Modeling and forecasting crude oil price volatility: Evidence from historical and recent data

This paper uses the Markov-switching multifractal (MSM) model and generalized autoregressive conditional heteroscedasticity (GARCH)-type models to forecast oil price volatility over the time periods from January 02, 1875 to December 31, 1895 and from January 03, 1977 to March 24, 2014. Based on six different loss functions and by means of the superior predictive ability (SPA) test, we evaluate and compare their forecasting performance at short and long horizons. The empirical results indicate that none of our volatility models can uniformly outperform other models across all six different loss functions. However, the new MSM model comes out as the model that most often across forecasting horizons and subsamples cannot be outperformed by other models, with long memory GARCH-type models coming out second best

Modeling and Forecasting Carbon Dioxide Emission Allowance Spot Price Volatility: Multifractal vs. GARCH-type Volatility Models

This paper applies Markov-switching multifractal (MSM) processes to model and forecast carbon dioxide (CO2) emission price volatility, and compares their forecasting performance to the standard GARCH, fractionally integrated GARCH (FIGARCH) and the two-state Markov-switching GARCH (MS-GARCH) models via three loss functions (the mean squared error, the mean absolute error and the value-at-risk). We evaluate the performance of these models via the superior predictive ability test. We find that the forecasts based on the MSM model cannot be outperformed by its competitors under the vast majority of criteria and forecast horizons, while MS-GARCH mostly comes out as the least successful model. Applying various VaR backtesting procedures, we do, however, not find significant differences in the performance of the candidate models under this particular criterion. We also find that we cannot reject the null hypothesis of MSM forecasts encompassing those of GARCH-type models. In line with this result, optimally combined forecasts do indeed hardly improve upon the best single models in our sample

Multifractal Models, Intertrade Durations and Return Volatility

This thesis covers the application of multifractal processes in modeling financial time series. It aims to demonstrate the capacity and the robustness of the multifractal processes to better model return volatility and ultra high frequency financial data than both the generalized autoregressive conditional heteroscedasticity (GARCH)-type and autoregressive conditional duration (ACD) models currently used in research and practice. The thesis is comprised of four main parts that particularize the different procedures and the main findings. In the first part of the thesis we first delineate the genesis of multifractal (MF) measures and processes and how one can construct a simple MF measure. We outline the generic properties of the MF processes, mention how they motivate financial time series models, and present the different tools developed for the estimation of the MF models and the forecasting of return volatilities and some empirical results. Second, we give a short overview of both autoregressive conditional duration (ACD) models and Markov switching multifractal duration (MSMD) models. We start with some theoretical microstructure literature that motivate both models. We present ACD and MSMD models and their subsequent extensions. Finally, we cite the different diagnostic tests developed in the literature for assessing their adequacy and provide some prominent empirical studies. The second part deals with the application the Markov-switching multifractal (MSM) model and generalized autoregressive conditional heteroscedasticity (GARCH) type models in forecasting crude oil price volatility. Based on six different loss functions and by means of the superior predictive ability (SPA) test of Hansen (2005) we evaluate and compare their forecasting performance at short- and long-horizons. The results give evidence that none of our volatility models can outperform other models across all six different loss functions. However, the long memory GARCH-type models and the MSM model seem to be more appropriate in terms of fitting and forecasting oil price volatility. We also found that forecast combinations of long memory GARCH-type models and the MSM lead to an improvement in forecasting crude oil price volatility. The third and longest part of the thesis compares the predictive ability of the Markov switching multifractal duration (MSMD) model recently introduced by Chen et al. (2013) to those of the standard ACD (cf. Engle and Russell, 1998), Log-ACD (cf. Bauwens and Giot, 2000), and fractionally integrated ACD (FIACD) (cf. Jasiak, 1998) models. We assume that innovations in the ACD and Log-ACD models follow Weibull, Burr, generalized gamma and Lognormal distributions. For FIACD we only consider the case where the innovation is standard exponentially distributed. We assess the forecasting performance of the models using density forecasts evaluation methodologies proposed by Diebold et al. (1998) and the likelihood ratio test of Berkowitz (2001). We complement these methodologies with Kolmogorov-Smirnov and Anderson-Darling distances (cf. Rachev and Mittnik, 2000). Empirically, results are quite nice and speak for the MSMD model. In fact, the MSMD model can better capture the long memory and the fat tails observed in trade and price duration data, and therefore, outperforms both the FIACD, ACD and Log-ACD models. We also found that certain distributional assumptions for the innovations strongly enhance the forecasting performance of the ACD and Log-ACD models. In line with the last result, we want to know to what extent different distributional assumptions for the innovation in the MSMD model may influence the model’s forecasting performance. So, we assume that the innovation in the MSMD model follows generalized gamma or Burr distribution. To compare and select the model that provides better fit to the empirical data (trade, price and volume durations) we make use of the Akaike information criterion (AIC), the Bayesian information criterion (BIC) and the likelihood ratio test. Surprisingly, both distributional assumptions for the innovation do not much affect the predictive ability of the model. It seems that the ability of the MSMD model to fit financial duration data largely stems from the multifractal processes. Third, we generalize the univariate MSMD model to a bivariate one. The bivariate MSMD model is substantially an adaptation of the bivariate Markov switching multifractal (MSM) process proposed by Calvet et al. (2006) to high frequency financial data. We apply the bivariate MSMD model to analyze the co-movement between the bid-ask spreads of different stocks. The results indicate that bid-ask spreads of sector-specific or cross-sector stocks may be simultaneously affected by arrival of information in the market. Fourth, we apply the standard MSMD and the generalized gamma ACD (GGACD) models to forecast irregularly spaced intra-day value-at-risk (ISIVaR) in a semi-parametric framework. We assess the performance of both models to produce accurate irregularly spaced intra-day VaR via the generalized moments method (GMM) duration-based test developed by Candelon et al. (2011). The results show that the MSMD model outperforms the GGACD model and can be used in practice to manage market risk. The last part summarizes the main findings of the thesis and presents some outlooks for future research

The experience and perceptions of nurses working in a public hospital, regarding the services they offer to patients.

Nurses comprise the majority of health care service providers and function as an integral part of the services rendered by the health care system in South Africa. There are however, frequent expressions of concern about their working conditions and circumstances. The health care system in South Africa faces difficulties in terms of resources and service provision, with nurses themselves sometimes being criticised for rendering less than adequate services (Khoza, Du Toit & Roos, 2010). Healthcare sector strikes have also been a feature of recent times, influenced by poor salaries, deterioration of academic facilities, poor working conditions in the public sector and the unfortunate conditions facing patients at public health facilities (Dhai, Etheredge, Voster & Veriava, 2011). The nursing care-relationship, however, requires qualities of empathy, compassion, ethical practice and commitment and these demands and contradictions may lead to burnout, compassion fatigue and secondary trauma (Holdt, 2006). The study therefore explored the perceptions of nurses about their role, the quality of the health care services which they provide, their perceptions on nurse/patient relationships; and their perceptions of both problems and strengths or protective factors in their nursing role. Using a qualitative approach, the study included twenty nurses working in a large public hospital in Gauteng. Purposive sampling was used to select participants from various wards. Data was collected through semi-structured, face-to-face interviews, in order to enable participants to reflect on the meanings of their experiences and the perceptions they attach to these experiences. Thematic content analysis was used to analyze data. The main findings were that nurses perceive their occupational stress arising from shortage of staff and limited and inadequate equipment. This resulted in fatigue, and a high rate of absenteeism. Nurses in this hospital reported that they experience trauma due to the nature of their work with little visible and accessible formal debriefings, trauma counseling and Employee Wellness Programmes in place to assist them with stress management for traumatic experiences and other work related problems. Working conditions are perceived as unfavorable and unsafe, exposing them to health hazards, while simultaneously having to deal with frustrated patients and relatives

Harnessing indigenous knowledge and practices for effective adaptation in the Sahel

The Sahel region of West Africa has experienced some of the most severe multidecadal rainfall variability over the past 50 years. Based on recollections of the past and observations of the present, local communities in the Sahel have developed extensive knowledge and understanding of their environment and climate that enables them to harness ecosystem services to support their livelihoods and survive environmental changes. Recent literature indicated that farmers’ knowledge and perceptions of changes in the local climate are largely consistent with observed meteorological data, except for the more heterogeneous precipitation change. This understanding of changes in their environment combined with their indigenous knowledge can be particularly useful in data-sparse regions such as the Sahel. This review highlights the importance of indigenous knowledge in enabling effective adaptation in the Sahel and beyond. It outlines some future research avenues for fostering indigenous knowledge-based adaptation, including addressing barriers to mainstreaming of indigenous knowledge into climate research and policy

The role of economic policy uncertainty in predicting US recessions : a mixed-frequency markov-switching vector autoregressive approach

This paper analyzes the performance of the monthly economic policy uncertainty (EPU) index in predicting recessionary regimes of the (quarterly) U.S. GDP. In this regard, the authors apply a mixed-frequency Markov-switching vector autoregressive (MF-MS-VAR) model, and compare its in-sample and out-of-sample forecasting performances to those of a Markov-switching vector autoregressive model (MS-VAR, where the EPU is averaged over the months to produce quarterly values) and a Markov-switching autoregressive (MS-AR) model. Their results show that the MF-MS-VAR fits the different recession regimes, and provides out-of-sample forecasts of recession probabilities which are more accurate than those derived from the MS-VAR and MS-AR models. The results highlight the importance of using high-frequency values of the EPU, and not averaging them to obtain quarterly values, when forecasting recessionary regimes for the U.S. economy.http://www.economics-ejournal.orgam2016Economic

Forecasting stock market volatility with regime-switching GARCH-MIDAS : the role of geopolitical risks

We investigate the role of geopolitical risks in forecasting stock market volatility at monthly horizons within a robust autoregressive Markov-switching GARCH mixed-data-sampling (AR-MSGARCH-MIDAS) framework. Our approach accounts for structural breaks through regime switching and allows us to disentangle short- and long-run volatility components. We conduct an empirical out-of-sample forecasting analysis using (i) daily Dow Jones Industrial Average returns, and (ii) monthly sampled geopolitical risks and macroeconomic variables over a time span of 122 years. We find that the impact of geopolitical risks as explanatory variables for stock market volatility forecasts at monthly horizons hinges crucially on the specific prediction model chosen by the forecaster. After capturing the non-stationarities in the data via an MSGARCH framework, we do not find significant forecast accuracy improvements through the inclusion of geopolitical risk indices.http://www.elsevier.com/locate/ijforecasthj2024EconomicsSDG-08:Decent work and economic growt

Forecasting home sales in the four census regions and the aggregate US economy using singular spectrum analysis

Accurate forecasts of home sales can provide valuable information for not only, policy makers, but also financial institutions and real estate professionals. Given this, our analysis compares the ability of two different versions of Singular Spectrum Analysis (SSA) meth- ods, namely Recurrent SSA (RSSA) and Vector SSA (VSSA), in univariate and multivariate frameworks, in forecasting seasonally unadjusted home sales for the aggregate US economy and its four census regions (Northeast, Midwest, South and West). We compare the perfor- mance of the SSA-based models with classical and Bayesian variants of the autoregressive and vector autoregressive models. Using an out-of-sample period of 1979:8-2014:6, given an in-sample period of 1973:1-1979:7, we find that the univariate VSSA is the best performing model for the aggregate US home sales, while the multivariate versions of the RSSA is the outright favorite in forecasting home sales for all the four census regions. Our results high- light the superiority of the nonparametric approach of the SSA, which in turn, allows us to handle any statistical process: linear or nonlinear, stationary or non-stationary, Gaussian or non-Gaussian.http://link.springer.com/journal/106142017-12-31hb2016Economic

Technologies and practices for agriculture and food system adaptation to climate change in The Gambia

Agriculture is a major source of livelihood and income in The Gambia. Despite its socioeconomic importance, the sector faces many institutional, technological, and biophysical challenges limiting its contribution to economic development. The situation is exacerbated by adverse effects of climate change, which is undermining national efforts, making The Gambia one of the most vulnerable to climate change. This report documents and synthesizes available climate-smart agriculture (CSA) options that can inform adaptation planning in The Gambian agriculture and food system. We analysed the relevance of the documented options in sustainably increasing productivity and income while building climate resilience and reducing GHGs emissions in food systems. Through a mixed approach integrating multiple sources, a total of 28 technologies and practices has been identified as relevant adaptation options for The Gambia agriculture and food system. These options are grouped into nine adaptation categories including Crop diversity use and management, Soil and nutrient management, Soil & Water Conservation and Irrigation, Agroforestry systems, Livestock-based systems, agro-climatic information services, Social network and institutional support, and Livelihood diversification. Except for post-failure coping strategies known to be ineffective and unsustainable, all the identified options have some potentials to sustainably increase agricultural productivity and income while adapting and building resilience to climate change and reducing greenhouse gas emissions. This synthesis provides evidence of potential climate-smartness of the selected adaptation options and could be important to inform adaptation planning and prioritization

Are multifractal processes suited to forecasting electricity price volatility? Evidence from Australian intraday data

We analyze Australian electricity price returns and find that they exhibit volatility clustering, long memory, structural breaks, and multifractality. Consequently, we let the return mean equation follow two alternative specifications, namely (i) a smooth transition autoregressive fractionally integrated moving average (STARFIMA) process, and (ii) a Markov-switching autoregressive fractionally integrated moving average (MSARFIMA) process. We specify volatility dynamics via a set of (i) short- and long-memory GARCH-type processes, (ii) Markov-switching (MS) GARCH-type processes, and (iii) a Markov-switching multifractal (MSM) process. Based on equal and superior predictive ability tests (using MSE and MAE loss functions), we compare the out-of-sample relative forecasting performance of the models. We find that the (multifractal) MSM volatility model keeps up with the conventional GARCH- and MSGARCH-type specifications. In particular, the MSM model outperforms the alternative specifications, when using the daily squared return as a proxy for latent volatility.https://www.degruyter.com/view/j/snde2021-11-17am2021Economic