Structural 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.

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.

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.

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.

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.

Do structural breaks in volatility cause spurious volatility transmission?

Abstract We show through extensive Monte Carlo simulations that structural breaks in volatility (volatility shifts) across two independently generated return series cause spurious volatility transmission when estimated with popular bivariate GARCH models. However, using a dummy variable for the induced volatility shift virtually eliminates this bias. We also show that structural breaks in volatility have a substantial impact on the estimated hedge ratios. We confirm our simulation findings using the US stock market data.

The decline in volatility in the US economy. A historical perspective

Abstract In this paper, we analyse the volatility of US GDP growth using quarterly series starting in 1875. We find structural breaks in volatility at the end of World War II and at the beginning of the Great Moderation period. We show that the Great Moderation volatility reduction is only linked to changes in expansions, whereas that after World War II is due to changes in both expansions and recessions. We also propose several methodologies to date the US business cycle in this long period. We find that taking volatility into account improves the characterization of the business cycle.

Forecast Combinations for Structural Breaks in Volatility: Evidence from BRICS Countries

The aim of this paper is to investigate the relevance of structural breaks for forecasting the volatility of daily returns on BRICS countries (Brazil, Russia, India, China and South Africa). The data set used in the analysis is the Morgan Stanley Capital International MSCI daily returns and covers the period from 19 July 1999 to 16 July 2015. To identify structural breaks in the unconditional variance, a binary segmentation algorithm with a test, which considers both the fourth order moment of the process and persistence in the variance, has been implemented. Some forecast combinations that account for the identified structural breaks have been introduced and their performance has been evaluated and compared by using the Model Confidence Set (MCS). The results give significant evidence of the relevance of the structural breaks. In particular, in the regimes identified by the structural breaks, a substantial change in the unconditional variance is quite evident. In forecasting volatility, the combination that averages forecasts obtained using different rolling estimation windows outperforms all the other combinations

Estimating downside risk in stock returns under structural breaks

We show with simulations that inducing structural breaks in the volatility of returns causes non-normality by significantly increasing kurtosis. We endogenously detect significant structural breaks in the volatility of US stock returns and incorporate this information to estimate Value-at-Risk (VaR) to measure the downside risk. Out-of-sample performance results indicate that our proposed model, which incorporates both time varying volatility and structural breaks in volatility, produces more accurate VaR forecasts than several benchmark methods. We highlight the economic importance of our results by calculating the daily capital charges using the Basel Accords.

Volatility Prediction: A Study with Structural Breaks

We incorporate the impact of structural breaks in the unbiased unconditional volatility as proposed by Kumar and Maheswaran with a conditional autoregressive range (CARR) model. The findings of the proposed framework are compared with the findings based on the volatility forecasts of the GARCH model with and without structural breaks in volatility. Our findings based on the analysis on S&P 500, FTSE 100, SZSE Composite and FBMKLCI indices indicate that the proposed framework effectively captures the dynamics of conditional volatility and provides better out-of-sample forecasts relative to GARCH models with and without structural breaks in volatility.

Structural breaks and volatility forecasting in the copper futures market

This paper examines whether structural breaks contain incremental information for forecasting the volatility of copper futures. Considering structural breaks in volatility, we develop four heterogeneous autoregressive (HAR) models based on classical or latest HAR‐type models. Subsequently, we apply these models to forecast volatility in the copper futures market. The empirical results reveal that our models exhibit better in‐sample and out‐of‐sample performances than classical or latest HAR‐type models. This suggests that structural breaks contain incremental prediction information for the volatility of copper futures. More importantly, we argue that considering structural breaks can improve the performances of most of existing HAR‐type models.Highlights There are many structural break points in return volatility of the copper futures. We propose 12 new heterogeneous autoregressive models. Our models outperform the existing heterogeneous autoregressive models. Structural breaks contain additional ex ante information for volatility forecasting. The ex ante information is obvious in forecasting mid‐ and long‐term volatilities.

Interaction between oil and US dollar exchange rate: nonlinear causality, time-varying influence and structural breaks in volatility

ABSTRACTThis article examines the nonlinear Granger causality and time-varying influence between crude oil prices and the US dollar (USD) exchange rate using the Hiemstra and Jones (HP) test, the Diks and Panchenko (DP) test and the time-varying parameter structural vector autoregression model. By applying the iterated cumulative sums of squares (ICSS) algorithm and the DCC-GARCH model, the effects of structural breaks in volatility of the two markets are also investigated. The empirical analysis indicates that, first, crude oil prices are the nonlinear Granger-cause of the USD exchange rate, but not vice versa. Second, the USD exchange rate exerts a stronger and more stable negative influence on crude oil prices in the short term, and the influence gradually weakens after 2012. Finally, ignoring structural breaks can increase the negative volatility correlation between the oil and USD exchange rate markets, which is particularly remarkable during the financial crisis.

Modelling asymmetric volatility in oil prices under structural breaks

This paper shows that accounting for endogenously determined structural breaks within an asymmetric GARCH model reduces volatility persistence in oil prices. More importantly, we find that both good and bad news have significantly more impact on volatility if structural breaks are accounted for in a model. Thus, previous studies have significantly underestimated the impact of news on volatility as they have inadvertently ignored these structural breaks in volatility. Our empirical results suggest that it is best to include both asymmetric effects and structural breaks in a GARCH model to accurately estimate oil price volatility dynamics. Our results have important practical implications not only for option valuation and hedging decisions but also have major consequences for broader financial markets, the energy industry, and the overall economy.

Selected Techniques of Detecting Structural Breaks in Financial Volatility

We investigate several promising algorithms, proposed in literature, devised to detect sudden changes (structural breaks) in the volatility of financial time series. Comparative study of three techniques: ICSS, NPCPM and Cheng’s algorithm is carried out via numerical simulation in the case of simulated T-GARCH models and two real series, namely German and US stock indices. Simulations show that the NPCPM algorithm is superior to ICSS because is not over-sensitive either to heavy tails of market returns or to their serial dependence. Some signals generated by ICSS are falsely classified as structural breaks in volatility, while Cheng’s technique works well only when a single break occurs.

Structural Breaks in Volatility: The Case of Chinese Stock Returns

This article tests for periodic breaks in the unconditional variance of stock return data on two Chinese stock return market indexes. Using the modified ICSS algorithm, we observe three breaks in the Shanghai Stock Exchange composite index and Shenzhen Stock Exchange composite index series. We document the policy changes related to the Chinese stock market and explain that the Chinese stock market is largely influenced by government policy.