SSRN Author: Mathieu BoudreaultMathieu Boudreault SSRN Content
http://www.ssrn.com/author=1883131
http://www.ssrn.com/rss/en-usFri, 08 Sep 2017 01:23:40 GMTeditor@ssrn.com (Editor)Fri, 08 Sep 2017 01:23:40 GMTwebmaster@ssrn.com (WebMaster)SSRN RSS Generator 1.0New: On a Joint Frequency and Severity Loss Model Applied to Earthquake RiskIn the seismological and geophysics literature, it is suggested by numerous authors that the elapsed time between two earthquakes at a given location should be represented by either an exponential or Weibull distribution. In addition, the seismic gap hypothesis states that large waiting times could provoke larger earthquakes. This will create a statistical dependence relationship between the frequency and magnitude components of any earthquake risk model. This paper investigates the actuarial, statistical and risk management implications of these two characteristics of earthquake risk. To do so, we introduce the conditional Weibull renewal process to count the number of earthquakes over a given time period and we introduce statistical dependence between the interarrival times and the force of each earthquake. An actuarial earthquake risk model based on these elements is presented and applied to Montreal (Quebec) earthquake data.
http://www.ssrn.com/abstract=3015906
http://www.ssrn.com/1623479.htmlThu, 07 Sep 2017 05:34:59 GMTREVISION: Mitigating Interest Rate Risk in Variable Annuities: An Analysis of Hedging Effectiveness Under Model RiskVariable annuities are investment vehicles offered by insurance companies that combine a life insurance policy with long-term financial guarantees. These guarantees expose the insurer to market risks, such as volatility and interest rate risks, which can only be managed with a hedging strategy. The objective of this article is to study the effectiveness of dynamic delta-rho hedging strategies for mitigating interest rate risk in variable annuities with either a guaranteed minimum death benefit (GMDB) or guaranteed minimum withdrawal benefit (GMWB) rider. Our analysis centers on three important practical issues: (i) the robustness of delta-rho hedging strategies to model uncertainty, (ii) the impact of guarantee features (maturity versus withdrawal benefits) on the performance of the hedging strategy, and (iii) the importance of hedging interest rate risk in either a low and stable or rising interest rate environment. Overall, we find that the impact of interest rate risk is equally ...
http://www.ssrn.com/abstract=2769927
http://www.ssrn.com/1621888.htmlThu, 31 Aug 2017 09:27:53 GMTREVISION: Firm-Specific Credit Risk Estimation in the Presence of Regimes and Noisy PricesSecurity prices are important inputs for estimating credit risk. Yet, to obtain an accurate firm-specific credit risk assessment, one needs a reliable model and a methodology that filters the elements unrelated to the firm’s fundamentals from market prices.
In this article, we introduce a hybrid credit risk model defined in a Markov-switching environment. It captures firm-specific changes in the leverage uncertainty during crises. We also propose a new efficient method to estimate the model, and a numerical scheme based on trinomial lattices to price credit derivatives. The estimation is finally performed on more than 200 firms using maximum likelihood estimation.
http://www.ssrn.com/abstract=2772370
http://www.ssrn.com/1594865.htmlSun, 28 May 2017 07:30:50 GMTREVISION: Maximum Likelihood Estimation of the Markov-Switching GARCH Model Based on a General Collapsing ProcedureThe Markov-switching GARCH model allows for a GARCH structure with time-varying parameters. This flexibility is unfortunately undermined by a path dependence problem which complicates the parameter estimation process. This problem led to the development of computationally intensive estimation methods and to simpler techniques based on an approximation of the model, known as collapsing procedures. This article develops an original algorithm to conduct maximum likelihood inference in the Markov-switching GARCH model, generalizing and improving previously proposed collapsing approaches. A new relationship between particle filtering and collapsing procedures is established which reveals that this algorithm corresponds to a deterministic particle filter. Simulation and empirical studies show that the proposed method allows for a fast and accurate estimation of the model.
http://www.ssrn.com/abstract=2365763
http://www.ssrn.com/1555050.htmlTue, 03 Jan 2017 16:00:22 GMTREVISION: Maximum Likelihood Estimation of the Markov-Switching GARCH Model Based on a General Collapsing ProcedureThe Markov-switching GARCH model allows for a GARCH structure with time-varying parameters. This flexibility is unfortunately undermined by a path dependence problem which complicates the parameter estimation process. This problem led to the development of computationally intensive estimation methods and to simpler techniques based on an approximation of the model, known as collapsing procedures. This article develops an original algorithm to conduct maximum likelihood inference in the Markov-switching GARCH model, generalizing and improving previously proposed collapsing approaches. A new relationship between particle filtering and collapsing procedures is established which reveals that this algorithm corresponds to a deterministic particle filter. Simulation and empirical studies show that the proposed method allows for a fast and accurate estimation of the model.
http://www.ssrn.com/abstract=2365763
http://www.ssrn.com/1554299.htmlWed, 28 Dec 2016 17:57:07 GMTREVISION: Maximum Likelihood Estimation of the Markov-Switching GARCH Model Based on a General Collapsing ProcedureThe Markov-switching GARCH model allows for a GARCH structure with time-varying parameters. This flexibility is unfortunately undermined by a path dependence problem which complicates the parameter estimation process. This problem led to the development of computationally intensive estimation methods and to simpler techniques based on an approximation of the model, known as collapsing procedures. This article develops an original algorithm to conduct maximum likelihood inference in the Markov-switching GARCH model, generalizing and improving previously proposed collapsing approaches. A new relationship between particle filtering and collapsing procedures is established which reveals that this algorithm corresponds to a deterministic particle filter. Simulation and empirical studies show that the proposed method allows for a fast and accurate estimation of the model.
http://www.ssrn.com/abstract=2365763
http://www.ssrn.com/1554198.htmlWed, 28 Dec 2016 09:20:46 GMT