Multifractal models in finance: Their origin, properties, and applications

This chapter provides an overview over the recently developed so called multifractal (MF) approach for modeling and forecasting volatility. We outline the genesis of this approach from similar models of turbulent flows in statistical physics and provide details on different specifications of multifractal time series models in finance, available methods for their estimation, and the current state of their empirical applications

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

Controlling the gain contribution of background emitters in few-quantum-dot microlasers

Funding: European Research Council under the European Union's Seventh Framework ERC Grant Agreement No. 615613; German Research Foundation via Grant-No.: Re2974/10-1, Gi1121/1-1.We provide experimental and theoretical insight into single-emitter lasing effects in a quantum dot (QD)-microlaser under controlled variation of background gain provided by off-resonant discrete gain centers. For that purpose, we apply an advanced two-color excitation concept where the background gain contribution of off-resonant QDs can be continuously tuned by precisely balancing the relative excitation power of two lasers emitting at different wavelengths. In this way, by selectively exciting a singleresonant QD and off-resonant QDs, we identify distinct single-QD signatures in the lasing characteristics and distinguish between gain contributions of a single resonant emitter and a countable number of offresonant background emitters to the optical output of the microlaser. Our work addresses the importantquestion whether single-QD lasing is feasible in experimentally accessible systems and shows that, for the investigated microlaser, the single-QD gain needs to be supported by the background gain contribution ofoff-resonant QDs to reach the transition to lasing. Interestingly, while a single QD cannot drive the investigated micropillar into lasing, its relative contribution to the emission can be as high as 70% and it dominates the statistics of emitted photons in the intermediate excitation regime below threshold.Publisher PDFPeer reviewe

Forecasting the price of gold

This article seeks to evaluate the appropriateness of a variety of existing forecasting techniques (17 methods) at providing accurate and statistically significant forecasts for gold price. We report the results from the nine most competitive techniques. Special consideration is given to the ability of these techniques to provide forecasts which outperforms the random walk (RW) as we noticed that certain multivariate models (which included prices of silver, platinum, palladium and rhodium, besides gold) were also unable to outperform the RW in this case. Interestingly, the results show that none of the forecasting techniques are able to outperform the RW at horizons of 1 and 9 steps ahead, and on average, the exponential smoothing model is seen providing the best forecasts in terms of the lowest root mean squared error over the 24-month forecasting horizons. Moreover, we find that the univariate models used in this article are able to outperform the Bayesian autoregression and Bayesian vector autoregressive models, with exponential smoothing reporting statistically significant results in comparison with the former models, and classical autoregressive and the vector autoregressive models in most cases

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

Drivers and impacts of mobile phone-mediated scaling of agricultural technologies: a meta-analysis

Mobile phone-mediated scaling of agricultural technologies (MPSATs) attracts attention as an effective approach for promoting agricultural development and sustainability. Despite the growing interest, a comprehensive understanding of drivers of MPSAT at the farm level and the evidence base of their impacts remains limited. To fill this gap, we conducted a systematic literature review and meta-analysis of 18 relevant empirical studies covering 10,757 farmers across 12 countries. Meta-analyses reveal that farmers’ innovativeness and full-time farming increase the odds of adopting agricultural technologies. Age, gender, digital skills, mobile phone device ownership, and membership in farmer groups also influence MPSAT but display heterogeneity. Moderation analysis reveals that the development status of countries plays a moderating role in variables such as asset ownership and farm size. Moreover, the results show that using mobile phones as a standalone method increases the odds of adopting agricultural technologies by 2%. In combination with traditional extension methods, this figure rises significantly to 17%. Additionally, MPSAT increases yields by 2%, and profits by 5%, and contributes to a 3% improvement in farmers’ learning outcomes. This study sheds light on the potential and multifaceted nature of MPSAT, providing insights for policymakers and practitioners promoting sustainable agriculture through digital technologies

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