Computational Economics最新文献_第2页

Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation 利用基于因子的情景树生成技术进行多阶段国际投资组合选择

IF 2 4区经济学

Computational Economics Pub Date : 2024-08-15 DOI: 10.1007/s10614-024-10699-x

Zhiping Chen, Bingbing Ji, Jia Liu, Yu Mei

{"title":"Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation","authors":"Zhiping Chen, Bingbing Ji, Jia Liu, Yu Mei","doi":"10.1007/s10614-024-10699-x","DOIUrl":"https://doi.org/10.1007/s10614-024-10699-x","url":null,"abstract":"To comprehensively reflect the heteroscedasticity, nonlinear dependence and heavy-tailed distributions of stock returns while reducing the huge cost of parameter estimation, we use the Fama-French three-factor model to describe stock returns and then model the factor dynamics by using the ARMA-GARCH and Student-t copula models. A factor-based scenario tree generation algorithm is thus proposed, and the corresponding multi-stage international portfolio selection model is constructed and its reformulation is derived. Different from the current literature, our proposed models can capture the dynamic dependence among international markets and the dynamics of exchange rates, and what’s more important, make it possible for the practical solution of large-scale multi-stage international portfolio selection problems. Considering three different objective functions and international investments in the USA, Japanese and European markets, we carry out a series of empirical studies to demonstrate the practicality and efficiency of the proposed factor-based scenario tree generation algorithm and multi-stage international portfolio selection models.","PeriodicalId":50647,"journal":{"name":"Computational Economics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quantifying the Predictive Capacity of Dynamic Graph Measures on Systemic and Tail Risk 量化动态图表措施对系统风险和尾端风险的预测能力

IF 2 4区经济学

Computational Economics Pub Date : 2024-08-14 DOI: 10.1007/s10614-024-10692-4

George Tzagkarakis, Eleftheria Lydaki, Frantz Maurer

{"title":"Quantifying the Predictive Capacity of Dynamic Graph Measures on Systemic and Tail Risk","authors":"George Tzagkarakis, Eleftheria Lydaki, Frantz Maurer","doi":"10.1007/s10614-024-10692-4","DOIUrl":"https://doi.org/10.1007/s10614-024-10692-4","url":null,"abstract":"Understanding financial contagion and instability, especially during financial crises, is an important issue in risk management. The emergence of alternative high-risk and speculative asset classes such as cryptocurrencies, make it imperative to effectively monitor the financial connectivity between heterogeneous asset classes across time, in conjunction with the associated risk, to avoid a substantial breakdown of financial systems during turmoil periods. To address this problem, this paper investigates the predictive capacity of time-varying graph connectivity measures on tail and systemic risk for heterogeneous asset classes. To this end, proper statistical and geometric rules are defined first, to infer the dynamic graph topology of asset returns. Then, a novel predictive signal is proposed to quantify and rank the predictive power of dynamic nodal and global graph measures. Finally, a minimum dominating set detection method is used to track the community structure of our asset classes over time and study its consistency with the time evolution of the top predictive measures. Our empirical findings show a remarkable variability of the predictive potential for the distinct connectivity measures, and reveal its importance in designing alerting mechanisms for risk management.","PeriodicalId":50647,"journal":{"name":"Computational Economics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Household Financial Fragility, Debt and Income in a Dynamic Model 动态模型中的家庭财务脆弱性、债务和收入

IF 2 4区经济学

Computational Economics Pub Date : 2024-08-14 DOI: 10.1007/s10614-024-10698-y

Giorgio Calcagnini, Federico Favaretto, Germana Giombini, Fabio Tramontana

引用次数: 0

Machine Learning Methods and Time Series: A Through Forecasting Study via Simulation and USA Inflation Analysis 机器学习方法与时间序列：通过模拟和美国通胀分析进行预测研究

IF 2 4区经济学

Computational Economics Pub Date : 2024-08-14 DOI: 10.1007/s10614-024-10675-5

Klaus Boesch, Flavio A. Ziegelmann

{"title":"Machine Learning Methods and Time Series: A Through Forecasting Study via Simulation and USA Inflation Analysis","authors":"Klaus Boesch, Flavio A. Ziegelmann","doi":"10.1007/s10614-024-10675-5","DOIUrl":"https://doi.org/10.1007/s10614-024-10675-5","url":null,"abstract":"Modern problems in Economics have tremendously benefited from the ever increasing amount of available information. Hence, most of the recent econometric approaches have focused on how to model and estimate relationships between covariates and dependent variables under this high-dimensional scenario. Particularly in the time series context, one usually aims to produce valuable forecasts of the dependent variables. In this paper our main goal is two-folded: i) employ several modern computationally highly intensive Machine Learning (ML) methods for achieving time series forecasting accuracy under a high-dimensional covariates setting; ii) propose a novel variation of the Elastic Net (ENet), the Weighted Lag Adaptive ENet (WLadaENet), which combines the popular Ridge Regression with a regularization method tailored for time series, the WLAdaLASSO (Konzen and Ziegelmann in J Forecast 35:592–612, 2016). To achieve our goal, we carry out Monte Carlo simulation studies as well as a real data analysis of USA inflation with a forecast range from January 2013 to December 2023. In our Monte Carlo implementations, the WLadaENet presents a solid performance both in terms of variable selection when the true model is sparse and in terms of forecasting accuracy even when the model is not sparse and nonlinearities are included. Our approach also performs reasonably well to forecast the USA inflation for different horizons ahead. Since the chosen period includes the Covid-19 crisis, a sub-period analysis is carried out, not leading to a uniformly best forecaster.","PeriodicalId":50647,"journal":{"name":"Computational Economics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142223693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reconciling Tracking Error Volatility and Value-at-Risk in Active Portfolio Management: A New Frontier 主动投资组合管理中跟踪误差波动与风险价值的协调：新领域

IF 2 4区经济学

Computational Economics Pub Date : 2024-08-14 DOI: 10.1007/s10614-024-10684-4

Riccardo Lucchetti, Mihaela Nicolau, Giulio Palomba, Luca Riccetti

{"title":"Reconciling Tracking Error Volatility and Value-at-Risk in Active Portfolio Management: A New Frontier","authors":"Riccardo Lucchetti, Mihaela Nicolau, Giulio Palomba, Luca Riccetti","doi":"10.1007/s10614-024-10684-4","DOIUrl":"https://doi.org/10.1007/s10614-024-10684-4","url":null,"abstract":"This article introduces the Risk Balancing Frontier (RBF), a new portfolio boundary in the absolute risk-total return space: the RBF arises when two risk indicators, the Tracking Error Volatility (TEV) and the Value-at-Risk (VaR), are both constrained not to exceed pre-set maximum values. By focusing on the trade-off between the joint restrictions on the two risk indicators, this frontier is the set of all portfolios characterized by the minimum VaR attainable for each TEV level. First, the RBF is defined analytically and its mathematical properties are discussed: we show its connection with the Constrained Tracking Error Volatility Frontier (Jorion in Financ Anal J, 59(5):70–82, 2003. https://doi.org/10.2469/faj.v59.n5.2565) and the Constrained Value-at-Risk Frontier (Alexander and Baptista in J Econ Dyn Control, 32(3):779–820, 2008. https://doi.org/10.1016/j.jedc.2007.03.005) frontiers. Next, we explore computational issues implied with its construction, and we develop a fast and accurate algorithm to this aim. Finally, we perform an empirical example and consider its relevance in the context of applied finance: we show that the RBF provides a useful tool to investigate and solve potential agency problems.","PeriodicalId":50647,"journal":{"name":"Computational Economics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamics in Realized Volatility Forecasting: Evaluating GARCH Models and Deep Learning Algorithms Across Parameter Variations 已实现波动率预测的动态性：跨参数变化评估 GARCH 模型和深度学习算法

IF 2 4区经济学

Computational Economics Pub Date : 2024-08-12 DOI: 10.1007/s10614-024-10694-2

Omer Burak Akgun, Emrah Gulay

{"title":"Dynamics in Realized Volatility Forecasting: Evaluating GARCH Models and Deep Learning Algorithms Across Parameter Variations","authors":"Omer Burak Akgun, Emrah Gulay","doi":"10.1007/s10614-024-10694-2","DOIUrl":"https://doi.org/10.1007/s10614-024-10694-2","url":null,"abstract":"The modeling and forecasting of return volatility for the top three cryptocurrencies, which are identified by the highest trading volumes, is the main focus of the study. Eleven different GARCH-type models were analyzed using a comprehensive methodology in six different distributions, and deep learning algorithms were used to rigorously assess each model’s forecasting performance. Additionally, the study investigates the impact of selecting dynamic parameters for the forecasting performance of these models. This study investigates if there are any appreciable differences in forecast outcomes between the two different realized variance calculations and variations in training size. Further investigation focuses on how the use of expanding and rolling windows affects the optimal window type for forecasting. Finally, the importance of choosing different error measurements is emphasized in the framework of comparing forecasting performances. Our results indicate that in GARCH-type models, 5-minute realized variance shows the best forecasting performance, while in deep learning models, median realized variance (MedRV) has the best performance. Moreover, it has been determined that an increase in the training/test ratio and the selection of the rolling window approach both play important roles in achieving better forecast accuracy. Finally, our results show that deep learning models outperform GARCH-type models in volatility forecasts.","PeriodicalId":50647,"journal":{"name":"Computational Economics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141947880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Characteristics of RMB Internationalization and Stock Market Co-movement Between China and RCEP Countries: An Analysis Based on Kernel PCA and SV-TVP-SVAR Model 中国与 RCEP 国家间人民币国际化与股市同向波动的特征：基于核 PCA 和 SV-TVP-SVAR 模型的分析

IF 1.9 4区经济学

Computational Economics Pub Date : 2024-08-10 DOI: 10.1007/s10614-024-10691-5

Ke Huang, Zuo-Ming Zhang, Yakun Wang

引用次数: 0

Detecting Insider Trading in the Indian Stock Market: An Optimized Deep Learning Approach 检测印度股市的内幕交易：优化的深度学习方法

IF 1.9 4区经济学

Computational Economics Pub Date : 2024-08-09 DOI: 10.1007/s10614-024-10697-z

Prashant Priyadarshi, Prabhat Kumar

引用次数: 0

Deep Learning for Solving and Estimating Dynamic Macro-finance Models 深度学习用于求解和估算动态宏观金融模型