Securing Lifelong Retirement Income: Global Annuity Markets and Policy
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Notice bibliographique
Résumé
Interest in longevity and longevity risk management is burgeoning, as government and regulatory agencies are increasingly conscious of the potential risks and benefits of longer lifespans. Commercial and industrial organizations, especially within the financial sector, are awakening to the opportunities presented by population aging, along with the new array of financial insurance instruments to manage longevity risk, which more sophisticated markets are making possible. This volume explores three main themes: the need for products to manage longevity risk; the structure and safety of financial products on the market that help manage longevity risk; and the role of policy in stimulating and strengthening longevity insurance products. This edited volume brings together leading international experts to evaluate the challenge posed by trends in longevity risk and draws out the implications and constraints of this new reality for insurance companies and annuity providers. It discusses both emerging economies (India, Chile) and many of the older nations (Sweden, Canada, the US, Australia, Japan, the UK and Switzerland). It aims to instigate new thinking among retirement planners, plan sponsors, academics, and industry leaders seeking to manage retirement payouts and longevity risk. Contributors to this volume - Mukul G. Asher, Professor of Public Policy, the Lee Kuan Yew School of Public Policy, the National University of Singapore Hazel Bateman, Associate Professor of Economics and the Director of the Centre for Pensions and Superannuation, the University of New South Wales, Sydney, Australia Monika Butler, Professor of Economics and Public Policy, St. Gallen University, Switzerland and Managing Director, the Swiss Institute for Empirical Economic Research SEW-HSG Edmund Cannon, Professor of Economics, Finance, and Management. the University of Bristol Barbara Kaschutzke, Researcher, the Finance Department, the Goethe University Frankfurt, and Chair of Investment, Portfolio Management, and Pension Finance Bo Larsson, Analyst, the Swedish Pensions Agency and Assistant Professor, Dalarna University College Raimond Maurer, Endowed Chair of Investment, Portfolio Management, and Pension Finance, the Finance Department, the Goethe University of Frankfurt Moshe A. Milevsky, Professor of Finance, the Schulich School of Business, York University, Toronto, and Executive Director, The IFID Centre Olivia S. Mitchell, Executive Director of the Pension Research Council, Wharton School, University of Pennsylvania Edward Palmer, Professor of Social Insurance Economics, Uppsala University, Sweden, and Senior Advisor to the Swedish Social Insurance Agency John Piggott, Professor of Economics in the Australian School of Business, University of New South Wales Jose Ruiz, Professor of Finance, the University of Chile Business School Junichi Sakamoto, Chief Adviser to the Pension Management Research Group of the Nomura Research Institute and Lecturer at the University of Tokyo, Nihon University, and Sophia University Stefan Staubli, Research Associate, the Swiss Institute for Empirical Economic Research, the University of St. Gallen Noriyuki Takayama, Professor at the Institute of Economic Research, the Hitotsubashi University, Tokyo Ian Tonks, Professor of Finance, the School of Management, the University of Bath Deepa Vasudevan, Researcher Anthony Webb, Associate Director of Research, the Center for Retirement Research, Boston College Ling-wu Shao, doctoral student in Finance, the Schulich School of Business, York University
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle