Serial Autocorrelation Research Articles

An effective approach to detect the source(s) of out-of-control signals in productive processes by vector error correction (VEC) residual and Hotelling’s T2 decomposition techniques

One of the biggest challenges in the industrial sector is to simultaneously control quality characteristics. Multivariate control charts (MCC) are an efficient method to detect process variability, although they cannot signal the source responsible for variability to cause target deviation, especially in the presence of serial autocorrelation. Thus, this study proposes an approach to point out the source of variability of the process based on the vector error correction (VEC) residual and Hotelling’s T2 decomposition technique. The Design Research Methodology (DRM) was used to guide this study, and the accuracy and efficiency of the proposed approach were analyzed using simulated and real data from an extrusion machine operation, being: pressure (Pr), heating power (Hp), and temperature (Tp). Each of the monitored variables has 763 observations measured every 10 min of operation. An outlier was incorporated into each of the three variables: Pr - observation 91, Hp - observation 473, and Tp - observation 352. A VEC (3) model was fit and the residuals were plotted on a Hotelling’s T2 chart, which pointed the intentionally incorporated outliers. The Hotelling’s T2 decomposition of observation 91 showed thatT(Pr)2 = 14.4053 > 9.9187, and the variable Pr contributed significantly to the outlier. The variable Tp was identified as the source of variability for outlier 352 (p-value = 0.002). For observation 473, T(Hp)2=11.7202 > 9.9187; that is, Hp contributed significantly to the out-of-control point. Hotelling’s T2 chart was based on the VEC residual accurately identified the outliers, and the Hotelling’s T2 decomposition technique could identify the source of process variability.

LONG-TERM MODEL AND MONTE CARLO SIMULATION OF THE PUBLIC HEALTH EXPENDITURE IN BULGARIA

Purpose: The aim of the study is to build a long-term model and conduct a Monte Carlo simulation of the public health expenditure (PHE) of Bulgaria with the gross domestic product (GDP) as an independent variable. Material/Methods: Statistical models are used for modeling the long-term dependence between the macroeconomic dynamic rows, testing of hypotheses of stationarity (Augmented Dickey-Fuller tests), for serial autocorrelation and others. Results: There is a well-defined, statistically significant long-term relationship between public health expenditure and gross domestic product. The long-term model of health expenditure has an estimate of the cointegration constant of 1.023 (p-value < 0.05). Monte Carlo simulations are presented with 1 000, 2 000 and 3 000 experiments, generated based on the normal distribution of the input variable. Conclusions: In the period after the year 1990, a well-defined long-term relationship between public health expenditure and GDP exists. The Monte Carlo simulation can be regarded as a reliable instrument for studying the most likely fluctuations in health expenditure caused by the GDP.

Statistical control charts to assess the incidence of presumably infectious diarrhea reported between 2009 and 2019 in children under 4 years of age in the macro regions of Araçatuba, Marília and Presidente Prudente, São Paulo, Brazil.

Objective: To evaluate the monthly rates of hospitalizations for childhood diarrhea in macro-regions of Araçatuba, Marília and Presidente Prudente, SP, between 2019 -June Between June 2009. Methods: The average rates and their standard deviations for admission of diarrhea in the target population were obtained from DATASUS and standardized for cases x 100,000 inhabitants. Confidence limits were established, occurrences above confidence limits were considered epidemic events. The normality of the data and serial autocorrelation were tested using the Shapiro-Wilk and Durbin-Watson method. Results: All methods detected epidemic occurrences in the three regions. Araçatuba and Marília, the peaks were concentrated in the first half of the decade and Presidente Prudente, close to the middle. The CUSUM method was more sensitive to detect epidemic periods, however the normality data and assumptions have been violated by serial autocorrelation in a few months. The EWMA method was considered the most appropriate. Conclusions: Statistical process control charts can be used to monitor and compare disease incidence between different regions.

Spatio -Temporal Assessments of Rainfall Variability and Trends in the Highlands to Coastal Regions of Papua New Guinea

The present study assesses spatio-temporal rainfall variability of the most highlands to the coastal zones, comprising of eight provinces, of PNG. The variability investigation was carried out over for a period of 50 years starting from the year 1968 to 2018. After testing and checking for serial autocorrelation in the data series, Mann-Kendal non-parametric statistical evaluation was carried out to investigate rainfall trends and variability. Sen’s method was also used to investigate the magnitude of change in millimeters (mm) per year. Furthermore, the ArcGIS spatial analysis tools were used for the calculation of mean rainfall and to carry out spatial investigation. The assessments were carried out on an annual and seasonal basis within each designated study zone. CRU TS 4.03 gridded rainfall data on a 0.50 x 0.50 spatial resolution was used as an input data for trend as well as variability investigation. The CRU gridded station wise analysis was carried out to understand the variability at each specific location. From the assessments, it was found out that a higher rainfall is observed in the Eastern parts of Morobe, Southern Highlands region and central to northern part of Madang Province, while a low rainfall was observed in Goroka, the Western part of Morobe, Simbu, Western Highlands, Jiwaka and Enga province. From the trend investigation, it was observed that more grid stations show an increasing trend than a decreasing trend. On annual assessments, the significant decreasing trend is observed in the Enga and SH province, while significantly increasing trend is observed in the whole parts of Madang, and to the northern part of EH and Simbu Province. From overall assessments, it was found out that, there has been an increasing trend since 1968 up to the present.

Modelling taper and stem volume considering stand density in Eucalyptus grandis and Eucalyptus dunnii

Eucalyptus grandis and Eucalyptus dunnii are the most planted tree species in Uruguay. Anticipating information about the quantity and quality of wood is important for managing intensive forest plantation. The estimate of merchantable and total wood volume is an essential tool in forest planning and management. The aim of this study was to evaluate four systems of taper and merchantable volume that consisted in a taper, a merchantable volume and a total tree volume function. A modified second-order continuous autoregressive error structure corrected the inherent serial autocorrelation of different observations in one tree. Taper and volume equations were fitted simultaneously after autocorrelation correction by full information maximum likelihood method. The segmented system proposed by Fang et al. (2000) produced the best fit as it explained more than 98% of the taper, merchantable volume and total volume variability for both species. In addition, precision of the segmented system was compared with and without incorporating stand density as a variable. Results of this analysis showed that for E. grandis, the predictive accuracy of the model was improved by including the stand density variable, whereas for E. dunnii this variable was not statistically significant. This modelling framework provides an improvement in taper and tree volume predictions for E. dunnii and E. grandis in Uruguay. The possibilities offered by this methodology could be of interest for its application in countries where fast growing plantations are managed.

Event Tourism Demand and Selected Macroeconomic Variables: An Econometrics View of the Long-Run and Short-Run Relationships

Assessing the substantial economic benefits of event tourism will provide insight into the effectiveness of tourism event in Malaysia.The significance of the tourism event sector has the potential to boost Malaysia’s economic growth, increase the arrivalof international tourists, increase tourist expenditure and further job creations.This study empirically investigates determinantsof event tourism demand in Malaysia during the period of 1991Q1to 2016Q4. The Autoregressive Distributed Lag (ARDL) techniqueis used to find the long-run cointegration relationshipsof the model. The model is further tested by employing diagnostic tests(Normality test, Serial Autocorrelation, Heteroscedasticity and Ramsey’s RESET test) and stability tests(CUSUM and CUSUMSQ). The empirical analysis of the boundstest indicatesthat there is a long-run cointegration among the variables under study. Besides that, the ARDL model produces reliable results,as all of the computed coefficientsof the independent variables are statistically significant with the expected signs. The findings are consistent witheconomic theory and the model passed all of the diagnostic tests.The findings of this study imply that event demand can be improved significantly when government spending,theexchange rate and tourist receipts increaseand the crude oil price decreases. Hence, government authorities and the private sector should createan integrated plan to enhancethe profit gained through the Malaysian economyfrom event tourism.

Improving the ability of a BACI design to detect impacts within a kelp-forest community.

Distinguishing between human impacts and natural variation in abundance remains difficult because most species exhibit complex patterns of variation in space and time. When ecological monitoring data are available, a before-after-control-impact (BACI) analysis can control natural spatial and temporal variation to better identify an impact and estimate its magnitude. However, populations with limited distributions and confounding spatial-temporal dynamics can violate core assumptions of BACI-type designs. In this study, we assessed how such properties affect the potential to identify impacts. Specifically, we quantified the conditions under which BACI analyses correctly (or incorrectly) identified simulated anthropogenic impacts in a spatially and temporally replicated data set of fish, macroalgal, and invertebrate species found on nearshore subtidal reefs in southern California, USA. We found BACIfailed to assess very localized impacts, and had low power but high precision when assessing region-wide impacts. Power was highest for severe impacts of moderate spatial scale, and impacts were most easily detected in species with stable, widely distributed populations. Serial autocorrelation in the data greatly inflated false impact detection rates, and could be partly controlled for statistically, while spatial synchrony in dynamics had no consistent effect on power or false detection rates. Unfortunately, species that offer high power to detect real impacts were also more likely to detect impacts where none had occurred. However, considering power and false detection rates together can identify promising indicator species, and collectively analyzing data for similar species improved the net ability to assess impacts. These insights set expectations for the sizes and severities of impacts that BACI analyses can detect in real systems, point to the importance of serial autocorrelation (but not of spatial synchrony), and indicate how to choose the species, and groups of species, that can best identify impacts.

The Non-Observed Economy in Morocco: Estimation and Analysis Using the Currency Demand Approach

Abstract This study estimates the size of the non-observed economy (NOE) in Morocco and analyzes its evolution over the period 1977 - 2019. We estimate a currency demand function following Tanzi’s (1980; 1983) Currency Demand Method. We take into consideration the difference in money velocities between the formal and informal sectors using the Ahumada et al.’s (2006) correction. We also adopt the fully modified least squares (FM-OLS) in order to tackle the series’ cointegration and to circumvent the serial autocorrelation issues. In terms of epistemological paradigms, we follow the positivism stance with a hypothetico-deductive approach. In addition to the conventional variables used in the currency demand approach, we emphasize the effects of other variables that are specific to the Moroccan context and that influence the size of its informal economy. Therefore, we introduce a dummy variable that captures the impact of banking regulations, net remittances, consumer price index, the part of government’s final public consumption in the Gross Domestic Product (GDP) and the part of public investment in GDP. The results of our econometric estimations show that remittances have a negative effect on currency demand. The weight of taxation, reforms, rising living costs, the part of wages in the national revenue, the consumer price index and the parts of public consumption and investment in GDP have a positive impact on the quantity of currency circulating in the economy. Keywords: Non-observed economy, informal sector, tax burden, fiscal evasion, money velocity currency demand method, fully modified least squares.

Stock Return Autocorrelation and Individual Equity Option Prices

This study demonstrates empirically the impact of stock return autocorrelation on the prices of individual equity option. The option prices are characterized by the level and slope of implied volatility curves, and the stock return autocorrelation is measured by variance ratio and first-order serial return autocorrelation. Using a large sample of U.S. stocks, we show that there is a clear link between stock return autocorrelation and individual equity option prices: a higher stock return autocorrelation leads to a lower level of implied volatility (compared to realized volatility) and a steeper implied volatility curve. The stock return autocorrelation is more important in explaining the level of implied volatility curve for relatively small stocks. The relation between stock return autocorrelation and option price structure is more pronounced when market is volatile, especially during financial crisis. The stock return autocorrelation is more important in explaining the level of implied volatility curve for relatively small stocks. Thus, stock return autocorrelation can help differentiate the price structure across individual equity options.

THE CONTRIBUTION OF FDI AND FINANCIAL DEVELOPMENT ON ECONOMIC GROWTH IN SRI LANKA

The present study investigates the relationship between financial development, Foreign direct investment and economic growth in Sri Lanka for the period 1980 to 2019 by applying the Augmented Dickey-Fuller Unit root test along with the ARDL approach in process of achieving the desired objective. The outcome of this study shows that except GDP and FDI all other variables such as Capital investment as a percent of GDP (CI), Bank credit to the private sector as a percent of GDP (BCP), net foreign direct investment inflows in % of GDP (FDI) are stationary at first difference. The findings reveal that net foreign direct investment inflows are a positive relationship with economic growth in the long run. It means a one percent increase in net foreign direct investment inflows increases the GDP by 0.826439 percent. At the same time, a one percent increase in bank credit to the private sector decreases the GDP by 0.864320 percent. Moreover, in the short run FDI, CI and BCP have a positive and significant impact on GDP. Diagnostic tests such as normality test, heteroskedasticity and serial autocorrelation are employed to validate parameter estimation outcomes. Further, the stability of the variables confirms by the CUSUM test. The country should propose Strategies to boost the growth of efficient domestic financial institutions and encourage policy to attract greater FDI inflows that meet the needs of the knowledge-based economy.

Forecasting Wind Speed in Peninsular Malaysia: An Application of ARIMA and ARIMA-GARCH Models

In the global energy context, renewable energy sources such as wind is considered as a credible candidate for meeting new energy demands and partly substituting fossil fuels. Modelling and forecasting wind speed are noteworthy to predict the potential location for wind power generation. An accurate forecasting of wind speed will improve the value of renewable energy by enhancing the reliability of this natural resource. In this paper, the wind speed data from year 1990 to 2014 in 18 meteorological stations throughout Peninsular Malaysia were modelled using the Autoregressive Integrated Moving Average (ARIMA) to forecast future wind speed series. The Ljung-Box test was used to determine the presence of serial autocorrelation, while the Engle’s Lagrange Multiplier (LM) test was used to investigate the presence of Autoregressive Conditional Heteroscedasticity (ARCH) effect in the residual of the ARIMA model. In this study, three stations showed good fit using the ARIMA modelling since no serial correlation and ARCH effect were present in the residuals of the ARIMA model, while the ARIMA-GARCH had proven to precisely capture the nonlinear characteristic of the wind speed daily series for the remaining stations. The forecasting accuracy measure used was based on the value of root mean square error (RMSE) and mean absolute percentage error (MAPE). Both ARIMA and ARIMA-GARCH model proposed provided good forecast accuracy measure of wind speed series in Peninsular Malaysia. These results will help in providing a quantitative measure of wind energy available in the potential location for renewable energy conversion.

Flypaper Effect of Intergovernmental Transfers and Incentives to Improve Own-Source Revenue Mobilization of Local Governments in the Central Region of Ghana

Flypaper effect and the fiscal interest model have been employed to explain the fiscal behaviour of local government officials in the Central Region of Ghana. Central government transfers or grants have taken dominion over local government expenditure compared with own-source revenues, creating a situation known as the flypaper effect. Using panel data for 17 local governments from 2008 to 2015, the study examined the fiscal behavior of local government officials when presented with intergovernmental fiscal transfers and own-source revenues. The analysis employed the panel data analysis of fixed effects and random effects. However, given the optimal unbiased results, the Generalized Least Squares (GLS) is estimated to account for heteroscedasticity and serial autocorrelation. The results show that central government transfers contribute more to local government expenditure than local governments’ own-source revenues. This situation confirms the flypaper effect on local governments in the Central Region, thus explaining the fiscal behavior of the local governments. Whilst the system of intergovernmental transfers in Ghana has been very successful in directing resources towards the local governments, it may be counterproductive in encouraging the local governments in raising their revenue at the local level as demonstrated by the presence of the flypaper effect.

Insights on the trend of the Novel Coronavirus 2019 series in some Small Island Developing States: A Thinning-based Modelling Approach

Undeniably, the Novel Coronavirus 2019, (COVID-19), has disrupted the routine functioning of the global economic and social activities. In particular, vulnerable economies such as the Small Island Developing states (SIDs) are facing unprecedented health and financial crisis. In such critical situation, some in-depth statistical models can be helpful for proper planning in terms of identifying factors that can influence significantly the number of infected COVID-19 cases and for forecasting. Modelling the COVID-19 infected series is a statistical challenge since the series are severely over-dispersed with lots of oscillations. This paper attempts a new integer-valued time series model based on the auto-regressive structure (INAR), with an oscillating Weighted Cosine Geometric (WCG) innovation term. The parameters in the proposed model constitute of the regression effects and serial auto-correlation coefficients and are estimated via likelihood and moment-based approaches. Monte Carlo simulation experiments are implemented to assess the performance and consistency of the different resulting estimators. Thereon, the INAR-WCG is applied to the COVID-19 series of various SIDs countries that include Singapore, Cape Verde, Bahrain, Mauritius and Maldives. The findings reveal that factors such as the transmission mode and the Government Stringency Index are the most influential. In terms of model fitting and forecasting, the INAR-WCG provides slightly better RMSEs than the other competing INAR-based processes.

Suicide rates between men and women in Brazil, 2000-2017.

This study aimed to analyze the trend in the standardized suicide rates by gender, according to Brazil's geographic regions from 2000 to 2017. Mortality data were retrieved from the Brazilian Mortality Information System database. Linear regression models were used, and Durbin-Watson tests were applied to detect the independence of the residues, as well as Prais-Winsten to control serial autocorrelation. We classified the trends as increasing, decreasing, and stable at 5% significance level. The standardized rate of deaths by suicide for men increased by 75%, from 6.5 to 11.3 deaths per 100,000 inhabitants. For women, the increase was 85%, from 1.6 to 3.0 deaths per 100,000 inhabitants. We found a gradual increase in the standardized suicide rates in all regions for both genders. The growth magnitude of suicide rates in the South is twice for women, whereas for the Northeast it is greater for men. The South and the Central-West regions presented the highest rates, whereas the North the lowest. The trend of standardized rates of suicide is linear and significantly increasing for Brazil and its total geographic regions for both genders. Male and female suicide rates indicate different risk rates according to gender.

Comprehensive Assessment of Sanitary Landfills in Edo State of Nigeria for Waste Management and Pollution Control

The aim of this study is to comprehensively assess the need for Sanitary landfills in Edo state for management of waste, control of pollution for sustainable manufacturing system using concurrent triangulation design. The concurrent triangulation design is in two phases, Phase 1 using quantitative analysis, descriptive statistics while phase 2 used the qualitative analysis using thematic selection. Final results collected from the merger of results from phase 1 and phase 2 showed the Regression analysis results had a T-test value of -0.4615 at P value significant of 0.000 obtained for Sanitary landfill in the model and was less than 5% level of significance indicating that there is a significant relationship between Sanitary landfilling and pollution control. The Durbin Watson result of 1.955 showed that the model is reliable with absence of serial auto correlation. A coefficient of - 0.346 showed that 1% increase in sanitary landfilling activities results in 34.6% decrease in environmental pollution. Hence Sanitary landfilling for waste management has proved to sustain manufacturing system, control environmental pollution and reduce waste.
 Keywords: Sanitary landfilling, waste management, pollution control, sustainable manufacturing system,

Abstract P156: Improvement In Blood Pressure Using Remote Patient Monitoring During COVID19

Introduction: The coronavirus disease 2019 (COVID19) pandemic required strict social distancing to curb transmission. Unfortunately, these measures severely limited healthcare access and chronic disease management. In response, many health organizations rapidly developed or expanded telemedicine to provide care directly to patients at home. Little has been reported about the impact of such interventions on clinical outcomes during COVID19. We examined whether enrollment in a remote patient monitoring (RPM) program for hypertension (HTN) prior to COVID19 was associated with improved blood pressure during the pandemic. Methods: We developed an RPM program that tracked vital signs, medication side effects, and treatment adherence patterns outside of the clinic. Patients were referred by their primary care doctor for uncontrolled HTN or suspected white coat HTN. Patients received a two-way tablet, blood pressure cuff, and virtual nursing support via scheduled video visits. Those referred for uncontrolled HTN who had at least two weeks of data both before and after the onset of COVID19 (defined as the first two weeks of March 2020) were included in the study. A mixed-models analysis that adjusted for serial autocorrelation was used to compare mean systolic blood pressure (SBP) and mean diastolic blood pressure (DBP) in the pre-/post-COVID19 periods. Results: Of 94 patients enrolled in the RPM program to date, 46 had at least two weeks of data both pre-COVID19 and post-COVID19. Mean age was 69.0 ± 10.9 years, 69.6% (32 of 46) were women, 78.3% (36 of 46) were Hispanic, and 63.0% (29 of 46) were Spanish-speaking. Pre-COVID, mean SBP was 132.31 ± 13.99 mmHg, mean DBP was 77.10 ± 9.87 mmHg, and 70% (32 of 46) of patients had uncontrolled BP (>130/80 mmHg per AHA guidelines). Post-COVID, mean SBP was 129.57 ± 13.29 mmHg, mean DBP was 76.00 ± 9.16 mmHg, and 57% (26 of 46) of patients had uncontrolled BP. There was a significant reduction in both mean SBP (β = –2.74, 95% CI –5.21, –0.26, p = 0.03) and mean DBP (β = –1.10, 95% CI –2.22, 0.02, p = 0.05) post-COVID vs. pre-COVID. Discussion: Despite the stress and social isolation associated with COVID19, participation in an RPM program that combines home BP monitoring with virtual nursing support can help maintain and even mildly decrease BP.

Most recent changepoint detection in censored panel data

This study aims to detect the most recent changepoint in censored panel data by ignoring dependence within and between segments as well as taking into account the serial autocorrelation. A comparison of different methods to detect the most recent changepoint for censored data is presented. Different censoring rates such as 20%, 50%, and 90% in the case of right and left censoring while (10%, 10%), (25%, 25%) and (40%, 50%) for interval censoring are considered. Further, we use most recent changepoint (MRC), double cumulative sum binary segmentation, non parametric changepoint detection (ECP), multiple changepoints in multivariate time series, analyzing each series in the panel independently, and analyzing aggregated data (AGG) methods. It is observed that different censoring rates have a significant effect on the detection of changepoints in high dimensional data. It is also noticed that the MRC method outperforms the competing methods considered in this study. In addition to investigating the impact of penalties, the performance of MRC and AGG methods is also compared using water quality data of the Niagara River. Also, a data set related to survival time of stroke patients is also a part of this study. An R package “cpcens” is available in comprehensive R archive network to replicate the results of this article.

Associations Over Time Between Paid Medical Malpractice Claims and Imaging Utilization in the United States.

The aim of this study was to explore state-level relationships between the incidence and payout amounts for medical malpractice claims and Medicare imaging utilization and spending across the United States. Using claims data from a 5% sample of Medicare beneficiaries for 2004 to 2016, annual state population-adjusted rates of imaging utilization and spending were calculated. Using National Practitioner Data Bank files, all paid physician malpractice claims were identified, and lagged state population-adjusted rolling averages of paid claim frequencies and payout amounts were calculated. Controlling for secular trends and state fixed effects with robust standard errors clustered at the state level to account for serial autocorrelation, associations between imaging utilization and lagged paid malpractice claims were assessed using multivariate regression models. Log-log model specification was used to obtain elasticity measures. Between 2004 and 2016, national Medicare diagnostic imaging utilization and spending declined by 31.4% and 47.2%, respectively (from 355,057 to 243,517 examinations and from $28,591,146 to $15,099,291 per 100,000 beneficiaries). Overall national paid malpractice claims and payout amounts declined by 46.4% and 39.6%, respectively (from 4.83 to 2.59 claims and from $1,803,565 to $1,089,112 per 100,000 population). After controlling for secular trends and state fixed effects, advanced imaging utilization was positively associated with the lagged number of per capita paid malpractice claims. Each 1% increase in average paid malpractice claims was associated with a subsequent 0.20% increase in advanced imaging utilization (P= .001). Positive associations between paid malpractice claims and advanced Medicare imaging utilization support the contention that US physicians use medical imaging as a defensive medicine strategy.

Studying the trend of the novel coronavirus series in Mauritius and its implications.

Mauritius stands as one of the few countries in the world to have controlled the current pandemic, the novel coronavirus 2019 (COVID-19) to a significant extent in a relatively short lapse of time. Owing to uncertainties and crisis amid the pandemic, as an emergency announcement, the World Health Organization (WHO) solicits the help of health authorities, especially, researchers to conduct in-depth research on the evolution and treatment of COVID-19. This paper proposes an integer-valued time series model to analyze the series of COVID-19 cases in Mauritius wherein the corresponding innovation term accommodates for covariate specification. In this set-up, sanitary curfew followed by sanitization and sensitization campaigns, time factor and safe shopping guidelines have been tested as the most significant variables, unlike climatic conditions. The over-dispersion estimates and the serial auto-correlation parameter are also statistically significant. This study also confirms the presence of some unobservable effects like the pathological genesis of the novel coronavirus and environmental factors which contribute to rapid propagation of the zoonotic virus in the community. Based on the proposed COM-Poisson mixture models, we could predict the number of COVID-19 cases in Mauritius. The forecasting results provide satisfactory mean squared errors. Such findings will subsequently encourage the policymakers to implement strict precautionary measures in terms of constant upgrading of the current health care and wellness system and re-enforcement of sanitary obligations.

Quant GANs: deep generation of financial time series

Modeling financial time series by stochastic processes is a challenging task and a central area of research in financial mathematics. As an alternative, we introduce Quant GANs, a data-driven model which is inspired by the recent success of generative adversarial networks (GANs). Quant GANs consist of a generator and discriminator function, which utilize temporal convolutional networks (TCNs) and thereby achieve to capture long-range dependencies such as the presence of volatility clusters. The generator function is explicitly constructed such that the induced stochastic process allows a transition to its risk-neutral distribution. Our numerical results highlight that distributional properties for small and large lags are in an excellent agreement and dependence properties such as volatility clusters, leverage effects, and serial autocorrelations can be generated by the generator function of Quant GANs, demonstrably in high fidelity.