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Impact analysis of financial regulation on multi-asset markets using artificial market simulations

Hirano, Masanori; Izumi, Kiyoshi; Shimada, Takashi; … - In: Journal of risk and financial management : JRFM 13 (2020) 4/75, pp. 1-20

of an artificial market, one of the computer simulations imitating real financial markets. In the simulation, we proposed …

Persistent link: https://www.econbiz.de/10012302643

Computational finance

Stentoft, Lars - In: Journal of risk and financial management : JRFM 13 (2020) 7/145, pp. 1-4

The field of computational finance is evolving ever faster. This book collects a number of novel contributions on the use of computational methods and techniques for modelling financial asset prices, returns, and volatility, and on the use of numerical methods for pricing, hedging, and risk...

Persistent link: https://www.econbiz.de/10012309311

Volatility-adjusted 60/40 versus 100 : new risk investing paradigm

Liew, Jim Kyung-Soo; Ajakh, Ahmad - In: Journal of risk and financial management : JRFM 13 (2020) 9/190, pp. 1-6

In this study we examine the volatility-adjusted 60/40 rule at the individual company level. We document that strong diversification benefits exist over the long-term, and that both the equity and corporate bonds exhibit positive expected drifts. For our sample of 30 large-cap companies, given...

Persistent link: https://www.econbiz.de/10012386869

Diversification and fund performance : an analysis of buyout funds

Huss, Matthias; Steger, Daniel - In: Journal of risk and financial management : JRFM 13 (2020) 6/136, pp. 1-16

This paper studies the relationship between portfolio diversification and fund performance, based on an unexplored … performance. We do not find a significant relationship between geographical diversification and performance. These results partly …

Persistent link: https://www.econbiz.de/10012309185

Simulating multi-asset classes prices using Wasserstein Generative Adversarial Network : a study of stocks, futures and cryptocurrency

Han, Feng; Ma, Xiaojuan; Zhang, Jiheng - In: Journal of risk and financial management : JRFM 15 (2022) 1, pp. 1-21

Financial data are expensive and highly sensitive with limited access. We aim to generate abundant datasets given the original prices while preserving the original statistical features. We introduce the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) into the field of...

Persistent link: https://www.econbiz.de/10012813868

Forecasting stochastic volatility characteristics for the financial fossil oil market densities

Solibakke, Per Bjarte - In: Journal of risk and financial management : JRFM 14 (2021) 11, pp. 1-17

This paper builds and implements multifactor stochastic volatility models for the international oil/energy markets (Brent oil and WTI oil) for the period 2011-2021. The main objective is to make step ahead volatility predictions for the front month contracts followed by an implication discussion...

Persistent link: https://www.econbiz.de/10012794710

Stochastic optimization system for bank reverse stress testing

Montesi, Giuseppe; Papiro, Giovanni; Fazzini, Massimiliano - In: Journal of risk and financial management : JRFM 13 (2020) 8/174, pp. 1-43

propose a reverse stress test methodology based on a stochastic simulation optimization system. This methodology enables users …

Persistent link: https://www.econbiz.de/10012322078

Neural network predictive modeling on dynamic portfolio management : a simulation-based portfolio optimization approach

Yu, Jiayang; Chang, Kuo-Chu - In: Journal of risk and financial management : JRFM 13 (2020) 11/285, pp. 1-23

into their investment strategies and optimizing their portfolio performance in a timely manner. In this paper, we present a … simulation-based approach by fusing a number of macroeconomic factors using Neural Networks (NN) to build an Economic Factor …-based Predictive Model (EFPM). Then, we combine it with the Copula-GARCH simulation model and the Mean-Conditional Value at Risk (Mean …

Persistent link: https://www.econbiz.de/10012388728

Modeling of bank credit risk management using the cost risk model

Yanenkova, Iryna; Nehoda, Yuliia; Drobyazko, Svetlana; … - In: Journal of risk and financial management : JRFM 14 (2021) 5, pp. 1-15

This article deals with the issue of managing bank credit risk using a cost risk model. Modeling of bank credit risk management was proposed based on neural-cell technologies, which expand the possibilities of modeling complex objects and processes and provide high reliability of credit risk...

Persistent link: https://www.econbiz.de/10012534575