Better to give than to receive: Predictive directional measurement of volatility spillovers

Using a generalized vector autoregressive framework in which forecast-error variance decompositions are invariant to the variable ordering, we propose measures of both the total and directional volatility spillovers. We use our methods to characterize daily volatility spillovers across US stock, bond, foreign exchange and commodities markets, from January 1999 to January 2010. We show that despite significant volatility fluctuations in all four markets during the sample, cross-market volatility spillovers were quite limited until the global financial crisis, which began in 2007. As the crisis intensified, so too did the volatility spillovers, with particularly important spillovers from the stock market to other markets taking place after the collapse of the Lehman Brothers in September 2008.     

Volatility spillovers among the U.S. and Asian stock markets: A comparison between the periods of Asian currency crisis and subprime credit crisis

This paper examines the changing nature of volatility spillovers among the U.S. and eight East Asian stock markets between two financial crises: the Asian currency crisis and the U.S. subprime credit crisis. Our empirical results suggest that volatility is not always spilled over from the directly affected markets to surrounding markets in crisis periods. The East Asian markets who directly suffered from the Asian currency crisis are the ones to which volatility is spilled over from other markets during the Asian currency crisis period, whereas uni-directional volatility spillovers from the U.S. market to other markets are observed during both crisis periods. This difference can be explained by a pre-determined hierarchy in which volatility spillovers tend to start from the U.S. market regardless of the geographical origin of the crisis. Furthermore, our results reveal that the markets in three major Asian financial hubs, i.e., Japan, Hong Kong and Singapore, are the markets to which volatility is spilled over uni-directionally from several other countries during the subprime credit crisis period, but not during the Asian currency crisis period. We attribute this difference to crisis-specific (currency or credit crisis), market-specific (credit derivatives market participation and foreign currency reserves), and time-specific (more integrated global market) factors.     

The effect of investors’ confidence on monetary policy transmission mechanism: A Multivariate GARCH approach

This paper investigates the financial stability’s effect on the monetary policy transmission mechanisms. The correlations between investors’ confidence in the markets, money growth and economic growth are analyzed along with the correlations within their volatilities. Specifically, the heteroskedasticity of the errors is exploited in a Multivariate GARCH framework to obtain endogenously estimated measures of uncertainty. By a two-step estimator, the indirect interplay of money growth and financial markets is highlighted at different time horizons. The results contrast previous literature supportive of the “Great Moderation” as causing the recent financial crisis. Effectively, by accounting for the breaks in volatility series due to structural shifts in monetary policy, a low period of macroeconomic volatility is found not to drive directly low financial stability.     

The impact of VIX on China’s financial market: A new perspective based on high-dimensional and time-varying methods

Based on the new perspective of high-dimensional and time-varying methods, this paper analyzes the contagion effects of US financial market volatility on China’s nine financial sub-markets. The results show evidence of non-linear Granger causality from the US financial volatility (VIX) to the China’s financial markets. Increased US financial volatility has a negative next-day impact on the stock, bond, fund, interest rate, foreign exchange, industrial product and agricultural product markets, and a positive next-day impact on the gold and real estate markets. US financial volatility has the greatest impact on industrial product market, following by stock, agricultural product, fund, real estate, bond, gold, foreign exchange, and interest rates. Major risk events such as the global financial crisis can cause an enhanced contagion effect of US financial volatility to China's financial markets. This paper supports the achievements of China's actions to prevent and resolve major financial risks in the period of the COVID-19 epidemic.     

Volatility analysis of precious metals returns and oil returns: An ICSS approach

This study examines volatility persistence on precious metals returns taking into account oil returns and the three world major stock equity indices (Dow Jones Industrial, FTSE 100, and Nikkei 225) using daily data over the sample period January 1995 to May 2008; the aim is to analyze market relationships before the global financial crisis. We first determine when large changes in the volatility of each market returns occur by identifying major global events that would increase fluctuations in these markets. The Iterated Cumulative Sums of Squares (ICSS) algorithm was used to identify the existence of structural breaks or sudden changes in the variance of returns. In each market the standardized residuals were obtained through the GARCH(1,1) mean equation. Our main results identify a clear relationship between precious metals returns and oil returns, while the interaction between precious metals and stock returns seems to be an independent one in the case of gold with mixed results for silver and platinum. In relation to volatility persistence, the results show clear evidence of high volatility persistence between these markets, especially during times when markets were affected by excessive volatility due to economic and financial shocks.     

Overnight stock returns and realized volatility

The information flow in modern financial markets is continuous, but major stock exchanges are open for trading for only a limited number of hours. No consensus has yet emerged on how to deal with overnight returns when calculating and forecasting realized volatility in markets where trading does not take place 24 hours a day. Based on a recently introduced formal testing procedure, we find that for the S&P 500 index, a realized volatility estimator that optimally incorporates overnight information is more accurate in-sample. In contrast, estimators that do not incorporate overnight information are more accurate for individual stocks. We also show that accounting for overnight returns may affect the conclusions drawn in an out-of-sample horserace of forecasting models. Finally, there is considerably less variation in the selection of the best out-of-sample forecasting model when only the most accurate in-sample RV estimators are considered.     

Return and volatility spillovers across the Western and MENA countries

We use weekly data on returns and range-based volatility over 2005–2017 to examine the degree of interconnectedness in financial markets of eleven MENA and four Western economies using the methodology proposed by Diebold and Yilmaz (2009, 2012, 2014). Our findings suggest (a) similar patterns of dynamic spillovers in both returns and in volatility. Both return and volatility spillover indices experienced significant bursts from 2008 to 2011 coinciding with the U.S. financial crisis. (b) Financial markets of Israel, Saudi Arabia and the UAE are more closely integrated with Westerns markets and may serve as primary channels for transmission of Western shocks to the region. Also, shocks to these three markets have noticeable impacts on other MENA markets. (c) Shocks to the U.S. financial markets play a critical role in return and volatility of MENA markets. (d) These findings are robust to alterations in window size and forecast horizon.     

Extracting a common stochastic trend: Theory with some applications

This paper investigates the statistical properties of estimators of the parameters and unobserved series for state space models with integrated time series. In particular, we derive the full asymptotic results for maximum likelihood estimation using the Kalman filter for a prototypical class of such models—those with a single latent common stochastic trend. Indeed, we establish the consistency and asymptotic mixed normality of the maximum likelihood estimator and show that the conventional method of inference is valid for this class of models. The models we explicitly consider comprise a special–yet useful–class of models that may be employed to extract the common stochastic trend from multiple integrated time series. Such models can be very useful to obtain indices that represent fluctuations of various markets or common latent factors that affect a set of economic and financial variables simultaneously. Moreover, our derivation of the asymptotics of this class makes it clear that the asymptotic Gaussianity and the validity of the conventional inference for the maximum likelihood procedure extends to a larger class of more general state space models involving integrated time series. Finally, we demonstrate the utility of this class of models extracting a common stochastic trend from three sets of time series involving short- and long-term interest rates, stock return volatility and trading volume, and Dow Jones stock prices.     

波动和相关的一致性与差异及传染的阶段特征

本文首次分析了市场波动和相关的一致性与差异,并进而考察金融危机期间传染的阶段特征。研究表明,危机期间,高波动与高相关具有较高的一致性,其他时期则存在差异;几乎不会出现高波动低相关的情形,但并非总是波动性的增加引致了关联水平的上升,关联水平的上升也可能会先于波动性的增加;危机初期,市场需要对复杂的信息进行不断地识别和过滤,以至于波动机制、相关机制都存在较为频繁的转换;次贷危机、欧洲主权债务危机的传染具有系统性特征。     

Do High‐Frequency Data Improve High‐Dimensional Portfolio Allocations?

This paper addresses the debate about the usefulness of high‐frequency (HF) data in large‐scale portfolio allocation. We construct global minimum variance portfolios based on the constituents of the S&P 500. HF‐based covariance matrix predictions are obtained by applying a blocked realized kernel estimator, different smoothing windows, various regularization methods and two forecasting models. We show that HF‐based predictions yield a significantly lower portfolio volatility than methods employing daily returns. Particularly during the 2008 financial crisis, these performance gains hold over longer horizons than previous studies have shown, translating into substantial utility gains for an investor with pronounced risk aversion. Copyright © 2013 John Wiley & Sons, Ltd.     

Great Places to Work®: Resilience in Times of Crisis

We study the resilience of the “100 Best Companies to Work for in America” in times of financial crisis by analyzing their long‐term financial performance. Apart from implementing methods that tackle the statistical problems of stock returns, we use a conditional model to measure financial performance in periods of market growth (bull markets) and market downturn (bear markets). We find that best places to work are indeed resilient in times of crisis since neither their financial performance nor their systematic risk are affected during bear markets: top companies continue to outperform the market during periods of crisis, and the performance of lower‐ranked great workplaces does not deteriorate. Moreover, we find that previous studies were overestimating performance, and only great workplaces on the top half of the rankings exhibit positive excessive returns. © 2015 Wiley Periodicals, Inc.     

Herding behavior,market sentiment and volatility: Will the bubble resume?

This paper aims to investigate herding behavior and its impact on volatility under uncertainty. We apply a cross-sectional absolute deviation approach as well as Quantile Regression methods to capture the herding behavior in daily and monthly frequencies in US markets over several time-periods including the global financial crisis. In a novel attempt we modify the empirical CSAD herding modeling by introducing implied volatility as a measure of agent risk expectations. Our findings indicate that herding tends to be intense under extreme market conditions, as depicted in the upper high quantile range of the conditional distribution of returns. During crisis periods herding is observed at the beginning of the crisis and becomes insignificant towards the end. The US market herding behavior exhibits time-varying dynamic trading patterns that can be attributed e.g., to overconfidence or excessive “flight to quality” features, mostly observed in the aftermath of the global financial crisis. Moreover, implied volatility reveals asymmetric patterns and plays a key role in enforcing irrational behavior.     

Recent developments in financial economics and econometrics: An overview

Research papers in empirical finance and financial econometrics are among the most widely cited, downloaded and viewed articles in the discipline of Finance. The special issue presents several papers by leading scholars in the field on “Recent Developments in Financial Economics and Econometrics”. The breadth of coverage is substantial, and includes original research and comprehensive review papers on theoretical, empirical and numerical topics in Financial Economics and Econometrics by leading researchers in finance, financial economics, financial econometrics and financial statistics. The purpose of this special issue on “Recent Developments in Financial Economics and Econometrics” is to highlight several novel and significant developments in financial economics and financial econometrics, specifically dynamic price integration in the global gold market, a conditional single index model with local covariates for detecting and evaluating active management, whether the Basel Accord has improved risk management during the global financial crisis, the role of banking regulation in an economy under credit risk and liquidity shock, separating information maximum likelihood estimation of the integrated volatility and covariance with micro-market noise, stress testing correlation matrices for risk management, whether bank relationship matters for corporate risk taking, with evidence from listed firms in Taiwan, pricing options on stocks denominated in different currencies, with theory and illustrations, EVT and tail-risk modelling, with evidence from market indices and volatility series, the economics of data using simple model free volatility in a high frequency world, arbitrage-free implied volatility surfaces for options on single stock futures, the non-uniform pricing effect of employee stock options using quantile regression, nonlinear dynamics and recurrence plots for detecting financial crisis, how news sentiment impacts asset volatility, with evidence from long memory and regime-switching approaches, quantitative evaluation of contingent capital and its applications, high quantiles estimation with Quasi-PORT and DPOT, with an application to value-at-risk for financial variables, evaluating inflation targeting based on the distribution of inflation and inflation volatility, the size effects of volatility spillovers for firm performance and exchange rates in tourism, forecasting volatility with the realized range in the presence of noise and non-trading, using CARRX models to study factors affecting the volatilities of Asian equity markets, deciphering the Libor and Euribor spreads during the subprime crisis, information transmission between sovereign debt CDS and other financial factors for Latin America, time-varying mixture GARCH models and asymmetric volatility, and diagnostic checking for non-stationary ARMA models with an application to financial data.     

Volatility contagion: A range-based volatility approach

This article proposes a new approach to evaluate volatility contagion in financial markets. A time-varying logarithmic conditional autoregressive range model with the lognormal distribution (TVLCARR) is proposed to capture the possible smooth transition in the range process. Additionally, a smooth transition copula function is employed to detect the volatility contagion between financial markets. The approach proposed is applied to the stock markets of the G7 countries to investigate the volatility contagion due to the subprime mortgage crisis. Empirical evidence shows that volatility is contagious from the US market to several markets examined.     

Bubbles and Crises: The Role of House Prices and Credit

This paper utilizes quarterly panel data for 20 OECD countries over the period 1975:Q1–2014:Q2 to explore the importance of house prices and credit in affecting the likelihood of a financial crisis. Estimating a set of multivariate logit models, we find that booms in credit to both households and non‐financial enterprises are important to account for when evaluating the stability of the financial system. In addition, we find that global housing market developments have predictive power for domestic financial stability. Finally, econometric measures of bubble‐like behavior in housing and credit markets enter with positive and highly significant coefficients. Specifically, we find that the probability of a crisis increases markedly when bubble‐like behavior in house prices coincides with high household leverage. Copyright © 2016 John Wiley & Sons, Ltd.     

A discrete‐choice model for large heterogeneous panels with interactive fixed effects with an application to the determinants of corporate bond issuance

What is the effect of funding costs on the conditional probability of issuing a corporate bond? We study this question in a novel dataset covering 5610 issuances by US firms over the period from 1990 to 2014. Identification of this effect is complicated because of unobserved, common shocks such as the global financial crisis. To account for these shocks, we extend the common correlated effects estimator to settings where outcomes are discrete. Both the asymptotic properties and the small‐sample behavior of this estimator are documented. We find that for non‐financial firms yields are negatively related to bond issuance but that the effect is larger in the pre‐crisis period.     

Measuring Financial Contagion Using Time‐Aligned Data: The Importance of the Speed of Transmission of Shocks*

This paper presents a new empirical approach to address the problem of trading time differences between markets in studies of financial contagion. In contrast to end‐of‐business‐day data common to most contagion studies, we employ price observations, which are exactly aligned in time to correct for time‐zone and end‐of‐business‐day differences between markets. Additionally, we allow for time lags between price observations in order to test the assumption that the shock is not immediately transmitted from one market to the other. Our analysis of the financial turmoil surrounding the Asian crisis reveals that such corrections have an important bearing on the evidence for contagion, independent of the methodology employed. Using a correlation‐based test, we find more contagion the faster we assume the shock to be transmitted.     

Composite likelihood methods for large Bayesian VARs with stochastic volatility

Adding multivariate stochastic volatility of a flexible form to large vector autoregressions (VARs) involving over 100 variables has proved challenging owing to computational considerations and overparametrization concerns. The existing literature works with either homoskedastic models or smaller models with restrictive forms for the stochastic volatility. In this paper, we develop composite likelihood methods for large VARs with multivariate stochastic volatility. These involve estimating large numbers of parsimonious models and then taking a weighted average across these models. We discuss various schemes for choosing the weights. In our empirical work involving VARs of up to 196 variables, we show that composite likelihood methods forecast much better than the most popular large VAR approach, which is computationally practical in very high dimensions: the homoskedastic VAR with Minnesota prior. We also compare our methods to various popular approaches that allow for stochastic volatility using medium and small VARs involving up to 20 variables. We find our methods to forecast appreciably better than these as well.     

Estimating and testing linkage disequilibrium from sib data

We consider estimation and testing of linkage equilibrium from genotypic data on a random sample of sibs, such as monozygotic and dizygotic twins. We compute the maximum likelihood estimator with an EM‐algorithm and a likelihood ratio statistic that takes the family structure into account. As we are interested in applying this to twin data we also allow observations on single children, so that monozygotic twins can be included. We allow non‐zero recombination fraction between the loci of interest, so that linkage disequilibrium between both linked and unlinked loci can be tested. The EM‐algorithm for computing the maximum likelihood estimator of the haplotype frequencies and the likelihood ratio test‐statistic, are described in detail. It is shown that the usual estimators of haplotype frequencies based on ignoring that the sibs are related are inefficient, and the likelihood ratio test for testing that the loci are in linkage disequilibrium.     

Oil,Gas, or Financial Conditions-Which One Has a Stronger Link with Growth?

Using the models of Diebold-Yilmaz (2012) and Barunik and Krehlik (2018) and monthly U.S. data from January 1992 to May 2019 (329 observations), this study estimates the return and volatility connectedness transmitted from commodity markets (natural gas and crude oil) and the Kansas City financial stress index to macroeconomic indicators (GDP and CPI). As a research target, crude oil has received significant attention. Although natural gas plays an important role in the energy markets as an environment-friendly alternative, it has not been studied extensively. We find the different spread speed of shocks to return and volatility variables through the total spillover index. We focus on both crude oil and natural gas and find that after the bankruptcy of the Lehman Brothers on September 19, 2008, there was a significant jump in the total return spillover from 35.09% to 46.91%, peaking in October 2008. Furthermore, in the frequency domain, we find that the total long-term return spillover index had the highest proportion during the global financial crisis. When the total spillover is concentrated on the high frequencies, it means the system will have an impact mostly in the short term. When it is concentrated on the lower frequencies, it shows that shocks are persistent and works in the long term among the system. It could give some information to the policymakers.