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1 – 10 of over 6000Torben G. Andersen, Tim Bollerslev, Francis X. Diebold and Ginger Wu
A large literature over several decades reveals both extensive concern with the question of time-varying betas and an emerging consensus that betas are in fact time-varying…
Abstract
A large literature over several decades reveals both extensive concern with the question of time-varying betas and an emerging consensus that betas are in fact time-varying, leading to the prominence of the conditional CAPM. Set against that background, we assess the dynamics in realized betas, vis-à-vis the dynamics in the underlying realized market variance and individual equity covariances with the market. Working in the recently popularized framework of realized volatility, we are led to a framework of nonlinear fractional cointegration: although realized variances and covariances are very highly persistent and well approximated as fractionally integrated, realized betas, which are simple nonlinear functions of those realized variances and covariances, are less persistent and arguably best modeled as stationary I(0) processes. We conclude by drawing implications for asset pricing and portfolio management.
Tourism demand forecasting is vital for the airline industry and tourism sector. Combination forecasting has the advantage of fusing several forecasts to reduce the risk of…
Abstract
Purpose
Tourism demand forecasting is vital for the airline industry and tourism sector. Combination forecasting has the advantage of fusing several forecasts to reduce the risk of inappropriate model selection for analyzing decisions. This paper investigated the effects of a time-varying weighting strategy on the performance of linear and nonlinear forecast combinations in the context of tourism.
Design/methodology/approach
This study used grey prediction models, which did not require that the available data satisfy statistical assumptions, to generate forecasts. A quality-control technique was applied to determine when to change the combination weights to generate combined forecasts by using linear and nonlinear methods.
Findings
The empirical results showed that except for when the Choquet fuzzy integral was used, forecast combination with time-varying weights did not significantly outperform that with fixed weights. The Choquet integral with time-varying weights significantly outperformed that with fixed weights for all model combinations, and had a superior forecasting accuracy to those of other combination methods.
Practical implications
The tourism sector can benefit from the use of the Choquet integral with time-varying weights, by using it to formulate suitable strategies for tourist destinations.
Originality/value
Combining forecasts with time-varying weights may improve the accuracy of the predictions. This study investigated incorporating a time-varying weighting strategy into combination forecasting by using CUSUM. The results verified the effectiveness of the time-varying Choquet integral for tourism forecast combination.
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This paper investigates the usefulness of switching Gaussian state space models as a tool for implementing dynamic model selection (DMS) or averaging (DMA) in time-varying…
Abstract
This paper investigates the usefulness of switching Gaussian state space models as a tool for implementing dynamic model selection (DMS) or averaging (DMA) in time-varying parameter regression models. DMS methods allow for model switching, where a different model can be chosen at each point in time. Thus, they allow for the explanatory variables in the time-varying parameter regression model to change over time. DMA will carry out model averaging in a time-varying manner. We compare our exact method for implementing DMA/DMS to a popular existing procedure which relies on the use of forgetting factor approximations. In an application, we use DMS to select different predictors in an inflation forecasting application. We find strong evidence of model switching. We also compare different ways of implementing DMA/DMS and find forgetting factor approaches and approaches based on the switching Gaussian state space model to lead to similar results.
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The purpose of this paper is to determine and contrast the risk mitigating effectiveness from optimal multiproduct time-varying hedge ratios, applied to the margin of a cattle…
Abstract
Purpose
The purpose of this paper is to determine and contrast the risk mitigating effectiveness from optimal multiproduct time-varying hedge ratios, applied to the margin of a cattle feedlot operation, over single commodity time-varying and naive hedge ratios.
Design/methodology/approach
A parsimonious regime-switching dynamic correlations (RSDC) model is estimated in two-stages, where the dynamic correlations among prices of numerous commodities vary proportionally between two different regimes/levels. This property simplifies estimation methods for a large number of parameters involved.
Findings
There is significant evidence that resulting simultaneous correlations among the prices (spot and futures) for each commodity attain different levels along the time-series. Second, for in and out-of-sample data there is a substantial reduction in the operation's margin variance provided from both multiproduct and single time-varying optimal hedge ratios over naive hedge ratios. Lastly, risk mitigation is attained at a lower cost given that average optimal multiproduct and single time-varying hedge ratios obtained for corn, feeder cattle and live cattle are significantly below the naive full hedge ratio.
Research limitations/implications
The application studied is limited in that once a hedge position has been set at a particular period, it is not possible to modify or update at a subsequent period.
Practical implications
Agricultural producers, specifically cattle feeders, may profit from a tool using improved techniques to determine hedge ratios by considering a larger amount of up-to-date information. Moreover, these agents may apply hedge ratios significantly lower than one and thus mitigate risk at lower costs.
Originality/value
Feedlot operators will benefit from the potential implementation of this parsimonious RSDC model for their hedging operations, as it provides average optimal hedge ratios significantly lower than one and sizeable advantages in margin risk mitigation.
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In this paper, the authors aim to investigate the short‐run as well as long‐run market efficiency of Indian commodity futures markets using different asset pricing models. Four…
Abstract
Purpose
In this paper, the authors aim to investigate the short‐run as well as long‐run market efficiency of Indian commodity futures markets using different asset pricing models. Four agricultural (soybean, corn, castor seed and guar seed) and seven non‐agricultural (gold, silver, aluminium, copper, zinc, crude oil and natural gas) commodities have been tested for market efficiency and unbiasedness.
Design/methodology/approach
The long‐run market efficiency and unbiasedness is tested using Johansen cointegration procedure while allowing for constant risk premium. Short‐run price dynamics is investigated with constant and time varying risk premium. Short‐run price dynamics with constant risk premium is modeled with ECM model and short‐run price dynamics with time varying risk premium is modeled using ECM‐GARCH in‐Mean framework.
Findings
As far as long‐run efficiency is concerned, the authors find that near month futures prices of most of the commodities are cointegrated with the spot prices. The cointegration relationship is not found for the next to near months futures contracts, where futures trading volume is low. The authors find support for the hypothesis that thinly traded contracts fail to forecast future spot prices and are inefficient. The unbiasedness hypothesis is rejected for most of the commodities. It is also found that for all commodities, some inefficiency exists in the short run. The authors do not find support of time varying risk premium in Indian commodity market context.
Originality/value
In context of Indian commodity futures markets, probably this is the first study which explores the short‐run market efficiency of futures markets in time varying risk premium framework. This paper also links trading activity of Indian commodity futures markets with market efficiency.
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The purpose of this study is to clarify the nature of the predictive relationship between crude oil and the US stock market, with particular attention to whether this relationship…
Abstract
Purpose
The purpose of this study is to clarify the nature of the predictive relationship between crude oil and the US stock market, with particular attention to whether this relationship is driven by time-varying risk premia.
Design/methodology/approach
The authors formulate the predictive regression as a state-space model and estimate the time-varying coefficients via the Kalman filter and prediction-error decomposition.
Findings
The authors find that the nature of the predictive relationship between crude oil and the US stock market changed in the latter half of 2008. After mid-2008, the predictive relationship switched signs and exhibited characteristics which make it much more likely that the predictive relationship is due to time-varying risk premia rather than a market inefficiency.
Originality/value
The authors apply a state-space approach to modeling the predictive relationship. This allows one to watch the evolution of the predictive relationship over time. In particular, the authors identify a dramatic shift in the relationship around August 2008. Prior research has not been able to identify shifts in the relationship.
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Nan Li and Liu Yuanchun
The purpose of this paper is to summarize different methods of constructing the financial conditions index (FCI) and analyze current studies on constructing FCI for China. Due to…
Abstract
Purpose
The purpose of this paper is to summarize different methods of constructing the financial conditions index (FCI) and analyze current studies on constructing FCI for China. Due to shifts of China’s financial mechanisms in the post-crisis era, conventional ways of FCI construction have their limitations.
Design/methodology/approach
The paper suggests improvements in two aspects, i.e. using time-varying weights and introducing non-financial variables. In the empirical study, the author first develops an FCI with fixed weights for comparison, constructs a post-crisis FCI based on time-varying parameter vector autoregressive model and finally examines the FCI with time-varying weights concerning its explanatory and predictive power for inflation.
Findings
Results suggest that the FCI with time-varying weights performs better than one with fixed weights and the former better reflects China’s financial conditions. Furthermore, introduction of credit availability improves the FCI.
Originality/value
FCI constructed in this paper goes ahead of inflation by about 11 months, and it has strong explanatory and predictive power for inflation. Constructing an appropriate FCI is important for improving the effectiveness and predictive power of the post-crisis monetary policy and foe achieving both economic and financial stability.
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The purpose of this paper is to explore the impact of WeChat public platforms (abbreviated as WPP) on blood donation behavior using data from the platforms’ backend and…
Abstract
Purpose
The purpose of this paper is to explore the impact of WeChat public platforms (abbreviated as WPP) on blood donation behavior using data from the platforms’ backend and information system.
Design/methodology/approach
First, this paper established a time-varying difference-in-difference (DID) model to evaluate the change before and after following the WPP under normal scenarios. The difference-in-difference-in-difference (DDD) method was further used to analyze the heterogeneous effects of gender, age, occupation and education. Second, a logit model was used to examine the impact of WPP on blood donation behavior under emergency scenarios (i.e. COVID-19).
Findings
The research shows that following WPP has a positive impact on donation volume. For each donor, the average blood donation volume after following WPP increased by 12.94% compared to before following. The WPP has a greater impact on groups with males, medical staff, middle-aged individuals and those with primary school education. Following WPP also enhanced blood donation behavior in emergency scenarios. During the COVID-19 pandemic, the probability of fans donating blood was 2.6% higher than non-fans, and the average blood donation volume of fans was 7.04% higher than non-fans, which was 5.9% lower than in normal scenarios.
Originality/value
For theory, this paper quantified the impact of WPP on blood donation behavior in normal and emergency scenarios and addressed the research gap surrounding the impact exerted by social media on blood donation behavior. For methodology, the time-varying DID model, DDD model and logit model were applied to the field of blood donation, which expanded the application scenarios. For practice, the findings are of great significance for recruiting blood donors and providing evidence for promotion on WPP.
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As the Association of Southeast Asian Nations (ASEAN) becomes an emerging market, US investors will want to know how their favorite method of calculating asset pricing fits into…
Abstract
As the Association of Southeast Asian Nations (ASEAN) becomes an emerging market, US investors will want to know how their favorite method of calculating asset pricing fits into this new undeveloped market. Also, as the ASEAN becomes more internationalized, managers within will look for ways in which the capital asset pricing model (CAPM) can be applied for their needs. This research looks at the capabilities of the CAPM using ex-post time varying and compares it with the traditional constant beta model. The data include five US sectors and five ASEAN countries, for 10 total portfolios. Find that using a simple nonparametric method that allows for time variation is not statistically different from the traditional constant beta model for portfolios. This research provides additional support for the constant beta.
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Existing multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models either impose strong restrictions on the parameters or do not guarantee a…
Abstract
Existing multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models either impose strong restrictions on the parameters or do not guarantee a well-defined (positive-definite) covariance matrix. I discuss the main multivariate GARCH models and focus on the BEKK model for which it is shown that the covariance and correlation is not adequately specified under certain conditions. This implies that any analysis of the persistence and the asymmetry of the correlation is potentially inaccurate. I therefore propose a new Flexible Dynamic Correlation (FDC) model that parameterizes the conditional correlation directly and eliminates various shortcomings. Most importantly, the number of exogenous variables in the correlation equation can be flexibly augmented without risking an indefinite covariance matrix. Empirical results of daily and monthly returns of four international stock market indices reveal that correlations exhibit different degrees of persistence and different asymmetric reactions to shocks than variances. In addition, I find that correlations do not always increase with jointly negative shocks implying a justification for international portfolio diversification.