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Article
Publication date: 20 December 2019

Ya Qian, Wolfgang Härdle and Cathy Yi-Hsuan Chen

Interdependency among industries is vital for understanding economic structures and managing industrial portfolios. However, it is hard to precisely model the interconnecting…

Abstract

Purpose

Interdependency among industries is vital for understanding economic structures and managing industrial portfolios. However, it is hard to precisely model the interconnecting structure among industries. One of the reasons is that the interdependencies show a different pattern in tail events. This paper aims to investigate industry interdependency with the tail events.

Design/methodology/approach

General predictive model of Rapach et al. (2016) is extended to an interdependency model via least absolute shrinkage and selection operator quantile regression and network analysis. A dynamic network approach was applied on the Fama–French industry portfolios to study the time-varying interdependencies.

Findings

A denser network with heterogeneous central industries is found in tail cases. Significant interdependency varieties across time are shown under dynamic network analysis. Market volatility is identified as an influential factor of industry connectedness as well as clustering tendency under both normal and tail cases. Moreover, combining dynamic network with prediction direction information into out-of-sample industry return forecasting, a lower tail case is obtained, which gives the most accurate prediction of one-month forward returns. Finally, the Sharpe ratio criterion prefers high-centrality portfolios when tail risks are considered.

Originality/value

This study examines the industry portfolio interactions under the framework of network analysis and also takes into consideration tail risks. The combination of economic interpretation and statistical methodology helps in having a clear investigation of industry interdependency. Moreover, a new trading strategy based on network centrality seems profitable in our data sample.

Details

Studies in Economics and Finance, vol. 37 no. 1
Type: Research Article
ISSN: 1086-7376

Keywords

Book part
Publication date: 19 October 2020

Andrija Mihoci, Michael Althof, Cathy Yi-Hsuan Chen and Wolfgang Karl Härdle

A systemic risk measure is proposed accounting for links and mutual dependencies between financial institutions utilizing tail event information. Financial Risk Meter (FRM) is…

Abstract

A systemic risk measure is proposed accounting for links and mutual dependencies between financial institutions utilizing tail event information. Financial Risk Meter (FRM) is based on least absolute shrinkage and selection operator quantile regression designed to capture tail event co-movements. The FRM focus lies on understanding active set data characteristics and the presentation of interdependencies in a network topology. Two FRM indices are presented, namely, FRM@Americas and FRM@Europe. The FRM indices detect systemic risk at selected areas and identify risk factors. In practice, FRM is applied to the return time series of selected financial institutions and macroeconomic risk factors. The authors identify companies exhibiting extreme “co-stress” as well as “activators” of stress. With the SRM@EuroArea, the authors extend to the government bond asset class, and to credit default swaps with FRM@iTraxx. FRM is a good predictor for recession probabilities, constituting the FRM-implied recession probabilities. Thereby, FRM indicates tail event behavior in a network of financial risk factors.

Details

The Econometrics of Networks
Type: Book
ISBN: 978-1-83867-576-9

Keywords

Book part
Publication date: 18 January 2022

James Mitchell, Aubrey Poon and Gian Luigi Mazzi

This chapter uses an application to explore the utility of Bayesian quantile regression (BQR) methods in producing density nowcasts. Our quantile regression modeling strategy is…

Abstract

This chapter uses an application to explore the utility of Bayesian quantile regression (BQR) methods in producing density nowcasts. Our quantile regression modeling strategy is designed to reflect important nowcasting features, namely the use of mixed-frequency data, the ragged-edge, and large numbers of indicators (big data). An unrestricted mixed data sampling strategy within a BQR is used to accommodate a large mixed-frequency data set when nowcasting; the authors consider various shrinkage priors to avoid parameter proliferation. In an application to euro area GDP growth, using over 100 mixed-frequency indicators, the authors find that the quantile regression approach produces accurate density nowcasts including over recessionary periods when global-local shrinkage priors are used.

Details

Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling
Type: Book
ISBN: 978-1-80262-062-7

Keywords

Book part
Publication date: 18 October 2019

Mohammad Arshad Rahman and Shubham Karnawat

This article is motivated by the lack of flexibility in Bayesian quantile regression for ordinal models where the error follows an asymmetric Laplace (AL) distribution. The…

Abstract

This article is motivated by the lack of flexibility in Bayesian quantile regression for ordinal models where the error follows an asymmetric Laplace (AL) distribution. The inflexibility arises because the skewness of the distribution is completely specified when a quantile is chosen. To overcome this shortcoming, we derive the cumulative distribution function (and the moment-generating function) of the generalized asymmetric Laplace (GAL) distribution – a generalization of AL distribution that separates the skewness from the quantile parameter – and construct a working likelihood for the ordinal quantile model. The resulting framework is termed flexible Bayesian quantile regression for ordinal (FBQROR) models. However, its estimation is not straightforward. We address estimation issues and propose an efficient Markov chain Monte Carlo (MCMC) procedure based on Gibbs sampling and joint Metropolis–Hastings algorithm. The advantages of the proposed model are demonstrated in multiple simulation studies and implemented to analyze public opinion on homeownership as the best long-term investment in the United States following the Great Recession.

Details

Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part B
Type: Book
ISBN: 978-1-83867-419-9

Keywords

Content available
Book part
Publication date: 19 October 2020

Abstract

Details

The Econometrics of Networks
Type: Book
ISBN: 978-1-83867-576-9

Article
Publication date: 13 October 2023

Ikhlaas Gurrib, Firuz Kamalov, Olga Starkova, Elgilani Eltahir Elshareif and Davide Contu

This paper aims to investigate the role of price-based information from major cryptocurrencies, foreign exchange, equity markets and key commodities in predicting the next-minute…

Abstract

Purpose

This paper aims to investigate the role of price-based information from major cryptocurrencies, foreign exchange, equity markets and key commodities in predicting the next-minute Bitcoin (BTC) price. This study answers the following research questions: What is the best sparse regression model to predict the next-minute price of BTC? What are the key drivers of the BTC price in high-frequency trading?

Design/methodology/approach

Least absolute shrinkage and selection operator and Ridge regressions are adopted using minute-based open-high-low-close prices, volume and trade count for eight major cryptos, global stock market indices, foreign currency pairs, crude oil and gold price information for February 2020–March 2021. This study also examines whether there was any significant break and how the accuracy of the selected models was impacted.

Findings

Findings suggest that Ridge regression is the most effective model for predicting next-minute BTC prices based on BTC-related covariates such as BTC-open, BTC-high and BTC-low, with a moderate amount of regularization. While BTC-based covariates BTC-open and BTC-low were most significant in predicting BTC closing prices during stable periods, BTC-open and BTC-high were most important during volatile periods. Overall findings suggest that BTC’s price information is the most helpful to predict its next-minute closing price after considering various other asset classes’ price information.

Originality/value

To the best of the authors’ knowledge, this is the first paper to identify the covariates of major cryptocurrencies and predict the next-minute BTC crypto price, with a focus on both crypto-asset and cross-market information.

Details

Studies in Economics and Finance, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1086-7376

Keywords

Article
Publication date: 7 August 2017

Shianghau Wu and Jiannjong Guo

In this paper, the authors aim to propose to find the variables that affect the Taiwanese people’s satisfaction level of the general public with the government.

Abstract

Purpose

In this paper, the authors aim to propose to find the variables that affect the Taiwanese people’s satisfaction level of the general public with the government.

Design/methodology/approach

The authors intend to utilize the Bayesian quantile regression to explore the variables that affect the satisfaction of the general public at specific quantiles of Taiwanese satisfaction with the government and rough set classification to explore key variables related to the satisfaction level. Then they make the comparison of the classification among the two methods to obtain the performance of the classification.

Findings

The experiment result shows the major factors which have the positive relationship with the people who have higher satisfaction level with the central government. These factors include satisfaction with the uncorrupted performance of the central government; the evaluation of household’s economic condition one year after the present time; the satisfaction with the Taiwanese central government’s measures on food safety and the satisfaction with the 12 years primary education reform.

Originality/value

The study’s originality hinges on the application of Bayesian quantile regression and rough set classification to the analysis of the Taiwanese satisfaction with the government. It offers more insights on the key variables related to different satisfaction level and the classification performance between the two methods.

Details

Kybernetes, vol. 46 no. 7
Type: Research Article
ISSN: 0368-492X

Keywords

Open Access
Article
Publication date: 15 September 2023

Sanshao Peng, Catherine Prentice, Syed Shams and Tapan Sarker

Given the cryptocurrency market boom in recent years, this study aims to identify the factors influencing cryptocurrency pricing and the major gaps for future research.

3060

Abstract

Purpose

Given the cryptocurrency market boom in recent years, this study aims to identify the factors influencing cryptocurrency pricing and the major gaps for future research.

Design/methodology/approach

A systematic literature review was undertaken. Three databases, Scopus, Web of Science and EBSCOhost, were used for this review. The final analysis comprised 88 articles that met the eligibility criteria.

Findings

The influential factors were identified and categorized as supply and demand, technology, economics, market volatility, investors’ attributes and social media. This review provides a comprehensive and consolidated view of cryptocurrency pricing and maps the significant influential factors.

Originality/value

This paper is the first to systematically and comprehensively review the relevant literature on cryptocurrency to identify the factors of pricing fluctuation. This research contributes to cryptocurrency research as well as to consumer behaviors and marketing discipline in broad.

Details

China Accounting and Finance Review, vol. 26 no. 1
Type: Research Article
ISSN: 1029-807X

Keywords

Article
Publication date: 15 September 2023

Panos Fousekis

This study aims to investigate the connectivity among four principal implied volatility (“fear”) markets in the USA.

Abstract

Purpose

This study aims to investigate the connectivity among four principal implied volatility (“fear”) markets in the USA.

Design/methodology/approach

The empirical analysis relies on daily data (“fear gauge indices”) for the period 2017–2023 and the quantile vector autoregressive (QVAR) approach that allows connectivity (that is, the network topology of interrelated markets) to be quantile-dependent and time-varying.

Findings

Extreme increases in fear are transmitted with higher intensity relative to extreme decreases in it. The implied volatility markets for gold and for stocks are the main risk connectors in the network and also net transmitters of shocks to the implied volatility markets for crude oil and for the euro-dollar exchange rate. Major events such as the COVID-19 pandemic and the war in Ukraine increase connectivity; this increase, however, is likely to be more pronounced at the median than the extremes of the joint distribution of the four fear indices.

Originality/value

This is the first work that uses the QVAR approach to implied volatility markets. The empirical results provide useful insights into how fear spreads across stock and commodities markets, something that is important for risk management, option pricing and forecasting.

Details

Studies in Economics and Finance, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1086-7376

Keywords

Content available
Book part
Publication date: 19 October 2020

Abstract

Details

The Econometrics of Networks
Type: Book
ISBN: 978-1-83867-576-9

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