Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models

This paper investigates whether structural breaks and long memory are relevant features in modeling and forecasting the conditional volatility of oil spot and futures prices using three GARCH-type models, i.e., linear GARCH, GARCH with structural breaks and FIGARCH. By relying on a modified version of Inclan and Tiao (1994)'s iterated cumulative sum of squares (ICSS) algorithm, our results can be summarized as follows. First, we provide evidence of parameter instability in five out of twelve GARCH-based conditional volatility processes for energy prices. Second, long memory is effectively present in all the series considered and a FIGARCH model seems to better fit the data, but the degree of volatility persistence diminishes significantly after adjusting for structural breaks. Finally, the out-of-sample analysis shows that forecasting models accommodating for structural break characteristics of the data often outperform the commonly used short-memory linear volatility models. It is however worth noting that the long memory evidence found in the in-sample period is not strongly supported by the out-of-sample forecasting exercise.

How Volatile is ENSO?

The El Niños Southern Oscillations (ENSO) is a periodical phenomenon of climatic interannual variability which could be measured through either the Southern Oscillation Index (SOI) or the Sea Surface Temperature (SST) Index. The main purpose of this paper is to analyze these two indexes in order to capture ENSO volatility. The empirical results show that both the ARMA(1,1)-GARCH(1,1) and ARMA(3,2)-GJR(1,1) models are suitable for modelling ENSO volatility. Moreover, 1998 is a turning point for the volatility of SOI, and the ENSO volatility has became stronger since 1998 which indicates that the ENSO strength has increased.GARCH;Volatility;EGARCH;GJR;ENSO;SOI;SOT

Real effects of inflation uncertainty in the US

We empirically investigate the effects of inflation uncertainty on output growth for the US using both monthly and quarterly data over 1985-2009. Employing a Markov regime switching approach to model output dynamics, we show that inflation uncertainty obtained from a Markov regime switching GARCH model exerts a negative and regime dependant impact on output growth. In particular, we show that the negative impact of inflation uncertainty on output growth is almost 4.5 times higher during the low growth regime than that during the high growth regime. We verify the robustness of our findings using quarterly data

Modeling the Effect of Oil Price on Global Fertilizer Prices

The main purpose of this paper is to evaluate the effect of crude oil price on global fertilizer prices in both the mean and volatility. The endogenous structural breakpoint unit root test, the autoregressive distributed lag (ARDL) model, and alternative volatility models, including the generalized autoregressive conditional heteroskedasticity (GARCH) model, Exponential GARCH (EGARCH) model, and GJR model, are used to investigate the relationship between crude oil price and six global fertilizer prices. Weekly data for 2003-2008 for the seven price series are analyzed. The empirical results from ARDL show that most fertilizer prices are significantly affected by the crude oil price, which explains why global fertilizer prices reached a peak in 2008. We also find that that the volatility of global fertilizer prices and crude oil price from March to December 2008 are higher than in other periods, and that the peak crude oil price caused greater volatility in the crude oil price and global fertilizer prices. As volatility invokes financial risk, the relationship between oil price and global fertilizer prices and their associated volatility is important for public policy relating to the development of optimal energy use, global agricultural production, and financial integration.Volatility; Global fertilizer price; Crude oil price; Non-renewable fertilizers; Structural breakpoint unit root test

How Volatile is ENSO?

The El Niños Southern Oscillations (ENSO) is a periodical phenomenon of climatic interannual variability, which could be measured through either the Southern Oscillation Index (SOI) or the Sea Surface Temperature (SST) Index. The main purpose of this paper is to analyze these two indexes in order to capture the volatility inherent in ENSO. The empirical results show that both the ARMA(1,1)-GARCH(1,1) and ARMA(3,2)-GJR(1,1) models are suitable for modelling ENSO volatility accurately. The empirical results show that 1998 is a turning point, which indicates that the ENSO strength has increased since 1998. Moreover, the increasing ENSO strength is due to the increase in greenhouse gas emissions. The ENSO strengths for SST are predicted for the year 2030 to increase from 29.62% to 81.5% if global CO2 emissions increase by 40% to 110%, respectively. This indicates that we will be faced with an even stronger El Nino or La Nina in the future if global greenhouse gas emissions continue to increase unabated.ENSO; SOI; SOT; Greenhouse Gas Emissions; Volatility; GARCH; GJR; EGARCH

How Volatile is ENSO?

The El Niños Southern Oscillations (ENSO) is a periodical phenomenon of climatic interannual variability, which could be measured through either the Southern Oscillation Index (SOI) or the Sea Surface Temperature (SST) Index. The main purpose of this paper is to analyze these two indexes in order to capture the volatility inherent in ENSO. The empirical results show that both the ARMA(1,1)-GARCH(1,1) and ARMA(3,2)-GJR(1,1) models are suitable for modelling ENSO volatility accurately. The empirical results show that 1998 is a turning point, which indicates that the ENSO strength has increased since 1998. Moreover, the increasing ENSO strength is due to the increase in greenhouse gas emissions. The ENSO strengths for SST are predicted for the year 2030 to increase from 29.62% to 81.5% if global CO2 emissions increase by 40% to 110%, respectively. This indicates that we will be faced with an even stronger El Nino or La Nina in the future if global greenhouse gas emissions continue to increase unabated.ENSO, SOI, SOT, Greenhouse Gas Emissions, Volatility, GARCH, GJR, EGARCH.

Modeling the Effect of Oil Price on Global Fertilizer Prices

The main purpose of this paper is to evaluate the effect of crude oil price on global fertilizer prices in both the mean and volatility. The endogenous structural breakpoint unit root test, the autoregressive distributed lag (ARDL) model, and alternative volatility models, including the generalized autoregressive conditional heteroskedasticity (GARCH) model, Exponential GARCH (EGARCH) model, and GJR model, are used to investigate the relationship between crude oil price and six global fertilizer prices. Weekly data for 2003-2008 for the seven price series are analyzed. The empirical results from ARDL show that most fertilizer prices are significantly affected by the crude oil price, which explains why global fertilizer prices reached a peak in 2008. We also find that that the volatility of global fertilizer prices and crude oil price from March to December 2008 are higher than in other periods, and that the peak crude oil price caused greater volatility in the crude oil price and global fertilizer prices. As volatility invokes financial risk, the relationship between oil price and global fertilizer prices and their associated volatility is important for public policy relating to the development of optimal energy use, global agricultural production, and financial integration.volatility;crude oil price;global fertilizer price;non-renewable fertilizers;structural breakpoint unit root test

Modelling stock volatilities during financial crises: A time varying coefficient approach

We examine how the most prevalent stochastic properties of key financial time series have been affected during the recent financial crises. In particular we focus on changes associated with the remarkable economic events of the last two decades in the volatility dynamics, including the underlying volatility persistence and volatility spillover structure. Using daily data from several key stock market indices, the results of our bivariate GARCH models show the existence of time varying correlations as well as time varying shock and volatility spillovers between the returns of FTSE and DAX, and those of NIKKEI and Hang Seng, which became more prominent during the recent financial crisis. Our theoretical considerations on the time varying modelwhich provides the platformupon which we integrate our multifaceted empirical approaches are also of independent interest. In particular, we provide the general solution for time varying asymmetric GARCH specifications, which is a long standing research topic. This enables us to characterize these models by deriving, first, their multistep ahead predictors, second, the first two time varying unconditional moments, and third, their covariance structure.Open Access funded by European Research Council under a Creative Commons license

Regulatory Change and Micro Structure Effects in SPI Futures

In this article we investigate and test for structural change in conditional volatility and micro structure effects in the Australian Share Price Index futures contract. The modelling is conducted around the periods following the introduction of an automated screen-based trading system and alterations to the trading day. Multiple point Switching GARCH models are employed following a detailed examination of conditional volatility, volume and maturity features. The data is sampled at 5, 15 and 30-minute intervals from transaction records supplied by the Sydney Futures Exchange. Micro-structure features that are found to be important in the preliminary analysis are incorporated in the formal models. Failure to test for and then account for any of these market features would imply that tests for structural changes are mis-specified. There is significant evidence of structural changes in both the persistence of volatility shocks and simultaneous volume effects following the change to screen trading in this futures market.Regulatory intervention, Structural Breaks, Micro Structure Effects.

Detecting Multiple Breaks in Financial Market Volatility Dynamics

The paper evaluates the performance of several recently proposed tests for structural breaks in conditional variance dynamics of asset returns. The tests apply to the class of ARCH and SV type processes as well as data-driven volatility estimators using high-frequency data. In addition to testing for the presence of breaks, the statistics identify the number and location of multiple breaks. We study the size and power of the new test for detecting breaks in the second conditional variance under various realistic univariate heteroskedastic models, change-point hypotheses and sampling schemes. The paper concludes with an empirical analysis using data from the stock and FX markets for which we find multiple breaks associated with the Asian and Russian financial crises. These events resulted in changes in the dynamics of volatility of asset returns in the samples prior and post the breaks.change-point, break dates, ARCH, high-frequency data.