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Effect of the U.S.–China Trade War on Stock Markets: A Financial Contagion Perspective 中美贸易战对股市的影响:金融传染的视角
3区 经济学 Q2 BUSINESS, FINANCE Pub Date : 2023-06-29 DOI: 10.1093/jjfinec/nbad016
Minseog Oh, Donggyu Kim
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.
在本文中,基于高频金融数据,我们建立了一个新的连续时间跳跃扩散过程来模拟中美股市之间的风险传染。例如,我们考虑了波动率传染的三种渠道,如综合波动率、正跳变和负跳变,并且每个股票市场都能够作为隔夜风险因素影响其他股票市场。给出了模型参数的拟极大似然估计,并建立了其渐近性质。此外,为了识别传染渠道并测试具有已知结构断裂日期的结构断裂的存在性,我们提出了假设检验程序。本文利用提出的高频金融数据扩散模型,从金融传染的角度考察了中美贸易战对股市的影响。从实证研究中,我们发现了从美国到中国的金融传染的证据,证据表明风险传染渠道已经从整体波动率转变为负跳变。
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引用次数: 0
A New Test on Asset Return Predictability with Structural Breaks 结构性断裂下资产收益可预测性的新检验
3区 经济学 Q2 BUSINESS, FINANCE Pub Date : 2023-06-02 DOI: 10.1093/jjfinec/nbad018
Zongwu Cai, Seong Yeon Chang
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.
本文考虑在未知日期允许结构中断的预测回归。我们利用基于加权分数方程的经验似然(EL)方法建立了新的资产回报可预测性测试程序。理论结果在实践中是有用的,因为我们的统一框架不需要区分预测变量是平稳的还是非平稳的。蒙特卡罗仿真研究表明,基于el的测试在有限样本的大小和功率方面表现良好。最后,作为实证分析,我们使用不同的预测变量检验了月度标准普尔500指数价值加权对数超额收益的可预测性。
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引用次数: 0
Test for Trading Costs Effect in a Portfolio Selection Problem with Recursive Utility 具有递归效用的投资组合问题中交易成本效应的检验
3区 经济学 Q2 BUSINESS, FINANCE Pub Date : 2023-05-22 DOI: 10.1093/jjfinec/nbad015
Marine Carrasco, N’Golo Koné
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.
摘要本文研究了股票市场中存在交易成本的投资组合选择问题。更准确地说,我们开发了一种基于简单广义矩量法(GMM)的检验程序来检验交易成本效应在具有灵活交易成本形式的经济体中的显著性。我们还提出了一个两步程序来测试我们的GMM估计中的过度识别限制。在实证分析中,我们将测试程序应用于Novy-Marx和Velikov(2016)中使用的异常类。我们发现,交易成本对许多异常情况下的投资者行为有显著影响。在这种情况下,投资者通过考虑交易成本,显著提高了其投资组合的样本外表现。
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引用次数: 0
Real-Time Identification and High-Frequency Analysis of Deposits Outflows 存款流出的实时识别与高频分析
IF 2.5 3区 经济学 Q2 BUSINESS, FINANCE Pub Date : 2023-05-18 DOI: 10.1093/jjfinec/nbad012
Edoardo Rainone
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.
我们提出了一种基于控制图的方法来实时识别通过支付系统的突然存款流出。该方法的性能通过蒙特卡洛模拟和意大利银行大样本的真实交易水平TARGET2数据进行了评估。我们发现了一组特殊的银行压力事件。利用高频支付系统数据,我们为后全球金融危机时期零售、批发和央行融资之间的互动提供了新的证据。
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引用次数: 0
Forecasting Value-at-Risk Using Deep Neural Network Quantile Regression 利用深度神经网络分位数回归预测风险价值
IF 2.5 3区 经济学 Q2 BUSINESS, FINANCE Pub Date : 2023-05-15 DOI: 10.1093/jjfinec/nbad014
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.
在本文中,我们使用基于神经网络及其普遍逼近性质的深度分位数估计器来检验因变量的条件分位数与预测因子之间的非线性关联。这种方法是通用的,既允许使用不同的惩罚函数,也允许使用高维协变量。我们提出了一个蒙特卡罗练习,在那里我们检查了深度分位数估计器的有限样本性质,并表明它提供了良好的有限样本性能。我们使用深度分位数估计器来预测风险价值,并发现与线性分位数回归替代方案和其他模型相比有显著的收益,这些模型得到了各种测试方案的支持。此外,我们还考虑了一种允许在神经网络中使用混合频率数据的替代架构。本文还通过比较常用的Shapley加性解释值和一种基于偏导数的替代方法,为神经网络输出的可解释性做出了贡献。
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引用次数: 2
Forecasting Large Realized Covariance Matrices: The Benefits of Factor Models and Shrinkage 预测大已实现协方差矩阵:因子模型和收缩的好处
3区 经济学 Q2 BUSINESS, FINANCE Pub Date : 2023-05-11 DOI: 10.1093/jjfinec/nbad013
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.
摘要本文提出了一个预测收益的大已实现协方差矩阵的模型,并将其应用于标准普尔500指数成分股。为了解决维度的诅咒,我们使用标准的公司层面因素(例如,规模,价值和盈利能力)分解回报协方差矩阵,并在残差协方差矩阵中使用部门限制。然后使用具有最小绝对收缩和选择算子的矢量异构自回归模型估计该受限模型。我们的方法提高了相对于标准基准的预测精度,并导致对最小方差组合的更好估计。
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引用次数: 0
Beyond Co-integration: New Tools for Inference on Co-movements 超越共同整合:推断共同运动的新工具
IF 2.5 3区 经济学 Q2 BUSINESS, FINANCE Pub Date : 2023-05-11 DOI: 10.1093/jjfinec/nbad010
K. Abadir, G. Talmain
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.
宏观经济和总金融序列在经验上显示出具有非常规形式的周期性和持久性动态,其函数形式是从异质企业的一般均衡模型的解中获得的。连接此类序列的方程的计量经济学建模需要一种新的方法,因为现有的参数技术可能会导致矛盾的回归结果,并忽略可预测性。我们提供了一种解决方案,将变量(连接变量的参数)与驱动变量的非常规动力学之间的真正关系区分开来。作为一个应用,我们表明,一旦对现货利率的非常规动态进行建模,GBP-USD远期溢价对1976-2015年的超额回报没有预测能力(从而解决了这一远期溢价难题)。利用这些动态,我们发现了一种交易策略,该策略在2015-2021年的非样本期内始终优于现有策略,实现了几乎三倍的利润,并产生了85%的夏普比率。因此,即使在这个交易量很大的市场中,有效市场假说也已经失败了45多年 多年来,由于现货利率的非常规动态,持续的盈利机会仍未被开发。
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引用次数: 0
When Safe-Haven Asset Is Less than a Safe-Haven Play 当避风港资产低于避风港游戏
IF 2.5 3区 经济学 Q2 BUSINESS, FINANCE Pub Date : 2023-04-03 DOI: 10.1093/jjfinec/nbad009
Leon Li, Carl R. Chen
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.
我们提出了一个四态制度转换模型,将低波动性和高波动性(HV)状态配对,以测试八种股票-避险资产组合的风险特性。我们发现,在包括HV–HV州在内的所有州,黄金、美国国债和瑞士法郎与股市之间的相关性都是负或零,而在HV–HV状态下,比特币(BTC)与股市的相关性是正的,这意味着黄金、国债和瑞士郎是完全的避风港,BTC是部分避风港资产。此外,我们的模型在投资组合构建中是有效的,比传统的基于时变广义自回归条件异方差的模型表现更好。
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引用次数: 1
Estimating Risk in Illiquid Markets: A Model of Market Friction with Stochastic Volatility 非流动性市场的风险估计:一个具有随机波动的市场摩擦模型
3区 经济学 Q2 BUSINESS, FINANCE Pub Date : 2023-03-24 DOI: 10.1093/jjfinec/nbad006
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.
摘要本文研究了存在摩擦的市场中金融证券波动率的估计问题。我们提出了一个具有时变基本价格波动的微观结构模型,在该模型中,只有当信息信号的价值大到足以保证利润超过交易成本时,交易价格才会变化。仅使用交易数据,所提出的方法允许恢复:(i)信息信号的条件波动,因此被市场摩擦清除;(ii)交易成本的估计。我们的分析表明,在修正摩擦后,非流动性证券的风险与传统波动率模型预测的风险有很大不同。此外,使用日内收益的大数据集,我们表明我们的交易成本估计与主要的非流动性措施高度相关,并且这种相关性在不同的波动性制度下显着。
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引用次数: 0
Endogenous Volatility in the Foreign Exchange Market 外汇市场的内生波动
IF 2.5 3区 经济学 Q2 BUSINESS, FINANCE Pub Date : 2023-03-24 DOI: 10.1093/jjfinec/nbad008
Leonardo Bargigli, G. Cifarelli
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.
我们研究了高频金融数据中异方差的两个来源,并通过马尔可夫切换(MS)结构VAR模型估计了它们对总体波动率的贡献。我们通过假设结构误差遵循GARCH-DCC过程来实现所有系数的识别。利用2016年欧元/美元交易商间市场的交易数据,我们首先发现了三种波动机制。然后我们表明,波动性的两个来源对冲击的传递都很重要,信息主要是通过需求冲击传递给市场的。这表明,在欧元/美元市场上,一些流动性接受者(lt)比流动性提供者和那些遵循反馈策略的lt都更了解情况。
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引用次数: 0
期刊
Journal of Financial Econometrics
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