Printed from https://ideas.repec.org/n/nep-rmg/2022-06-20.html
Report NEP-RMG-2022-06-20
This is the archive for NEP-RMG, a report on new working papers in the area of Risk Management. Stanley Miles issued this report. It is usually issued weekly.Subscribe to this report: email, RSS, or Mastodon, or Bluesky.
Other reports in NEP-RMG
The following items were announced in this report:
- Jay Cao & Jacky Chen & Soroush Farghadani & John Hull & Zissis Poulos & Zeyu Wang & Jun Yuan, 2022, "Gamma and Vega Hedging Using Deep Distributional Reinforcement Learning," Papers, arXiv.org, number 2205.05614, May, revised Jan 2023.
- Udai Nagpal & Krishan Nagpal, 2022, "Cluster-based Regression using Variational Inference and Applications in Financial Forecasting," Papers, arXiv.org, number 2205.00605, May, revised Dec 2023.
- Imran Yousaf & Vasilios Plakandaras & Elie Bouri & Rangan Gupta, 2022, "Hedge and Safe Haven Properties of Gold, US Treasury, Bitcoin, and Dollar/CHF against the FAANA Companies and S&P 500," Working Papers, University of Pretoria, Department of Economics, number 202227, May.
- Loïc Cantin & Christian Francq & Jean-Michel Zakoïan, 2022, "Estimating dynamic systemic risk measures," Working Papers, Center for Research in Economics and Statistics, number 2022-11, Jan.
- Taras Bodnar & Vilhelm Niklasson & Erik Thors'en, 2022, "Volatility Sensitive Bayesian Estimation of Portfolio VaR and CVaR," Papers, arXiv.org, number 2205.01444, May.
- Dixon Domfeh & Arpita Chatterjee & Matthew Dixon, 2022, "A Unified Bayesian Framework for Pricing Catastrophe Bond Derivatives," Papers, arXiv.org, number 2205.04520, May.
- Fredy Gamboa-Estrada & José Vicente Romero, 2022, "Modelling CDS Volatility at Different Tenures: An Application for Latin-American Countries," Borradores de Economia, Banco de la Republica de Colombia, number 1199, May, DOI: 10.32468/be.1199.
- Margherita Doria & Elisa Luciano & Patrizia Semeraro, 2022, "Machine learning techniques in joint default assessment," Papers, arXiv.org, number 2205.01524, May, revised Sep 2023.
- Item repec:fip:a00001:94154 is not listed on IDEAS anymore
- Mikhail Pomazanov, 2022, "Method of indirect estimation of default probability dynamics for industry-target segments according to the data of Bank of Russia," Papers, arXiv.org, number 2205.05984, May.
- Müller, Carola, 2022, "Capital requirements, market structure, and heterogeneous banks," IWH Discussion Papers, Halle Institute for Economic Research (IWH), number 15/2022.
- Olkhov, Victor, 2022, "The Market-Based Asset Price Probability," MPRA Paper, University Library of Munich, Germany, number 113096, May.
- Jean-Philippe Bouchaud & Iacopo Mastromatteo & Marc Potters & Konstantin Tikhonov, 2022, "Excess Out-of-Sample Risk and Fleeting Modes," Papers, arXiv.org, number 2205.01012, May.
- Georges Dionne & Denise Desjardins, 2022, "A re-examination of the U.S. insurance market’s capacity to pay catastrophe losses," Working Papers, HEC Montreal, Canada Research Chair in Risk Management, number 22-2, May.
- Claudiu Vinte & Marcel Ausloos & Titus Felix Furtuna, 2022, "A Volatility Estimator of Stock Market Indices Based on the Intrinsic Entropy Model," Papers, arXiv.org, number 2205.01370, May.
- Pierlauro Lopez & J. David López-Salido & Francisco Vazquez-Grande, 2022, "Accounting for Risk in a Linearized Solution: How to Approximate the Risky Steady State and Around It," Working Papers, Federal Reserve Bank of Cleveland, number 22-14, May, DOI: 10.26509/frbc-wp-202214.
- Takashi Hayashi & Ryoko Wada, 2022, "Comparative risk and ambiguity aversion: an experimental approach," KIER Working Papers, Kyoto University, Institute of Economic Research, number 1079, May.
- Jiling Cao & Jeong-Hoon Kim & Xi Li & Wenjun Zhang, 2022, "Pricing Path-dependent Options under Stochastic Volatility via Mellin Transform," Papers, arXiv.org, number 2205.00573, May.
- Cyril Bachelard & Apostolos Chalkis & Vissarion Fisikopoulos & Elias Tsigaridas, 2022, "Randomized geometric tools for anomaly detection in stock markets," Papers, arXiv.org, number 2205.03852, May, revised May 2022.
- Ryan Defina, 2021, "Machine Learning Methods: Potential for Deposit Insurance," IADI Fintech Briefs, International Association of Deposit Insurers, number 3, Sep.