Similar Search Results

Forecasting of realised volatility with the random forests algorithm

Luong, Chuong; Dokučaev, Nikolaj G. - In: Journal of Risk and Financial Management 11 (2018) 4, pp. 1-15

The paper addresses the forecasting of realised volatility for financial time series using the heterogeneous autoregressive model (HAR) and machine learning techniques. We consider an extended version of the existing HAR model with included purified implied volatility. For this extended model,...

Persistent link: https://www.econbiz.de/10012611068

Spurious relationships for nearly non-stationary series

Cheng, Yushan; Hui, Yongchang; McAleer, Michael; Wong, … - In: Journal of Risk and Financial Management 14 (2021) 8, pp. 1-24

Literature shows that the regression of independent and (nearly) nonstationary time series could result in spurious outcomes. In this paper, we conjecture that under some situations, the regression of two independent and nearly non-stationary series does not have any spurious problem at all. To...

Persistent link: https://www.econbiz.de/10013201050

Sample path generation of the stochastic volatility CGMY process and its application to path-dependent option pricing

Kim, Young Shin - In: Journal of Risk and Financial Management 14 (2021) 2, pp. 1-18

This paper proposes the sample path generation method for the stochastic volatility version of the CGMY process. We present the Monte-Carlo method for European and American option pricing with the sample path generation and calibrate model parameters to the American style S&P 100 index options...

Persistent link: https://www.econbiz.de/10012611634

Super RaSE: Super random subspace ensemble classification

Zhu, Jianan; Yang, Feng - In: Journal of Risk and Financial Management 14 (2021) 12, pp. 1-18

We propose a new ensemble classification algorithm, named super random subspace ensemble (Super RaSE), to tackle the … sparse classification problem. The proposed algorithm is motivated by the random subspace ensemble algorithm (RaSE). The RaSE … method was shown to be a flexible framework that can be coupled with any existing base classification. However, the success …

Persistent link: https://www.econbiz.de/10013201295

A comprehensive review of corporate bankruptcy prediction in Hungary

Kristóf, Tamás; Virág, Miklós - In: Journal of Risk and Financial Management 13 (2020) 2, pp. 1-20

classification method(s) to accomplish efficient corporate bankruptcy prediction. …

Persistent link: https://www.econbiz.de/10012611249

Machine learning applications to land and structure valuation

Mayer, Michael; Bourassa, Steven C.; Hoesli, Martin; … - In: Journal of Risk and Financial Management 15 (2022) 5, pp. 1-24

In some applications of supervised machine learning, it is desirable to trade model complexity with greater interpretability for some covariates while letting other covariates remain a "black box". An important example is hedonic property valuation modeling, where machine learning techniques...

Persistent link: https://www.econbiz.de/10014332394

Deep partial hedging

Hou, Songyan; Krabichler, Thomas; Wunsch, Marcus - In: Journal of Risk and Financial Management 15 (2022) 5, pp. 1-5

Using techniques from deep learning, we show that neural networks can be trained successfully to replicate the modified payoff functions that were first derived in the context of partial hedging by Föllmer and Leukert. Not only does this approach better accommodate the realistic setting of...

Persistent link: https://www.econbiz.de/10014332424

A machine learning framework towards bank telemarketing prediction

Koumétio Tékouabou, Stéphane Cédric; Gherghina, … - In: Journal of Risk and Financial Management 15 (2022) 6, pp. 1-19

The use of machine learning (ML) methods has been widely discussed for over a decade. The search for the optimal model is still a challenge that researchers seek to address. Despite advances in current work that surpass the limitations of previous ones, research still faces new challenges in...

Persistent link: https://www.econbiz.de/10014332470

HF-SCA: Hands-free strong customer authentication based on a memory-guided attention mechanisms

Distante, Cosimo; Fineo, Laura; Mainetti, Luca; Manco, Luigi - In: Journal of Risk and Financial Management 15 (2022) 8, pp. 1-24

Strong customer authentication (SCA) is a requirement of the European Union Revised Directive on Payment Services (PSD2) which ensures that electronic payments are performed with multifactor authentication. While increasing the security of electronic payments, the SCA impacted seriously on the...

Persistent link: https://www.econbiz.de/10014332543

Can ensemble machine learning methods predict stock returns for Indian banks using technical indicators?

Mohapatra, Sabyasachi; Mukherjee, Rohan; Roy, Arindam; … - In: Journal of Risk and Financial Management 15 (2022) 8, pp. 1-16

This paper develops ensemble machine learning models (XGBoost, Gradient Boosting, and AdaBoost in addition to Random Forest) for predicting stock returns of Indian banks using technical indicators. These indicators are based on three broad categories of technical analysis: Price, Volume, and...

Persistent link: https://www.econbiz.de/10014332551