Breaks In Volatility Research Articles

Volatility transmission between upstream and midstream energy sectors

This research studies volatility dynamics of the returns to upstream (exploration and production) and midstream (pipeline and storage) energy (oil and gas) sectors over time and across sectors. We find significant structural breaks in volatility of upstream sector returns possibly triggered by major events, while no breaks are detected in the midstream sector. These results indicate the midstream sector is fairly insulated from major events relative to the upstream sector. Results based on a bivariate generalized ARCH model, which controls for breaks, indicate that the conditional variance of the upstream sector is significantly more affected by midstream volatility than vice versa. This finding could be due to the fundamental difference in functions performed by the midstream and upstream sector firms. These patterns in volatility dynamics have practical significance for financial market participants.

Detecting turning points of stock markets in China and the United States

ABSTRACT Choosing the appropriate method to identify turning points is critical for investors and policymakers. We propose a novel hybrid model combining dual long memory with structural breaks in the mean to identify the turning points of the stock market in China and the United States. The results show that models accounting for long memory and structural break perform well in the turning point detection. Notably, the proposed hybrid model generates superior in-sample turning point matching and out-sample forecasts for SSECI over those obtained from the competing ARFIMA models. In contrast, DJIA is better explained by the dual memory model with structural breaks in volatility. Finally, our analysis extends to encompass FTSE, HSI, IBOVESPA, and SENSEX30. The results demonstrate the superior performance of the hybrid model in markets exhibiting fractal characteristics. Such heterogeneity indicates differences in investor risk preference across stock markets and offers elaborate international evidence to the adaptive market hypothesis.

Policy uncertainty and stock market volatility revisited: The predictive role of signal quality

AbstractThis paper provides novel insight into the growing literature on the policy uncertainty‐stock market volatility nexus by examining the out‐of‐sample predictive ability of the quality of political signals over stock market volatility at various forecast horizons. Specifically, we examine whether or not accounting for the signal quality in forecasting models within a mixed frequency framework can improve forecast performance and help achieve economic gains for investors. Both in‐ and out‐of‐sample tests, based on a GARCH‐MIDAS framework, show that the quality of the policy signal matters regarding the predictive role of policy uncertainty over subsequent stock market volatility. While high economic policy uncertainty (EPU) predicts high volatility, particularly when the signal quality is high, the positive relationship between EPU and volatility breaks down when the signal quality is low. The improved out‐of‐sample volatility forecasts obtained from the models that account for the quality of policy signals also help typical mean–variance investors achieve improved economic outcomes captured by higher certainty equivalent returns and Sharpe ratios. Although our results indicate clear distinctions between the US and UK stock markets in terms of how market participants process policy signals, they highlight the role of the quality of policy signals as a driver of volatility forecasts with significant economic implications.

Short selling, divergence of opinion and volatility in the corporate bond market

We investigate the impact of short selling activity on price volatility in the corporate bond market. We find that bond short selling activity is positively related to volatility, trading activity, and the volume-volatility relation. During the Global Financial Crisis, when investors’ expectations tend to be more homogenous, the positive relation between short selling activity and price volatility breaks down. We further show that bond short selling is not a substitute for equity short selling and put option trading. Overall, our study highlights the importance of bond short selling as a platform to express investors’ differences of opinion.

Time-series dynamics of Baltic trade flows: Structural breaks, regime shifts, and exchange-rate volatility

Aim/purpose – In the decades since their reintegration with the West, the small open economies of Estonia, Latvia, and Lithuania have seen their trade flows grow substan- tially. While the mix of trade partners has evolved over time, the region has been affect- ed by various political and economic shocks. This study examines the bilateral trade balances between the Baltic countries and nine partners to investigate whether there have been structural breaks due to political or economic events. Because these events may have been “priced into” exchange rates or increased these rates’ volatility, connections between these variables and trade balances are also considered. Design/methodology/approach – Monthly data beginning in 1994 are taken from the International Monetary Fund’s Direction of Trade Statistics [DOTS]. Trade partners include the Nordic countries of Finland, Sweden, and Norway, as well as Poland, Russia, and the United States and country groupings such as the CIS, Advanced Economies, and the World. Ratios of the export and import values are used to create bilateral trade bal- ances. The Bai–Perron (1998) structural break test is then used to identify “break points” that can classify time periods into regimes. Baltic nominal and real effective exchange rates, both in log changes and as a GARCH-based volatility measure, show whether regimes correspond to competitiveness or risk. Correlations are calculated to show links between bilateral trade balances and real exchange rates. Findings – Each trade balance has at least one structural break; many have more. In fewer than half of the cases do these correspond to specific events such as EU accession or the Global Financial Crisis. Trade with Russia has decreased, particularly for Estonia and Latvia. But many partners with historical ties, such as Estonia-Finland, Latvia- -Sweden, and Lithuania-Poland have more breaks than do other partners (such as Estonia- -Poland). Structural breaks in real exchange-rate returns and volatility do not match those of trade balances, and correlations between returns and trade balances are low. Research implications/limitations – These findings open the door to future research on the macroeconomic and cultural/historical factors behind these trade linkages and any changes in regimes. However, no structural determinants have yet been estimated. Originality/value/contribution – This study isolates changes in trade regimes, which can be further explained by specific events or particular dates. It also shows that variance has changed as well as the mean, but this differs by country and by the partner. Keywords: Trade flows, Baltics, time series, structural breaks. JEL Classification: F14, F4, C1.

Volatility spillover among sector equity returns under structural breaks

Recent evidence suggests that ignoring structural breaks in volatility in financial asset returns can result in overestimation of volatility spillover among markets. This paper examines volatility spillover among major US equity sectors (i.e. Financial, Technology, Energy, Health, Consumer and Industrial) with bivariate GARCH models utilizing daily data from April 2006 to March 2021 after adjusting for volatility breaks. I find significantly less volatility spillover between sector returns after adjusting for detected volatility breaks into a bivariate GARCH model. I also show that after adding volatility breaks into a model the estimated hedge ratios change significantly and show considerably less variability over time, which can result in substantial savings in portfolio rebalancing costs.

Forecasting the Crude Oil Prices Volatility With Stochastic Volatility Models

In this article, the stochastic volatility model is introduced to forecast crude oil volatility by using data from the West Texas Intermediate (WTI) and Brent markets. Not only that the model can capture stylized facts of multiskilling, extended memory, and structural breaks in volatility, it is also more frugal in parameterizations. The Euler–Maruyama scheme was applied to approximate the Heston model. On the contrary, the root mean square error (RMSE) and the mean average error (MAE) were used to approximate the generalized autoregressive conditional heteroskedasticity (GARCH)–type models (symmetric and asymmetric). Based on the approximation results obtained, the study established that the stochastic volatility model fits oil return data better than the traditional GARCH-class models.

Income volatility and portfolio choices

Based on administrative data from Statistics Norway, we find economically significant shifts in households’ financial portfolios around individual structural breaks in labor-income volatility. According to our estimates, when income risk doubles, households reduce their risky share of financial assets by 5 percentage points, thus tempering their overall risk exposure. We show that our estimated risky share response is consistent with a standard portfolio choice model augmented with idiosyncratic, time-varying income volatility.

COVID-19 and instability of stock market performance: evidence from the U.S.

The effect of COVID-19 on stock market performance has important implications for both financial theory and practice. This paper examines the relationship between COVID-19 and the instability of both stock return predictability and price volatility in the U.S over the period January 1st, 2019 to June 30th, 2020 by using the methodologies of Bai and Perron (Econometrica 66:47–78, 1998. https://doi.org/10.2307/2998540; J Appl Econo 18:1–22, 2003. https://doi.org/10.1002/jae.659), Elliot and Muller (Optimal testing general breaking processes in linear time series models. University of California at San Diego Economic Working Paper, 2004), and Xu (J Econ 173:126–142, 2013. https://doi.org/10.1016/j.jeconom.2012.11.001). The results highlight a single break in return predictability and price volatility of both S&P 500 and DJIA. The timing of the break is consistent with the COVID-19 outbreak, or more specifically the stock selling-offs by the U.S. senate committee members before COVID-19 crashed the market. Furthermore, return predictability and price volatility significantly increased following the derived break. The findings suggest that the pandemic crisis was associated with market inefficiency, creating profitable opportunities for traders and speculators. Furthermore, it also induced income and wealth inequality between market participants with plenty of liquidity at hand and those short of funds.

Soft information in insurer lending and reinsurance under capital regulation

ABSTRACT The paper develops a capped call option framework, explicitly integrating the borrowing-firm credit risk. Moreover, modelling the structural breaks in assets and volatility captures soft information about the insurer’s borrowing firm. We show that favourable soft information increases the optimal guaranteed rate and thus enhances policyholder protection. The negative effect of the reinsurance on the optimal guaranteed rate is more significant when obtaining negative soft information than when getting favourable soft information. The positive impact of stringent capital regulation on the optimal guaranteed rate in the negative soft information is less significant than that in the favourable soft information. Stringent capital regulation increases policyholder protection and the market-to-book equity value when the strict capital regulation is high, thereby contributing to insurance stability.

COVID-19 outbreak, government capital injections, and shadow banking efficiency

ABSTRACT This paper investigates the effects of the novel coronavirus (COVID-19) outbreak and government capital injections on the bank’s optimal interest margin and the efficiency gain/loss from the shadow banking operations. The down-and-out call option approach is adapted to model the structural break in volatility to capture the COVID-19 outbreak. Results show that the COVID-19 outbreak reduces the optimal bank interest margin, government capital injections enhance the margin, and both the outbreak and capital injections harm the efficiency gain from shadow banking. COVID-19, as such, makes the bank more prone to risk-taking, thereby adversely affecting banking stability.

How connected is the carbon market to energy and financial markets? A systematic analysis of spillovers and dynamics

Carbon allowances are a new class of financial instrument which aim to assist in limiting the extent and impact of global warming and climate change. The feedback mechanism in the “Carbon-Energy-Finance” system makes the information connectedness dynamics more complex since we add equity, bond and non-energy commodity assets into the system. Using modified error variance decomposition and network diagrams, we quantify and systematically analyze how the European carbon market connects with information from a wide range of other markets. Our results indicate: (i) the nature of information spillover changes over time, with system-wide return connectedness being higher and more variable than the volatility interdependence; (ii) both the oil and carbon markets closely connect with equity and non-energy commodity markets rather than bond markets; (iii) we identify three structural breaks in carbon volatility and their implication for carbon-finance linkages; (iv) financial risk-type macroeconomic factors make greater contributions to system-wide connectedness than commodity factors. These findings have economic implications for investors, portfolio managers and policymakers. • Directional and dynamic connectedness in“Carbon-Energy-Finance”system is explored. • Network connectedness exhibits substantial time variation. • Structural breaks in Carbon return impact how it connects to the system. • System-wide spillovers are determined by financial risk-type macroeconomic factors. • EUA is a better diversifier than oil given its modest linkage with financial asset.

Forecasting risk in the US Dollar exchange rate under volatility shifts

Recent evidence suggests that volatility shifts (i.e. structural breaks in volatility) in returns increases kurtosis which significantly contributes to the observed non-normality in market returns. In this paper, we endogenously detect significant shifts in the volatility of US Dollar exchange rate and incorporate this information to estimate Value-at-Risk (VaR) to forecast large declines in the US Dollar exchange rate. Our out-of-sample performance results indicate that a GARCH model with volatility shifts produces the most accurate VaR forecast relative to several benchmark methods. Our contribution is important as changes in US Dollar exchange rate have a substantial impact on the global economy and financial markets.

Derivative trading and structural breaks in volatility in India: an ICSS approach

Researchers argue that ignoring the structural breaks in the time-series variance can cause significant upward biases in the degree of persistence in estimated GARCH models. Against this backdrop, the present study empirically examines the effect of stock futures on the underlying stock’s volatility in India by incorporating the structural breaks with the help of ICSS test and AR (1)-GARCH (1, 1) model for 30 most liquid and actively traded underlying stocks and their associated futures contracts. The study period ranges from the 1st January 2000 or the listing date of the particular stock (whichever is prior) till 31st March 2019. The study contributes to the on-going debate regarding the effect of derivatives on the underlying stock market’s volatility in two ways. Firstly, by taking into consideration the breaks in the volatility and, secondly, studying the effect of single stock futures will allow us to evaluate company-specific response to futures trading directly. The study offers a mixed outcome for the stocks under consideration. However, there is evidence of a decline in unconditional volatility for the majority of the stocks. The overall findings indicate that trading in stock futures may not have any detrimental effect on the underlying stock’s volatility.

COVID-19, insurer board utility, and capital regulation

This paper develops a down-and-out call option model by introducing a structural break in volatility to capture the coronavirus (COVID-19) outbreak. The life insurer's equity and its board's utility are evaluated at the optimal guaranteed rate in the equity maximization. Results suggest that the seriousness degree of the COVID-19 outbreak and capital regulation enhance the optimal guaranteed rate and the board's utility. Increased the board's utility by increasing liabilities costs insurer profitability. Conflicts of incentives can arise during the COVID-19 outbreak.

The Price-Volume Relationship of the Shanghai Stock Index: Structural Change and the Threshold Effect of Volatility

The price–volume relationship of stocks can be impacted substantially by structural changes and market volatility. In this paper, we analyze China’s stock market behavior and subsequent price–volume equation, with emphasis on two periods of market volatility and structural changes during 2007–2008 and 2015–2016. To account for the impacts of unknown volatility and time breaks, we embed the price–volume relationship into a vector autoregression (VAR) framework with structural breaks and volatility thresholds. Our results indicate that significant time-breaking effects exist and that the high-low volatility effects are substantial. Finally, in its entirety, we identify only a linear causal relationship from price to volume.

Income Volatility and Portfolio Choices

Based on administrative data from Statistics Norway, we find economically significant shifts in households' financial portfolios around structural breaks in income volatility. When the standard deviation of labor-income growth doubles, the share of risky assets decreases by 4 percentage points. We ask whether this estimated marginal effect is consistent with a standard model of portfolio choice with idiosyncratic volatility shocks. The standard model generates a much more aggressive portfolio response than we see in the data. We show that Bayesian learning about the underlying volatility regime can reconcile the gap between the model and the data.

Detection of Breakpoints in Volatility

In this article, we test for the presence of structural breaks in volatility by two alternative approaches: the Iterative Cumulative Sum of Squares (ICSS) algorithm and wavelet analysis. Specifically, we look at the effect of the outbreak of the Asian crisis and the terrorist attacks of September 11, 2001 on Emerging Asia. Europe. Latin America and North America's stock markets. In addition, we focus on the behavior of interest rates in Chile after the Central Bank switched its monetary policy interest rate from an inflation-indexed to a nominal target in August 2001. Our estimation results show that the number of shifts detected by the two methods is substantially reduced when filtering out the data for both conditional heteroskedasticity and serial correlation.

Value-at-Risk in the Presence of Structural Breaks Using Unbiased Extreme Value Volatility Estimator

We provide a framework based on the unbiased extreme value volatility estimator to predict long and short position value-at-risk (VaR). The given framework incorporates the impact of asymmetry, structural breaks and fat tails in volatility. We generate forecasts of long and short position VaR for the cases when future structural breaks are known as well as unknown. We evaluate its VaR forecasting performance using various backtesting approaches for both long and short positions and compare the results with that from return based models. Our findings indicate that incorporating the impact of structural breaks in volatility indeed improves the accuracy of VaR forecasts of the proposed framework.

On the pernicious effects of oil price uncertainty on US real economic activities

The last five decades have witnessed dramatic changes in crude oil price dynamics. We identify the influence of extreme oil shocks and changing oil price uncertainty dynamics associated with economic and political events. Neglecting these features of the data can lead to model misspecification that gives rise to: firstly, an explosive volatility process for oil price uncertainty, and secondly, erroneous output growth dynamic responses to oil shocks. Unlike past studies, our results show that the sharp increase in oil price uncertainty after mid-1985 has a pernicious effect on output growth. There is evidence that output growth responds symmetrically (asymmetrically) to positive and negative shocks in the period when oil price uncertainty is lower (higher) and more (less) persistent before (after) mid-1985. These results highlight the importance of accounting for outliers and volatility breaks in oil price and output growth and the need to better understand the response of economic activity to oil shocks in the presence of oil price uncertainty. Our results remain qualitatively unchanged with the use of real oil price.