In this article, to model risk contagion between the U.S. and China stock markets based on high-frequency financial data, we develop a novel continuous-time jump-diffusion process. For example, we consider three channels for volatility contagion—such as integrated volatility, positive jump variation, and negative jump variation—and each stock market is able to affect the other stock market as an overnight risk factor. We develop a quasi-maximum likelihood estimator for model parameters and establish its asymptotic properties. Furthermore, to identify contagion channels and test the existence of a structural break with a known structural break date, we propose hypothesis test procedures. Using the proposed diffusion model with high-frequency financial data, we investigate the effect of the U.S.–China trade war on stock markets from a financial contagion perspective. From the empirical study, we find evidence of financial contagion from the United States to China and evidence that the risk contagion channel has changed from integrated volatility to negative jump variation.
{"title":"Effect of the U.S.–China Trade War on Stock Markets: A Financial Contagion Perspective","authors":"Minseog Oh, Donggyu Kim","doi":"10.1093/jjfinec/nbad016","DOIUrl":"https://doi.org/10.1093/jjfinec/nbad016","url":null,"abstract":"In this article, to model risk contagion between the U.S. and China stock markets based on high-frequency financial data, we develop a novel continuous-time jump-diffusion process. For example, we consider three channels for volatility contagion—such as integrated volatility, positive jump variation, and negative jump variation—and each stock market is able to affect the other stock market as an overnight risk factor. We develop a quasi-maximum likelihood estimator for model parameters and establish its asymptotic properties. Furthermore, to identify contagion channels and test the existence of a structural break with a known structural break date, we propose hypothesis test procedures. Using the proposed diffusion model with high-frequency financial data, we investigate the effect of the U.S.–China trade war on stock markets from a financial contagion perspective. From the empirical study, we find evidence of financial contagion from the United States to China and evidence that the risk contagion channel has changed from integrated volatility to negative jump variation.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134997445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article considers predictive regressions in which a structural break is allowed on an unknown date. We establish novel testing procedures for asset return predictability using empirical likelihood (EL) methods based on weighted score equations. The theoretical results are useful in practice because our unified framework does not require distinguishing whether the predictor variables are stationary or non-stationary. Monte Carlo simulation studies show that the EL-based tests perform well in terms of size and power in finite samples. Finally, as an empirical analysis, we test the predictability of the monthly S&P 500 value-weighted log excess return using various predictor variables.
{"title":"A New Test on Asset Return Predictability with Structural Breaks","authors":"Zongwu Cai, Seong Yeon Chang","doi":"10.1093/jjfinec/nbad018","DOIUrl":"https://doi.org/10.1093/jjfinec/nbad018","url":null,"abstract":"This article considers predictive regressions in which a structural break is allowed on an unknown date. We establish novel testing procedures for asset return predictability using empirical likelihood (EL) methods based on weighted score equations. The theoretical results are useful in practice because our unified framework does not require distinguishing whether the predictor variables are stationary or non-stationary. Monte Carlo simulation studies show that the EL-based tests perform well in terms of size and power in finite samples. Finally, as an empirical analysis, we test the predictability of the monthly S&P 500 value-weighted log excess return using various predictor variables.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136041493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This article addresses a portfolio selection problem with trading costs on stock market. More precisely, we develop a simple generalized method of moments (GMM)-based test procedure to test the significance of trading costs effect in the economy with a flexible form of transaction costs. We also propose a two-step procedure to test overidentifying restrictions in our GMM estimation. In an empirical analysis, we apply our test procedures to the class of anomalies used in Novy-Marx and Velikov (2016). We show that transaction costs have a significant effect on investors’ behavior for many anomalies. In that case, investors significantly improve the out-of-sample performance of their portfolios by accounting for trading costs.
{"title":"Test for Trading Costs Effect in a Portfolio Selection Problem with Recursive Utility","authors":"Marine Carrasco, N’Golo Koné","doi":"10.1093/jjfinec/nbad015","DOIUrl":"https://doi.org/10.1093/jjfinec/nbad015","url":null,"abstract":"Abstract This article addresses a portfolio selection problem with trading costs on stock market. More precisely, we develop a simple generalized method of moments (GMM)-based test procedure to test the significance of trading costs effect in the economy with a flexible form of transaction costs. We also propose a two-step procedure to test overidentifying restrictions in our GMM estimation. In an empirical analysis, we apply our test procedures to the class of anomalies used in Novy-Marx and Velikov (2016). We show that transaction costs have a significant effect on investors’ behavior for many anomalies. In that case, investors significantly improve the out-of-sample performance of their portfolios by accounting for trading costs.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135380436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We propose a method based on control charts to identify in real-time sudden deposits outflows through the payment system. The performance of the methodology is assessed with both Monte Carlo simulations and real transaction-level TARGET2 data for a large sample of Italian banks. We identify a set of idiosyncratic bank stress episodes. Using high-frequency payment system data, we provide new evidences on the interaction between retail, wholesale, and central bank funding in the post global financial crisis period.
{"title":"Real-Time Identification and High-Frequency Analysis of Deposits Outflows","authors":"Edoardo Rainone","doi":"10.1093/jjfinec/nbad012","DOIUrl":"https://doi.org/10.1093/jjfinec/nbad012","url":null,"abstract":"\u0000 We propose a method based on control charts to identify in real-time sudden deposits outflows through the payment system. The performance of the methodology is assessed with both Monte Carlo simulations and real transaction-level TARGET2 data for a large sample of Italian banks. We identify a set of idiosyncratic bank stress episodes. Using high-frequency payment system data, we provide new evidences on the interaction between retail, wholesale, and central bank funding in the post global financial crisis period.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47178377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ilias Chronopoulos, Aristeidis Raftapostolos, G. Kapetanios
In this article, we use a deep quantile estimator, based on neural networks and their universal approximation property to examine a non-linear association between the conditional quantiles of a dependent variable and predictors. This methodology is versatile and allows both the use of different penalty functions, as well as high dimensional covariates. We present a Monte Carlo exercise where we examine the finite sample properties of the deep quantile estimator and show that it delivers good finite sample performance. We use the deep quantile estimator to forecast value-at-risk and find significant gains over linear quantile regression alternatives and other models, which are supported by various testing schemes. Further, we consider also an alternative architecture that allows the use of mixed frequency data in neural networks. This article also contributes to the interpretability of neural network output by making comparisons between the commonly used Shapley Additive Explanation values and an alternative method based on partial derivatives.
{"title":"Forecasting Value-at-Risk Using Deep Neural Network Quantile Regression","authors":"Ilias Chronopoulos, Aristeidis Raftapostolos, G. Kapetanios","doi":"10.1093/jjfinec/nbad014","DOIUrl":"https://doi.org/10.1093/jjfinec/nbad014","url":null,"abstract":"\u0000 In this article, we use a deep quantile estimator, based on neural networks and their universal approximation property to examine a non-linear association between the conditional quantiles of a dependent variable and predictors. This methodology is versatile and allows both the use of different penalty functions, as well as high dimensional covariates. We present a Monte Carlo exercise where we examine the finite sample properties of the deep quantile estimator and show that it delivers good finite sample performance. We use the deep quantile estimator to forecast value-at-risk and find significant gains over linear quantile regression alternatives and other models, which are supported by various testing schemes. Further, we consider also an alternative architecture that allows the use of mixed frequency data in neural networks. This article also contributes to the interpretability of neural network output by making comparisons between the commonly used Shapley Additive Explanation values and an alternative method based on partial derivatives.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47131967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rafael P Alves, Diego S de Brito, Marcelo C Medeiros, Ruy M Ribeiro
Abstract We propose a model to forecast large realized covariance matrices of returns, applying it to the constituents of the S&P 500 daily. To address the curse of dimensionality, we decompose the return covariance matrix using standard firm-level factors (e.g., size, value, and profitability) and use sectoral restrictions in the residual covariance matrix. This restricted model is then estimated using vector heterogeneous autoregressive models with the least absolute shrinkage and selection operator. Our methodology improves forecasting precision relative to standard benchmarks and leads to better estimates of minimum variance portfolios.
{"title":"Forecasting Large Realized Covariance Matrices: The Benefits of Factor Models and Shrinkage","authors":"Rafael P Alves, Diego S de Brito, Marcelo C Medeiros, Ruy M Ribeiro","doi":"10.1093/jjfinec/nbad013","DOIUrl":"https://doi.org/10.1093/jjfinec/nbad013","url":null,"abstract":"Abstract We propose a model to forecast large realized covariance matrices of returns, applying it to the constituents of the S&P 500 daily. To address the curse of dimensionality, we decompose the return covariance matrix using standard firm-level factors (e.g., size, value, and profitability) and use sectoral restrictions in the residual covariance matrix. This restricted model is then estimated using vector heterogeneous autoregressive models with the least absolute shrinkage and selection operator. Our methodology improves forecasting precision relative to standard benchmarks and leads to better estimates of minimum variance portfolios.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135473661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Macroeconomic and aggregate financial series were shown empirically to share an unconventional form of cyclical and persistent dynamics, whose functional form was obtained from the solution of general-equilibrium models with heterogeneous firms. The econometric modeling of equations that link such series requires a new methodology, as existing parametric techniques can cause paradoxical regression results and omit predictabilities. We provide a solution to disentangle the genuine relation between variables (the parameters linking them) from the unconventional dynamics that drive them. As an application, we show that GBP-USD forward premia have no predictive power for excess returns over 1976–2015 (thus solving this forward-premium puzzle) once the unconventional dynamics of spot rates are modeled. Taking advantage of these dynamics, we uncover a trading strategy which consistently outperforms existing ones in the out-of-sample period 2015–2021, delivering almost treble their profits and yielding a Sharpe ratio of 85%. Hence, even in this heavily traded market, the efficient market hypothesis has been failing for over 45 years as persistent profit opportunities remained unexploited because of the unconventional dynamics of the spot rate.
{"title":"Beyond Co-integration: New Tools for Inference on Co-movements","authors":"K. Abadir, G. Talmain","doi":"10.1093/jjfinec/nbad010","DOIUrl":"https://doi.org/10.1093/jjfinec/nbad010","url":null,"abstract":"\u0000 Macroeconomic and aggregate financial series were shown empirically to share an unconventional form of cyclical and persistent dynamics, whose functional form was obtained from the solution of general-equilibrium models with heterogeneous firms. The econometric modeling of equations that link such series requires a new methodology, as existing parametric techniques can cause paradoxical regression results and omit predictabilities. We provide a solution to disentangle the genuine relation between variables (the parameters linking them) from the unconventional dynamics that drive them. As an application, we show that GBP-USD forward premia have no predictive power for excess returns over 1976–2015 (thus solving this forward-premium puzzle) once the unconventional dynamics of spot rates are modeled. Taking advantage of these dynamics, we uncover a trading strategy which consistently outperforms existing ones in the out-of-sample period 2015–2021, delivering almost treble their profits and yielding a Sharpe ratio of 85%. Hence, even in this heavily traded market, the efficient market hypothesis has been failing for over 45 years as persistent profit opportunities remained unexploited because of the unconventional dynamics of the spot rate.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42721053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We propose a four-state regime-switching model that pairs low-volatility and high-volatility (HV) states to test eight stock–safe-haven asset portfolios’ risk properties. We find the correlations between gold, U.S. T-bond, and the Swiss franc and stock markets are negative or zero in all states, including the HV–HV state, while the correlations between Bitcoin (BTC) and stock markets are positive in the HV–HV state, implying that gold, T-bond, and the Swiss franc are full safe-havens and BTC is a partial safe-haven asset. Moreover, our model is effective in portfolio construction, performing better than conventional time-varying generalized autoregressive conditional heteroskedasticity-based models.
{"title":"When Safe-Haven Asset Is Less than a Safe-Haven Play","authors":"Leon Li, Carl R. Chen","doi":"10.1093/jjfinec/nbad009","DOIUrl":"https://doi.org/10.1093/jjfinec/nbad009","url":null,"abstract":"\u0000 We propose a four-state regime-switching model that pairs low-volatility and high-volatility (HV) states to test eight stock–safe-haven asset portfolios’ risk properties. We find the correlations between gold, U.S. T-bond, and the Swiss franc and stock markets are negative or zero in all states, including the HV–HV state, while the correlations between Bitcoin (BTC) and stock markets are positive in the HV–HV state, implying that gold, T-bond, and the Swiss franc are full safe-havens and BTC is a partial safe-haven asset. Moreover, our model is effective in portfolio construction, performing better than conventional time-varying generalized autoregressive conditional heteroskedasticity-based models.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43809078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Giuseppe Buccheri, Stefano Grassi, Giorgio Vocalelli
Abstract This article deals with the problem of estimating the volatility of a financial security in a market with frictions. We propose a microstructural model with time-varying fundamental price volatility in which the trading price varies only if the value of the information signal is large enough to guarantee a profit in excess of transaction costs. Using transaction data only, the proposed approach allows to recover: (i) the conditional volatility of the information signal, which is thus cleaned out by market frictions and (ii) an estimate of transaction costs. Our analysis reveals that, after correcting for frictions, the risk of illiquid securities is substantially different from what is predicted by traditional volatility models. Furthermore, using a big dataset of intraday returns, we show that our transaction cost estimate is highly correlated with the main illiquidity measures and that such correlations are significant under different volatility regimes.
{"title":"Estimating Risk in Illiquid Markets: A Model of Market Friction with Stochastic Volatility","authors":"Giuseppe Buccheri, Stefano Grassi, Giorgio Vocalelli","doi":"10.1093/jjfinec/nbad006","DOIUrl":"https://doi.org/10.1093/jjfinec/nbad006","url":null,"abstract":"Abstract This article deals with the problem of estimating the volatility of a financial security in a market with frictions. We propose a microstructural model with time-varying fundamental price volatility in which the trading price varies only if the value of the information signal is large enough to guarantee a profit in excess of transaction costs. Using transaction data only, the proposed approach allows to recover: (i) the conditional volatility of the information signal, which is thus cleaned out by market frictions and (ii) an estimate of transaction costs. Our analysis reveals that, after correcting for frictions, the risk of illiquid securities is substantially different from what is predicted by traditional volatility models. Furthermore, using a big dataset of intraday returns, we show that our transaction cost estimate is highly correlated with the main illiquidity measures and that such correlations are significant under different volatility regimes.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":"276 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136090929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We study two sources of heteroscedasticity in high-frequency financial data and estimate their contribution to overall volatility by means of a Markov switching (MS) structural VAR model. We achieve identification for all coefficients by assuming that the structural errors follow a GARCH-DCC process. Using transaction data of the EUR/USD interdealer market in 2016, we first detect three regimesof volatility. Then we show that both sources of volatility matter for the transmission of shocks, and that information is channeled to the market mostly through demand shocks. This suggests that, on the EUR/USD market, some liquidity takers (LTs) are better informed than both liquidity providers and those LTs who follow a feedback strategy.
{"title":"Endogenous Volatility in the Foreign Exchange Market","authors":"Leonardo Bargigli, G. Cifarelli","doi":"10.1093/jjfinec/nbad008","DOIUrl":"https://doi.org/10.1093/jjfinec/nbad008","url":null,"abstract":"We study two sources of heteroscedasticity in high-frequency financial data and estimate their contribution to overall volatility by means of a Markov switching (MS) structural VAR model. We achieve identification for all coefficients by assuming that the structural errors follow a GARCH-DCC process. Using transaction data of the EUR/USD interdealer market in 2016, we first detect three regimesof volatility. Then we show that both sources of volatility matter for the transmission of shocks, and that information is channeled to the market mostly through demand shocks. This suggests that, on the EUR/USD market, some liquidity takers (LTs) are better informed than both liquidity providers and those LTs who follow a feedback strategy.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47622960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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