truncation distribution. New estimators for m(.) and sigma²(.) are constructed and relevant tools are used to quickly provide the main asymptotic properties for this kind of estimators. Extensive simulations are carried out and show that the new estimators outperform classical nonparametric estimators for left-truncated and right-censored data (when the truncation model is known). Finally, a data set on the mortality of diabetics is analyzed. [less ▲] Detailed reference viewed: 43 (3 ULg)On the importance of Quality, Liquidity Level and Liquidity Risk: A Markov-Switching Regime Approach Bazgour, Tarik ; Sougné, Danielle ; Heuchenne, Cédric E-print/Working paper (2014) Detailed reference viewed: 52 (11 ULg)A Statistically adaptive sampling policy to the Hotelling's T2 Control Chart: Markov Chain Approach ; Faraz, Alireza ; Heuchenne, Cédric et al in Communications in Statistics : Theory & Methods (2014) Detailed reference viewed: 37 (4 ULg)A new methodological approach for error distributions selection Hambuckers, julien ; Heuchenne, Cédric Conference (2013, December 15) Since 2008 and its ﬁnancial crisis, an increasing attention has been devoted to the selection of an adequate error distribution in risk models, in particular for Value-at-Risk (VaR) predictions. We ... [more ▼] Since 2008 and its ﬁnancial crisis, an increasing attention has been devoted to the selection of an adequate error distribution in risk models, in particular for Value-at-Risk (VaR) predictions. We propose a robust methodology to select the most appropriate error distribution candidate, in a classical multiplicative heteroscedastic model. In a ﬁrst step, unlike to the traditional approach, we do not use any GARCH-type estimation of the conditional variance. Instead, we propose to use a recently developed nonparametric procedure: the Local Adaptive Volatility Estimation (LAVE). The motivation for using this method is to avoid a possible model misspeciﬁcation for the conditional variance. In a second step, we suggest a set of estimation and model selection procedures tests based on the so-obtained residuals. These methods enable to assess the global ﬁt of a given distribution as well as to focus on its behaviour in the tails. Finally, we illustrate our methodology on three time series (UBS stock returns, BOVESPA returns and EUR/USD exchange rates). [less ▲] Detailed reference viewed: 29 (9 ULg) |
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