How Can Municipalities in British Columbia and Quebec Contribute to Flood Risk Reduction? - Deschamps, Bernard; Bourdeau-Brien, Michael; Boudreault, Mathieu (Institute on Municipal Finance and Governance, 2023)
Flood-related losses are on the rise in Canada and private insurance remains costly or unavailable in high-risk areas. Despite the introduction of overland flood insurance in 2015, following the federal government’s invitation to the insurance industry to participate in flood risk-sharing, federal and provincial disaster financial assistance programs still cover a large portion of these costs. As the risks increase, governments are questioning the sustainability of using taxpayers’ money to finance such losses, leaving municipalities with significant residual risk. The growing number of people and assets occupying flood-prone areas, including public infrastructure, has contributed to the sharp increase in flood damage costs. Based on a literature review and discussions with experts, this paper describes the municipal role in flood-risk management, and shows how provincial and federal financial assistance to municipalities for flood damage in British Columbia and Québec may be counterproductive in fostering flood-risk management at the municipal level. We conclude that municipalities can play a more proactive role in incorporating risk reduction as the key objective of disaster financial assistance and propose three specific policy instruments to help reduce the growing number of people living in flood zones: flood mapping, land-use planning, and the relocation of high-risk properties.

On a risk model with dependence between interclaim arrivals and claim sizes - Boudreault, Mathieu; Cossette, Hélène; Landriault, David; Marceau, Etienne (Scandinavian Actuarial Journal, 2006)

Multivariate Models of Equity Returns for Investment Guarantees Valuation - Boudreault, Mathieu; Panneton, Christian-Marc (North American Actuarial Journal, 2009)

An Out-of-Sample Analysis of Investment Guarantees for Equity-Linked Products: Lessons from the Financial Crisis of the Late 2000s - Augustyniak, Maciej; Boudreault, Mathieu (North American Actuarial Journal, 2012)

Recovery rate risk and credit spreads in a hybrid credit risk model - Boudreault, Mathieu; Gauthier, Geneviève; Thomassin, Tommy (The Journal of Credit Risk, 2013)

Contagion effect on bond portfolio risk measures in a hybrid credit risk model - Boudreault, Mathieu; Gauthier, Geneviève; Thomassin, Tommy (Finance Research Letters, 2014)

Risk models with dependence between claim occurrences and severities for Atlantic hurricanes - Boudreault, Mathieu; Cossette, Hélène; Marceau, Étienne (Insurance: Mathematics and Economics, 2014)

Changes in large-scale controls of Atlantic tropical cyclone activity with the phases of the Atlantic multidecadal oscillation - Caron, Louis-Philippe; Boudreault, Mathieu; Bruyère, Cindy L. (Climate Dynamics, 2015)

On the Variability and Predictability of Eastern Pacific Tropical Cyclone Activity* - Caron, Louis-Philippe; Boudreault, Mathieu; Camargo, Suzana J. (Journal of Climate, 2015)
Abstract Variability in tropical cyclone activity in the eastern Pacific basin has been linked to a wide range of climate factors, yet the dominant factors driving this variability have yet to be identified. Using Poisson regressions and a track clustering method, the authors analyze and compare the climate influence on cyclone activity in this region. The authors show that local sea surface temperature and upper-ocean heat content as well as large-scale conditions in the northern Atlantic are the dominant influence in modulating eastern North Pacific tropical cyclone activity. The results also support previous findings suggesting that the influence of the Atlantic Ocean occurs through changes in dynamical conditions over the eastern Pacific. Using model selection algorithms, the authors then proceed to construct a statistical model of eastern Pacific tropical cyclone activity. The various model selection techniques used agree in selecting one predictor from the Atlantic (northern North Atlantic sea surface temperature) and one predictor from the Pacific (relative sea surface temperature) to represent the best possible model. Finally, we show that this simple model could have predicted the anomalously high level of activity observed in 2014.

Estimation of correlations in portfolio credit risk models based on noisy security prices - Boudreault, Mathieu; Gauthier, Geneviève; Thomassin, Tommy (Journal of Economic Dynamics and Control, 2015)

Mitigating Interest Rate Risk in Variable Annuities: An Analysis of Hedging Effectiveness under Model Risk - Augustyniak, Maciej; Boudreault, Mathieu (North American Actuarial Journal, 2017)

Reanalysis of climate influences on Atlantic tropical cyclone activity using cluster analysis - Boudreault, Mathieu; Caron, Louis‐Philippe; Camargo, Suzana J. (Journal of Geophysical Research: Atmospheres, 2017)
Abstract We analyze, using Poisson regressions, the main climate influences on North Atlantic tropical cyclone activity. The analysis is performed using not only various time series of basin‐wide storm counts but also various series of regional clusters, taking into account shortcomings of the hurricane database through estimates of missing storms. The analysis confirms that tropical cyclones forming in different regions of the Atlantic are susceptible to different climate influences. We also investigate the presence of trends in these various time series, both at the basin‐wide and cluster levels, and show that, even after accounting for possible missing storms, there remains an upward trend in the eastern part of the basin and a downward trend in the western part. Using model selection algorithms, we show that the best model of Atlantic tropical cyclone activity for the recent past is constructed using Atlantic sea surface temperature and upper tropospheric temperature, while for the 1878–2015 period, the chosen covariates are Atlantic sea surface temperature and El Niño–Southern Oscillation. We also note that the presence of these artificial trends can impact the selection of the best covariates. If the underlying series shows an upward trend, then the mean Atlantic sea surface temperature captures both interannual variability and the upward trend, artificial or not. The relative sea surface temperature is chosen instead for stationary counts. Finally, we show that the predictive capability of the statistical models investigated is low for U.S. landfalling hurricanes but can be considerably improved when forecasting combinations of clusters whose hurricanes are most likely to make landfall. , Key Points Estimates of missing storms are not sufficient to account for the increase in hurricane activity in the eastern tropical Atlantic Recent upward trends, artificial or not, affect the selection of key determinants of tropical cyclone activity, especially the SST variable Despite previous results to that effect, the May–June NAO does not provide predictive skill for Atlantic landfalling hurricanes

Firm-specific credit risk estimation in the presence of regimes and noisy prices - Bégin, Jean-François; Boudreault, Mathieu; Gauthier, Geneviève (Finance Research Letters, 2017)

Maximum Likelihood Estimation of the Markov-Switching GARCH Model Based on a General Collapsing Procedure - Augustyniak, Maciej; Boudreault, Mathieu; Morales, Manuel (Methodology and Computing in Applied Probability, 2018)

Impact of reanalysis boundary conditions on downscaled Atlantic hurricane activity - Baudouin, Jean-Philippe; Caron, Louis-Philippe; Boudreault, Mathieu (Climate Dynamics, 2019)

Maximum likelihood estimation of first-passage structural credit risk models correcting for the survivorship bias - Amaya, Diego; Boudreault, Mathieu; McLeish, Don L. (Journal of Economic Dynamics and Control, 2019)

Credit and Systemic Risks in the Financial Services Sector: Evidence From the 2008 Global Crisis - Bégin, Jean‐François; Boudreault, Mathieu; Doljanu, Delia Alexandra; Gauthier, Geneviève (Journal of Risk and Insurance, 2019)
Abstract We develop a portfolio credit risk model that includes firm‐specific Markov‐switching regimes as well as individual stochastic and endogenous recovery rates. Using weekly credit default swap premiums for 35 financial firms, we analyze the credit risk of each of these companies and their statistical linkages, putting emphasis on the 2005–2012 period. Moreover, we study the systemic risk affecting both the banking and insurance subsectors.

Pricing Flood Insurance with a Hierarchical Physics-Based Model - Boudreault, Mathieu; Grenier, Patrick; Pigeon, Mathieu; Potvin, Jean-Mathieu; Turcotte, Richard (North American Actuarial Journal, 2020)

Limite à vie sur les inondations successives : vers un nouveau pacte social ? - Boudreault, Mathieu; Bourdeau-Brien, Michaël (Assurances et gestion des risques, 2020)
Après les nombreuses crues printanières qui ont affecté le sud du Québec depuis 2011, le gouvernement du Québec a annoncé en avril 2019 une refonte importante de son programme d’aide financière aux sinistrés. Le programme introduit désormais une couverture limitée à vie de 100 000 $ pour les inondations successives, une mesure unique au Canada. L’objectif de cet article est d’analyser le coût des inondations successives et les impacts financiers de cette limite de couverture pour les ménages.

Likelihood Evaluation of Jump-Diffusion Models Using Deterministic Nonlinear Filters - Bégin, Jean-François; Boudreault, Mathieu (Journal of Computational and Graphical Statistics, 2021)

A Global Flood Risk Modeling Framework Built With Climate Models and Machine Learning - Carozza, David A.; Boudreault, Mathieu (Journal of Advances in Modeling Earth Systems, 2021)
Abstract Large scale flood risk analyses are fundamental to many applications requiring national or international overviews of flood risk. While large‐scale climate patterns such as teleconnections and climate change become important at this scale, it remains a challenge to represent the local hydrological cycle over various watersheds in a manner that is physically consistent with climate. As a result, global models tend to suffer from a lack of available scenarios and flexibility that are key for planners, relief organizations, regulators, and the financial services industry to analyze the socioeconomic, demographic, and climatic factors affecting exposure. Here we introduce a data‐driven, global, fast, flexible, and climate‐consistent flood risk modeling framework for applications that do not necessarily require high‐resolution flood mapping. We use statistical and machine learning methods to examine the relationship between historical flood occurrence and impact from the Dartmouth Flood Observatory (1985–2017), and climatic, watershed, and socioeconomic factors for 4,734 HydroSHEDS watersheds globally. Using bias‐corrected output from the NCAR CESM Large Ensemble (1980–2020), and the fitted statistical relationships, we simulate 1 million years of events worldwide along with the population displaced in each event. We discuss potential applications of the model and present global flood hazard and risk maps. The main value of this global flood model lies in its ability to quickly simulate realistic flood events at a resolution that is useful for large‐scale socioeconomic and financial planning, yet we expect it to be useful to climate and natural hazard scientists who are interested in socioeconomic impacts of climate. , Plain Language Summary Flood is among the deadliest and most damaging natural disasters. To protect against large scale flood risk, stakeholders need to understand how floods can occur and their potential impacts. Stakeholders rely on global flood models to provide them with plausible flood scenarios around the world. For a flood model to operate at the global scale, climate effects must be represented in addition to hydrological ones to demonstrate how rivers can overflow throughout the world each year. Global flood models often lack the flexibility and variety of scenarios required by many stakeholders because they are computationally demanding. Designed for applications where detailed local flood impacts are not required, we introduce a rapid and flexible global flood model that can generate hundreds of thousands of scenarios everywhere in the world in a matter of minutes. The model is based on a historical flood database from 1985 to 2017 that is represented using an algorithm that learns from the data. With this model, the output from a global climate model is used to simulate a large sample of floods for risk analyses that are coherent with global climate. Maps of the annual average number of floods and number of displaced people illustrate the models results. , Key Points We present a global flood model built using machine learning methods fitted with historical flood occurrences and impacts Forced with a climate model, the global flood model is fast, flexible and consistent with global climate We provide global flood hazard (occurrence) and risk (population displaced) maps over 4,734 watersheds

Gridded flood depth estimates from satellite-derived inundations - Bryant, Seth; McGrath, Heather; Boudreault, Mathieu (Natural Hazards and Earth System Sciences, 2022)
Abstract. Canada's RADARSAT missions improve the potential to study past flood events; however, existing tools to derive flood depths from this remote-sensing data do not correct for errors, leading to poor estimates. To provide more accurate gridded depth estimates of historical flooding, a new tool is proposed that integrates Height Above Nearest Drainage and Cost Allocation algorithms. This tool is tested against two trusted, hydraulically derived, gridded depths of recent floods in Canada. This validation shows the proposed tool outperforms existing tools and can provide more accurate estimates from minimal data without the need for complex physics-based models or expert judgement. With improvements in remote-sensing data, the tool proposed here can provide flood researchers and emergency managers accurate depths in near-real time.

Ratemaking territories and adverse selection for flood insurance - Boudreault, Mathieu; Ojeda, Angelica (Insurance: Mathematics and Economics, 2022)

On the Measurement of Hedging Effectiveness for Long-Term Investment Guarantees - Augustyniak, Maciej; Badescu, Alexandru; Boudreault, Mathieu (Journal of Risk and Financial Management, 2023)
Although the finance literature has devoted a lot of research into the development of advanced models for improving the pricing and hedging performance, there has been much less emphasis on approaches to measure dynamic hedging effectiveness. This article discusses a statistical framework based on regression analysis to measure the effectiveness of dynamic hedges for long-term investment guarantees. The importance of taking model risk into account is emphasized. The difficulties in reducing hedging risk to an appropriately low level lead us to propose a new perspective on hedging, and recognize it as a tool to modify the risk–reward relationship of the unhedged position.

Global Asymmetries in the Influence of ENSO on Flood Risk Based on 1,600 Years of Hybrid Simulations - Del Rio Amador, L.; Boudreault, M.; Carozza, D. A. (Geophysical Research Letters, 2023)
Abstract El Niño‐Southern Oscillation (ENSO) is often considered as a source of long‐term predictability for extreme events via its teleconnection patterns. However, given that its characteristic cycle varies from two to 7 years, it is difficult to obtain statistically significant conclusions based on observational periods spanning only a few decades. To overcome this, we apply the global flood risk modeling framework developed by Carozza and Boudreault to an equivalent of 1,600 years of bias‐corrected General Circulation Model outputs. The results show substantial anomalies in flood occurrences and impacts for El Niño and La Niña when compared to the all‐year baseline. We were able to obtain a larger global coverage of statistically significant results than previous studies limited to observational data. Asymmetries in anomalies for both ENSO phases show a larger global influence of El Niño than La Niña on flood hazard and risk. , Plain Language Summary El Niño‐Southern Oscillation (ENSO) is one of the most important global climate phenomena. It is well‐known to affect precipitation and temperature in many areas of the world. It is therefore very important for researchers (environmental and climate sciences, economics, etc.), risk managers, decision‐ and policy‐makers to understand the influence of ENSO on flooding. Previous studies analyzed the link between ENSO and flooding but because they were based upon 40 years of data, a lot of uncertainties remained as to how ENSO has any significance on flooding. In this study, we use outputs from a climate model large ensemble that provides 1,600 years of simulated data to determine the impacts of ENSO on flooding. But because it is very difficult to run traditional flood models on 1,600 years of data, we rather leverage a machine learning approach to accelerate computations in a context where the focus is on socioeconomic impacts. We find that ENSO is a significant driver of flooding in more regions than what was previously found. Finally, there appears to be a greater global influence of El Niño than La Niña on flooding. , Key Points We simulated an equivalent of 1,600 years of realistic flood events globally using a statistical model forced with climate model outputs We found a statistically significant ( α = 0.05) influence of El Niño‐Southern Oscillation (ENSO) over 55% of land area for flood occurrence and over 69% for flood impact Asymmetries in anomalies for both ENSO phases show a larger global influence of El Niño than La Niña on flood hazard and risk

Comparison of three flood-related relocation programs with probabilistic cost-benefit analyses - Boudreault, Mathieu; Bourdeau-Brien, Michael; Milot, Nicolas (International Journal of Disaster Risk Reduction, 2023)

Leveraging prices from credit and equity option markets for portfolio risk management - Bégin, Jean‐François; Boudreault, Mathieu; Thériault, Mathieu (Journal of Futures Markets, 2024)
Abstract This study presents a firm‐specific methodology for extracting implied default intensities and recovery rates jointly from unit recovery claim prices—backed by out‐of‐the‐money put options—and credit default swap premiums, therefore providing time‐varying and market‐consistent views of credit risk at the individual level. We apply the procedure to about 400 firms spanning different sectors of the US economy between 2003 and 2019. The main determinants of default intensities and recovery rates are analyzed with statistical and machine learning methods linking default risk and credit losses to market, sector, and individual variables. Consistent with the literature, we find that individual volatility, leverage, and corporate bond market determinants are key factors explaining the implied default intensities and recovery rates. Then, we apply the framework in the context of credit risk management in applications, like, market‐consistent credit value‐at‐risk calculation and stress testing.

UQAM‐TCW: A Global Hybrid Tropical Cyclone Wind Model Based Upon Statistical and Coupled Climate Models - Carozza, David A.; Boudreault, Mathieu; Grenier, Manuel; Caron, Louis‐Philippe (Journal of Advances in Modeling Earth Systems, 2024)
Abstract Tropical cyclones (TCs) are among the most destructive natural hazards and yet, quantifying their financial impacts remains a significant methodological challenge. It is therefore of high societal value to synthetically simulate TC tracks and winds to assess potential impacts along with their probability distributions for example, land use planning and financial risk management. A common approach to generate TC tracks is to apply storm detection methodologies to climate model output, but such an approach is sensitive to the method and parameterization used and tends to underestimate intense TCs. We present a global TC model (the UQAM‐TCW model thereafter) that melds statistical modeling, to capture historical risk features, with a climate model large ensemble, to generate large samples of physically coherent TC seasons. Integrating statistical and physical methods, the model is probabilistic and consistent with the physics of how TCs develop. The model includes frequency and location of cyclogenesis, full trajectories with maximum sustained winds and the entire wind structure along each track for the six typical cyclogenesis basins from IBTrACS. Being an important driver of TCs globally, we also integrate ENSO effects in key components of the model. The global TC model thus belongs to a recent strand of literature that combines probabilistic and physical approaches to TC track generation. As an application of the model, we show global hazard maps for direct and indirect hits expressed in terms of return periods. The global TC model can be of interest to climate and environmental scientists, economists and financial risk managers. , Plain Language Summary Tropical cyclones (TCs) are among the most destructive natural hazards and yet, quantifying their financial impacts remains a difficult task. Being able to randomly simulate TCs and their features (such as wind speed) with mathematical models is therefore critical to build scenarios (and their corresponding probability) for land use planning and financial risk management. A common approach is to simulate TCs by tracking them directly in climate model outputs but this often underestimates the frequency of intense TCs while being computationally costly overall to generate a large number of events. For these reasons, many authors have looked into alternative approaches that replicate key physical features of TCs but rather using statistical models that are much less computationally demanding. This paper therefore presents a global TC model that leverages the strengths of both statistical and climate models to simulate a large number of TCs whose features are consistent with the physics and observations. As an important global phenomenon that affects TCs globally, we also integrate in our model the effects of El Niño. The paper focuses on the methodology and validation of each model component and concludes with global hazard maps for direct and indirect hits. , Key Points We present a global tropical cyclone (TC) wind model built upon a climate model large ensemble that can be used for risk analysis We integrate ENSO into our model since it is a strong driver of storm annual frequency, cyclogenesis, trajectories, and intensity We present global hazard maps consistent with statistical features of TC components and coherent with a global climate model

A Simulation and Empirical Study of the Maximum Likelihood Estimator for Stochastic Volatility Jump-Diffusion Models - Bégin, Jean-François; Boudreault, Mathieu (Studies in Nonlinear Dynamics & Econometrics, 2024)
Abstract We investigate the behaviour of the maximum likelihood estimator (MLE) for stochastic volatility jump-diffusion models commonly used in financial risk management. A simulation study shows the practical conditions under which the MLE behaves according to theory. In an extensive empirical study based on nine indices and more than 6000 individual stocks, we nonetheless find that the MLE is unable to replicate key higher moments. We then introduce a moment-targeted MLE – robust to model misspecification – and revisit both simulation and empirical studies. We find it performs better than the MLE, improving the management of financial risk.

Projected seasonal flooding in Canada under climate change with statistical and machine learning - Grenier, Manuel; Boudreault, Jérémie; Raymond, Sébastien; Boudreault, Mathieu (Journal of Hydrology: Regional Studies, 2024)

A data science approach to climate change risk assessment applied to pluvial flood occurrences for the United States and Canada - Bourget, Mathilde; Boudreault, Mathieu; Carozza, David A.; Boudreault, Jérémie; Raymond, Sébastien (ASTIN Bulletin, 2024)
Abstract There is mounting pressure on (re)insurers to quantify the impacts of climate change, notably on the frequency and severity of claims due to weather events such as flooding. This is however a very challenging task for (re)insurers as it requires modeling at the scale of a portfolio and at a high enough spatial resolution to incorporate local climate change effects. In this paper, we introduce a data science approach to climate change risk assessment of pluvial flooding for insurance portfolios over Canada and the United States (US). The underlying flood occurrence model quantifies the financial impacts of short-term (12–48 h) precipitation dynamics over the present (2010–2030) and future climate (2040–2060) by leveraging statistical/machine learning and regional climate models. The flood occurrence model is designed for applications that do not require street-level precision as is often the case for scenario and trend analyses. It is applied at the full scale of Canada and the US over 10–25 km grids. Our analyses show that climate change and urbanization will typically increase losses over Canada and the US, while impacts are strongly heterogeneous from one state or province to another, or even within a territory. Portfolio applications highlight the importance for a (re)insurer to differentiate between future changes in hazard and exposure, as the latter may magnify or attenuate the impacts of climate change on losses.

Flood occurrence and impact models for socioeconomic applications over Canada and the United States - Grenier, Manuel; Boudreault, Mathieu; Carozza, David A.; Boudreault, Jérémie; Raymond, Sébastien (Natural Hazards and Earth System Sciences, 2024)
Abstract. Large-scale socioeconomic studies of the impacts of floods are difficult and costly for countries such as Canada and the United States due to the large number of rivers and size of watersheds. Such studies are however very important for analyzing spatial patterns and temporal trends to inform large-scale flood risk management decisions and policies. In this paper, we present different flood occurrence and impact models based upon statistical and machine learning methods of over 31 000 watersheds spread across Canada and the US. The models can be quickly calibrated and thereby easily run predictions over thousands of scenarios in a matter of minutes. As applications of the models, we present the geographical distribution of the modelled average annual number of people displaced due to flooding in Canada and the US, as well as various scenario analyses. We find for example that an increase of 10 % in average precipitation yields an increase in the displaced population of 18 % in Canada and 14 % in the US. The model can therefore be used by a broad range of end users ranging from climate scientists to economists who seek to translate climate and socioeconomic scenarios into flood probabilities and impacts measured in terms of the displaced population.

A Global Multi-Source Tropical Cyclone Precipitation (MSTCP) Dataset - Morin, Gabriel; Boudreault, Mathieu; García-Franco, Jorge L. (Scientific Data, 2024)
Abstract A global tropical cyclone precipitation dataset covering the period from January 1979 to February 2023 is presented. Global precipitation estimates were taken from the newly developed high-resolution Multi-Source Weighted-Ensemble Precipitation, version 2 (MSWEP V2) and TC tracks were obtained from the International Best Track Archive for Climate Stewardship (IBTrACS) dataset. This Global Multi-Source Tropical Cyclone Precipitation (MSTCP) dataset is comprised of two main products and files in the format of tables: the main and profile datasets. The main file provides various TCP statistics per TC track, including mean and maximum precipitation rates over a fixed and symmetrical radius of 500 km. The profile dataset comprises the azimuthally averaged precipitation every 10-km away from the center of each storm (until 500 km). The case study of Hurricane Harvey is used to show that MSWEP estimates agree well with another commonly used satellite product. The main statistics of the dataset are analyzed as well, including the differences in the dataset metrics for each of the six TC basins and for each Saffir-Simpson category for storm intensity.

Contributing Factors to Residential Flood Damage in Canada - Deschamps, Bernard; Boudreault, Mathieu; Gachon, Philippe (2023)

Do Jumps Matter in the Long Term? A Tale of Two Horizons - Bégin, Jean-François; Boudreault, Mathieu (North American Actuarial Journal, 2022)