Breaks In Volatility Research Articles

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.

Changes in the global oil market

Changes in the parameters of a recursively identified oil market model are examined through an iterative algorithm that tests for possible breaks in coefficients and variances. The analysis detects breaks in the coefficients of the oil production and price equations, together with volatility shifts in all three equations of the model. Coefficient changes imply an enhanced response of production to aggregate demand shocks after 1980; and that the price response to supply shocks is more persistent from the mid-1990s. All variables evidence changes in the relative contributions of individual shocks to their forecast error variances, with coefficient and volatility breaks in the first half of the 1990s being particularly important in this respect. The results show that analysts of this market should eschew constant parameter models estimated over an extended period.

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.

Sudden changes in crude oil price volatility: an application of extreme value volatility estimator

This study provides a framework based on an extension of the Conditional Autoregressive Range (CARR) model which incorporates the impact of sudden changes in unconditional volatility. The results of the CARR model with and without volatility breaks are compared with the results of the GARCH model with and without volatility breaks to assess whether the forecasting ability of the CARR model is superior when endogenously determined structural breaks in volatility are accounted for. We undertake our analysis on WTI and Brent crude oil and find that the CARR model with volatility breaks effectively captures the dynamics in the crude oil volatility.

Downside risk measure performance in the presence of breaks in volatility

Downside risk measure performance in the presence of breaks in volatility

The Great Moderation in Historical Perspective. Is it that Great?

The Great Moderation (GM) is widely documented in the literature as one of the most important changes in the US business cycle. All the papers that analyze it use post WWII data. In this paper, for the first time we place the GM in a long historical perspective, stretching back a century and a half, which includes secular changes in the economic structure and a substantial reduction of output volatility. We find two robust structural breaks in volatility at the end of WWII and in the mid-eighties, showing that the GM still holds in the longer perspective. Furthermore, we show that GM volatility reduction is only linked to expansion features. We also date the US business cycle in the long run, finding that volatility plays a primary role in the definition of the business cycle, which has important consequences for econometricians and forecasters.

Forecasting crude oil market volatility: A Markov switching multifractal volatility approach

We use a Markov switching multifractal (MSM) volatility model to forecast crude oil return volatility. Not only can the model capture stylized facts of multiscaling, long memory, and structural breaks in volatility, it is also more parsimonious in parameterization, after allowing for hundreds of regimes in the volatility. Our in-sample results suggest that MSM models fit oil return data better than the traditional GARCH-class models. The out-of-sample results show that MSM models generate more accurate volatility forecasts than either popular GARCH-class models or the historical volatility model.

Sudden changes in extreme value volatility estimator: Modeling and forecasting with economic significance analysis

This study provides a framework based on an extension of the conditional autoregressive range (CARR) model which incorporates the impact of sudden changes in the unconditional volatility. This study proposes to use the RS estimator in the CARR model (called henceforth the CARRS model) instead of using the range. The results of the CARRS models with and without volatility breaks are compared with the results of the GARCH models with and without volatility breaks. We also compare the forecasting performance of CARRS models with the forecasting performance of EGARCH, TGARCH and FIGARCH models based on error statistics and regression approach. The findings indicate that the CARRS model with volatility breaks effectively captures the dynamics of volatility and provides better out-of-sample forecasts when compared with GARCH, EGARCH, TGARCH and FIGARCH models. We also devise a trading strategy to examine the economic significance of the proposed framework which indicates that the investor can make substantial gains (approximately 6%–10%) in return for most of cases based on volatility forecasts of CARRS model with volatility breaks. Results based on robustness check are consistent with our main findings.

English

This paper empirically investigates the interdependence between GCC stock market and oil price by considering structural breaks in conditional volatility. The univariate and multivariate GARCH models are extended by including structural breaks which are determined endogenously by using ICSS algorithm proposed by Inclan and Tiao. Empirical results indicate that the inclusion of structural breaks in the model substantially reduces the volatility persistence and the estimated half-life of shocks. Hence, the conditional volatility of oil price and stock market are more affected by their own shocks and volatility when structural breaks are neglected. Likewise, our results are conclusive on conditional dependency between GCC stock market and oil price revealing that the volatility shifts reduce the shocks and volatility spillover effects. For the portfolio management, the empirical results show evidence of sensitivity of the optimal weight and hedge ratios to structural breaks in conditional volatility. Key words: GCC stock market, oil price, dependency, multivariate GARCH, structural breaks, ICSS algorithm, portfolio implications.

Selected Techniques of Detecting Structural Breaks in Financial Volatility

Abstract 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.

Simulation Evidence on Theory‐based and Statistical Identification under Volatility Breaks

AbstractBeside a priori theoretical assumptions on instantaneous or long‐run effects of structural shocks, sign restrictions have become a prominent means for structural vector autoregressive (SVAR) analysis. Moreover, changes in second order moments of systems of time series can be fruitfully exploited for identification purposes in SVARs. By means of Monte Carlo studies, we examine to what degree theory‐based and statistical identification approaches offer an accurate quantification of the true structural relations in a standard model for monetary policy analysis. Subsequently, we discuss how identifying information from theory‐based and statistical approaches can be combined on the basis of a low‐dimensional empirical model of US monetary policy.

Investigating dynamic conditional correlation between crude oil and fuels in non-linear framework: The financial and economic role of structural breaks

To understand the crude oil volatility has been a challenge. The non-linear behavior, the skewed and leptokurtic returns, the presence of structural breaks and the constant political instability in suppliers' countries evidence the necessity of complex models to capture the market volatility. At the same time, crude oil is the raw material for several fuels such as jet fuel, gasoline, diesel and others, having a strong influence over their prices. Thus, this study aims to verify the presence of structural breaks in the volatility series and in the correlations between WTI return and the returns of Gasoline, Kerosene Jet Fuel, Diesel, Heating Oil, Propane and Natural Gas. To reach this objective, we identified which model presents the best fit to estimate the conditional mean between WTI and each fuel and we used a Copula–DCC–GARCH model to estimate the conditional volatility avoiding the frequently unrealistic presumptions of normality. Our main results indicate the necessity of a different model for each analyzed pair and the presence of at least one structural break in the conditional volatility and in the correlation between WTI and each fuel, usually preceded by a structural break in WTI return series.

Long memory properties on Tunisian sectorial stock market index volatilities: evidence from FIEGARCH model

We investigate the impact of Tunisian revolution on two fundamental features of stock index returns, namely, the long memory, the asymmetric and leverage effects. Firstly, we apply the ICSS procedure to identify the eventual existence of structural breaks in volatility and secondly, we use the fractionally integrated exponential GARCH (FIEGARCH) which estimates a direct shock-persistence as well as a shock asymmetric volatility measurement. Applied to the daily returns relevant to nine sectorial stock indices and to the benchmark Tunisian index, our findings suggest that shock impact on construction, raw materials and financial service index volatility returns proves to be permanent during the revolution period, while its persistence in the other indices is found to be transitory. The leverage effect is negative, but our achieved results revealed a lower leverage effect in all indices. These results seem to be very important since the Tunisian revolution has a very important effect on the Tunisian stock exchange.

Spillover effect of international crude oil market on tanker market

The tanker shipping market has been regarded as a key extension of the global oil market and its uncertainty is related to the volatility of oil market. Therefore, for improving transportation risk management of oil shipping companies and oil imports countries, it is of significance to investigate the spillover effects across the oil market and the tanker market. Taking the volatility breaks into account, this paper establishes a VAR-BEKK-GARCH model for both the entire sample period from 1 June 2006 to 1 April 2014 and two sub-period samples. The empirical results provide evidence that the volatility of Brent market has more significant impacts on the tanker market than the WTI market in general. Moreover, the influence of oil markets on the tanker market during sub-sample 1 period (1 June 2006-23 April 2009) is stronger than that in sub-sample 2 (24 April 2009-1 April 2014).

Examining Export Volatility, Structural Breaks in Price Volatility and Linkages between Domestic and Export Prices of Onion in India

Examining Export Volatility, Structural Breaks in Price Volatility and Linkages between Domestic and Export Prices of Onion in India

How Fat‐Tailed is US Output Growth?

AbstractSeveral studies have recently rejected the common hypothesis that aggregate output is normally distributed. The present paper reconsiders this issue for US output growth. To this end, it focusses on the shape parameter b of the exponential power distribution (EPD), the two polar values of which constitute the normal distribution and the Laplace distribution with its fatter tails, respectively. The paper first warns against premature conclusions that neglect a structural break in output volatility. On the basis of a battery of Monte Carlo experiments, it is then found out that the results strongly depend on which subperiod is considered, the Great Inflation (GI) or the Great Moderation (GM) period, and which data are referred to, the growth rates of GDP, firm sector output, and quarterly or monthly industrial production (IP). Here, only quarterly IP can be said to exhibit fat tails in GI as well as GM; other evidence is mixed.

Measuring the Volatility of Wheat Futures Prices on the LIFFE

AbstractAgricultural prices rose dramatically in 2007 and have subsequently fluctuated at high levels. This paper estimates the volatility of daily wheat futures prices on the Euronext/London International Financial Futures and Options Exchange for 1996–2012 using an exponential generalised autoregressive conditional heteroscedasticity model with a constant (price) elasticity of variance (CEV) and a broken trend. Results show that volatility is highly persistent; there is a structural break in volatility in June 2007 when volatility rose by 10%; subsequently, the wheat futures price has become more volatile; and the CEV is 0.04.

Have winter fuel payments reduced excess winter mortality in England and Wales?

The historical series of excess winter mortality (EWM) in England and Wales presents a negative trend. Winter fuel payments (WFPs) are the most important benefits for people aged 65 or over directly related to Winter Mortality in the UK. This study presents a time series analysis of the direct effect of WFPs on EWM in England and Wales. We find a significant structural break in trend and volatility in the EWM series in England and Wales in 1999-2000. After controlling for a number of covariates, an ARIMA-X model finds that WFPs can account for almost half of the reduction in EWM in England and Wales since 1999/2000. Almost half of the reduction in EWM since 1999/2000 is attributable to WFPs.

Price dynamics in agricultural commodity markets: a comparison of European and US markets

This study tests for the presence of linear and nonlinear dependences in returns and volatility for six agricultural futures daily prices series, three traded on MATIF Euronext (wheat, corn, and rapeseed), and three traded on Chicago Board of Trade (red winter wheat, corn, and soybean) over the period 2000–2013. Whereas price dynamics on the Chicago Board of Trade (CBOT) market seem to fit classical GARCH modelling, time series dependences in the MATIF market cannot be fully described by short-term dependences alone. According to various criteria, the results suggest the presence of long memories for the European market. However, the low fractional order of ARFIMA-type or FiGARCH-type models can explain only some, but not all, of the observed nonlinearity. Nonlinearity could be influenced by the regime shift. By taking volatility breaks in the series into account, it is possible to gain a better understanding of the serial dependencies.

Factor Model Forecasting: A Bayesian Model Averaging (BMA) Perspective

We use Bayesian factor regression models to construct a financial conditions index (FCI) for the U.S. Within this context we develop Bayesian model averaging methods that allow the data to select which variables should be included in the FCI or not. We also examine the importance of different sources of instability in the factors, such as stochastic volatility and structural breaks. Our results indicate that ignoring structural breaks in the loadings can be quite costly in terms of the forecasting performance of the FCI. Additionally, Bayesian model averaging can improve in specific cases the performance of the FCI, by means of discarding irrelevant financial variables during the estimation of the factor.