This study investigates the connectedness between Bitcoin and fiat currencies in two groups of countries:the developed G7 and the emerging BRICS.The methodology adopts the regular(R)-vine copula and compares it with t...This study investigates the connectedness between Bitcoin and fiat currencies in two groups of countries:the developed G7 and the emerging BRICS.The methodology adopts the regular(R)-vine copula and compares it with two benchmark models:the multivariate t copula and the dynamic conditional correlation(DCC)GARCH model.Moreover,this study examines whether the Bitcoin meltdown of 2013,selloff of 2018,COVID-19 pandemic,2021 crash,and the Russia-Ukraine conflict impact the linkage with conventional currencies.The results indicate that for both currency baskets,R-vine beats the benchmark models.Hence,the dependence is better modeled by providing sufficient information on the shock transmission path.Furthermore,the cross-market linkage slightly increases during the Bitcoin crashes,and reaches significant levels during the 2021 and 2022 crises,which may indicate the end of market isolation of the virtual currency.展开更多
This study aims to examine the time-varying efficiency of the Turkish stock market’s major stock index and eight sectoral indices,including the industrial,financial,service,information technology,basic metals,tourism...This study aims to examine the time-varying efficiency of the Turkish stock market’s major stock index and eight sectoral indices,including the industrial,financial,service,information technology,basic metals,tourism,real estate investment,and chemical petrol plastic,during the COVID-19 outbreak and the global financial crisis(GFC)within the framework of the adaptive market hypothesis.This study employs multifractal detrended fluctuation analysis to illustrate these sectors’multifractality and short-and long-term dependence.The results show that all sectoral returns have greater persis-tence during the COVID-19 outbreak than during the GFC.Second,the real estate and information technology industries had the lowest levels of efficiency during the GFC and the COVID-19 outbreak.Lastly,the fat-tailed distribution has a greater effect on multifractality in these industries.Our results validate the conclusions of the adaptive market hypothesis,according to which arbitrage opportunities vary over time,and contribute to policy formulation for future outbreak-induced economic crises.展开更多
This study proposes two new regime-switching volatility models to empirically analyze the impact of the COVID-19 pandemic on hotel stock prices in Japan compared with the US,taking into account the role of stock marke...This study proposes two new regime-switching volatility models to empirically analyze the impact of the COVID-19 pandemic on hotel stock prices in Japan compared with the US,taking into account the role of stock markets.The first model is a direct impact model of COVID-19 on hotel stock prices;the analysis finds that infection speed negatively affects Japanese hotel stock prices and shows that the regime continues to switch to high volatility in prices due to COVID-19 until September 2021,unlike US stock prices.The second model is a hybrid model with COVID-19 and stock market impacts on the hotel stock prices,which can remove the market impacts on regime-switching volatility;this analysis demonstrates that COVID-19 negatively affects hotel stock prices regardless of whether they are in Japan or the US.We also observe a transition to a high-volatility regime in hotel stock prices due to COVID-19 until around summer 2021 in both Japan and the US.These results suggest that COVID-19 is likely to affect hotel stock prices in general,except for the influence of the stock market.Considering the market influence,COVID-19 directly and/or indirectly affects Japanese hotel stocks through the Japanese stock market,and US hotel stocks have limited impacts from COVID-19 owing to the offset between the influence on hotel stocks and no effect on the stock market.Based on the results,investors and portfolio managers should be aware that the impact of COVID-19 on hotel stock returns depends on the balance between the direct and indirect effects,and varies from country to country and region to region.展开更多
This study proposes a wavelets approach to estimating time–frequency-varying betas in the capital asset pricing model(CAPM)framework.The dynamic of systematic risk across time and frequency is analyzed to investigate...This study proposes a wavelets approach to estimating time–frequency-varying betas in the capital asset pricing model(CAPM)framework.The dynamic of systematic risk across time and frequency is analyzed to investigate stock risk-profile robustness.Furthermore,we emphasize the effect of an investor’s investment horizon on the robustness of portfolio characteristics.We use a daily panel of French stocks from 2012 to 2022.Results show that varying systematic risk varies in time and frequency,and that its short and long-run evolutions differ.We observe differences in short and long dynamics,indicating that a stock’s betas differently fluctuate to early announcements or signs of events.However,short-run and long-run betas exhibit similar dynamics during persistent shocks.Betas are more volatile during times of crisis,resulting in greater or lesser robustness of risk profiles.Significant differences exist in short-run and longrun risk profiles,implying a different asset allocation.We conclude that the standard CAPM assumes short-run investment.Then,investors should consider time–frequency CAPM to perform systematic risk analysis and portfolio allocation.展开更多
The out-of-sample R^(2) is designed to measure forecasting performance without look-ahead bias.However,researchers can hack this performance metric even without multiple tests by constructing a prediction model using ...The out-of-sample R^(2) is designed to measure forecasting performance without look-ahead bias.However,researchers can hack this performance metric even without multiple tests by constructing a prediction model using the intuition derived from empirical properties that appear only in the test sample.Using ensemble machine learning techniques,we create a virtual environment that prevents researchers from peeking into the intuition in advance when performing out-of-sample prediction simulations.We apply this approach to robust monitoring,exploiting a dynamic shrink-age effect by switching between a proposed forecast and a benchmark.Considering stock return forecasting as an example,we show that the resulting robust monitoring forecast improves the average performance of the proposed forecast by 15%(in terms of mean-squared-error)and reduces the variance of its relative performance by 46%while avoiding the out-of-sample R^(2)-hacking problem.Our approach,as a final touch,can further enhance the performance and stability of forecasts from any models and methods.展开更多
文摘This study investigates the connectedness between Bitcoin and fiat currencies in two groups of countries:the developed G7 and the emerging BRICS.The methodology adopts the regular(R)-vine copula and compares it with two benchmark models:the multivariate t copula and the dynamic conditional correlation(DCC)GARCH model.Moreover,this study examines whether the Bitcoin meltdown of 2013,selloff of 2018,COVID-19 pandemic,2021 crash,and the Russia-Ukraine conflict impact the linkage with conventional currencies.The results indicate that for both currency baskets,R-vine beats the benchmark models.Hence,the dependence is better modeled by providing sufficient information on the shock transmission path.Furthermore,the cross-market linkage slightly increases during the Bitcoin crashes,and reaches significant levels during the 2021 and 2022 crises,which may indicate the end of market isolation of the virtual currency.
文摘This study aims to examine the time-varying efficiency of the Turkish stock market’s major stock index and eight sectoral indices,including the industrial,financial,service,information technology,basic metals,tourism,real estate investment,and chemical petrol plastic,during the COVID-19 outbreak and the global financial crisis(GFC)within the framework of the adaptive market hypothesis.This study employs multifractal detrended fluctuation analysis to illustrate these sectors’multifractality and short-and long-term dependence.The results show that all sectoral returns have greater persis-tence during the COVID-19 outbreak than during the GFC.Second,the real estate and information technology industries had the lowest levels of efficiency during the GFC and the COVID-19 outbreak.Lastly,the fat-tailed distribution has a greater effect on multifractality in these industries.Our results validate the conclusions of the adaptive market hypothesis,according to which arbitrage opportunities vary over time,and contribute to policy formulation for future outbreak-induced economic crises.
文摘This study proposes two new regime-switching volatility models to empirically analyze the impact of the COVID-19 pandemic on hotel stock prices in Japan compared with the US,taking into account the role of stock markets.The first model is a direct impact model of COVID-19 on hotel stock prices;the analysis finds that infection speed negatively affects Japanese hotel stock prices and shows that the regime continues to switch to high volatility in prices due to COVID-19 until September 2021,unlike US stock prices.The second model is a hybrid model with COVID-19 and stock market impacts on the hotel stock prices,which can remove the market impacts on regime-switching volatility;this analysis demonstrates that COVID-19 negatively affects hotel stock prices regardless of whether they are in Japan or the US.We also observe a transition to a high-volatility regime in hotel stock prices due to COVID-19 until around summer 2021 in both Japan and the US.These results suggest that COVID-19 is likely to affect hotel stock prices in general,except for the influence of the stock market.Considering the market influence,COVID-19 directly and/or indirectly affects Japanese hotel stocks through the Japanese stock market,and US hotel stocks have limited impacts from COVID-19 owing to the offset between the influence on hotel stocks and no effect on the stock market.Based on the results,investors and portfolio managers should be aware that the impact of COVID-19 on hotel stock returns depends on the balance between the direct and indirect effects,and varies from country to country and region to region.
文摘This study proposes a wavelets approach to estimating time–frequency-varying betas in the capital asset pricing model(CAPM)framework.The dynamic of systematic risk across time and frequency is analyzed to investigate stock risk-profile robustness.Furthermore,we emphasize the effect of an investor’s investment horizon on the robustness of portfolio characteristics.We use a daily panel of French stocks from 2012 to 2022.Results show that varying systematic risk varies in time and frequency,and that its short and long-run evolutions differ.We observe differences in short and long dynamics,indicating that a stock’s betas differently fluctuate to early announcements or signs of events.However,short-run and long-run betas exhibit similar dynamics during persistent shocks.Betas are more volatile during times of crisis,resulting in greater or lesser robustness of risk profiles.Significant differences exist in short-run and longrun risk profiles,implying a different asset allocation.We conclude that the standard CAPM assumes short-run investment.Then,investors should consider time–frequency CAPM to perform systematic risk analysis and portfolio allocation.
文摘The out-of-sample R^(2) is designed to measure forecasting performance without look-ahead bias.However,researchers can hack this performance metric even without multiple tests by constructing a prediction model using the intuition derived from empirical properties that appear only in the test sample.Using ensemble machine learning techniques,we create a virtual environment that prevents researchers from peeking into the intuition in advance when performing out-of-sample prediction simulations.We apply this approach to robust monitoring,exploiting a dynamic shrink-age effect by switching between a proposed forecast and a benchmark.Considering stock return forecasting as an example,we show that the resulting robust monitoring forecast improves the average performance of the proposed forecast by 15%(in terms of mean-squared-error)and reduces the variance of its relative performance by 46%while avoiding the out-of-sample R^(2)-hacking problem.Our approach,as a final touch,can further enhance the performance and stability of forecasts from any models and methods.
