Use Of Daily Data Research Articles

Electric load prediction using SARIMAX

ABSTRACT Electricity demand variation is one of the prevalent phenomena in Nepal. This variation can cause excess electricity during low-demand periods and a shortage during high-demand periods. An early prediction of electricity demand allows for levelling such fluctuations by introducing dynamic tariffs based on varying demands. This study aims to use Python’s time series models to make predictions about electrical demands and propose a dynamic tariff system. The dataset of Panama City’s hourly electrical load is brought into use. Two models, namely the Seasonal Auto-Regressive Integrated Moving Average with Exogenous Factors (SARIMAX) and the BATS model, are deemed to be the two best time series models. The selection of optimal seasonal and non-seasonal orders in SARIMAX is made by comparing Bayes Information Criterion (BIC). For the SARIMAX model, a daily dataset has been taken by summing the hourly data for each day, whereas for the BATS model, hourly data for the year 2018 has been used. The SARIMAX model resulted in the best fit with the evaluation metrics values: Root Mean Square Error (RMSE) of 975,336.88, Mean Average error (MAE) of 739.09, and R2 score of 0.75. However, SARIMAX’s limitation is that the use of daily data does not allow dynamic tariff prediction, which is possible with the BATS model.

European market timing

In this paper, we analyze equity mutual funds from the main European countries using daily and monthly returns to determine whether the temporary frequency of the data produces changes in the identification of timing skills by fund managers that justifies the current trend in the finance literature of using daily returns instead of monthly observations for performance measurement purposes. In our analysis we employ data for 17 European countries from 1990 to 2020, we appreciate a greater significance in the results obtained when using daily returns, approximately 10% of funds show significantly positive market timing skills and the same proportion of funds show negative market timing across countries. In the present study, we show the usefulness of the increase in the temporal frequency of the observations as the use of daily data instead of monthly returns implies a greater significance in the results obtained. Our findings indicate that some mutual fund managers take advantage of the predictability of market returns explained in the finance literature. Thus, potential investors might try to identify the managers who have these timing skills to invest in their funds.

Assessing the Income Distributional Effect of Lockdowns in Malaysia

This paper examines the distributional effect of lockdown measures on the loss of income. The significance of the study relates to the use of daily data on loss of income and lockdown measures arising from the unprecedented COVID-19 pandemic. A conditional variable of the total loss of income was evaluated using hypothesized variables of international restrictions, domestic movement restrictions, public events closure, restriction on gathering, stay at home restrictions, school closure, and workplace closure. For a robust result, the study applied four variants of regression such as Ordinary Least Squares (OLS), Fully Modified OLS, Dynamic OLS, and Autoregressive Distributed Lag (ARDL) as the empirical methods. The findings revealed that loss of income during the COVID-19 period has a positive and significant relationship with the lockdown measures in Malaysia. This implies that as the government strengthens the international and domestic movement restrictions to reduce the impact of COVID-19, the most vulnerable income groups lose their income. This finding is relevant to policymakers regarding plans to rebuild a resilient economy to withstand future pandemics. Policy strategies to reduce restriction measures and allow for opening up the economy to enable the vulnerable income groups to regain their jobs and hence the lost income was discussed.

ANÁLISE DE IMPACTO REGULATÓRIO: UMA ABORDAGEM DE ESTUDOS DE EVENTOS

There is a growing concern of governments and regulators in assessing the impact of laws, decrees and other regulations. Researchers, government and international organizations have been discussing the use of Regulatory Impact Analysis (RIA) and Cost Benefit Analysis (BCA). This article proposes an approach for impact analysis of regulation, derived from event study methodology with adjustments to circumvent the problem of contemporaneous correlation among observations. Indeed, as regulation affects all companies at the same time, it is particularly challeging to tell apart the effects of regulation from other influences. The approach proposed avoid this and other challenges, such as the establishment of a benchmark for market returns, the different effects of regulation over different regulated companies, the dealing with effects that are not noticeable in event studies and the effects of the use of daily data for those studies. At the end, it is presented a script for the econometric evaluation of financial and sectorial regulation and it is suggested, as an example, some empirical models for the test of financial regulations.

Midlatitude Winter Extreme Temperature Events and Connections with Anomalies in the Arctic and Tropics

AbstractFor the last few decades the Northern Hemisphere midlatitudes have seen an increasing number of temperature extreme events. It has been suggested that some of these extremes are related to planetary wave activity. In this study we identify wave propagation regions at 300 hPa using the ERA-Interim dataset from 1980 to 2017 and link them to temperature extremes in densely populated regions of the Northern Hemisphere. Most studies have used background flow fields at monthly or seasonal scale to investigate wave propagation. For a phenomenon that is influenced by threshold incidents and nonlinear processes, this can distort the net Rossby wave signal. A novel aspect of our investigation lies in the use of daily data to study wave propagation allowing it to be diagnosed for limited but important periods across a wider range of latitudes, including the polar region. We show that winter temperature extremes in the midlatitudes can be associated with circulation anomalies in both the Arctic and the tropics, while the relative importance of these areas differs according to the specific midlatitude region. In particular, wave trains connecting the tropical Pacific and Atlantic may be associated with temperature anomalies in North America and Siberia. Arctic seas are markedly important for Eurasian regions. Analysis of synoptic temperature extremes suggests that pre-existing local temperature anomalies play a key role in the development of those extremes, as well as amplification of large-scale wave trains. We also demonstrate that warm Arctic regions can create cold outbreaks in both Siberia and North America.

Vliyaniye techeniy i atmosfernoy tsirkulyatsii na effektivnost' promysla khamsy v period yeye zimovki u Chernomorskogo poberezh'ya Krasnodarskogo kraya

Russian fishermen harvest European anchovy primarily off the Black Sea coast of the Krasnodar Territory during its wintering and wintering migrations. At wintering grounds, temperature conditions become a secondary factor in determining the behaviour of commercial concentration of European anchovy, with wind and currents being the primary factors. Therefore, the aim of this work is to determine the potential use of daily data on water circulation and local atmospheric transport in short-term (1–7 days) forecasting of European anchovy fishing in the Black Sea. The research used the European anchovy fishery monitoring materials for January – March 2019, as well as daily maps of the Black and Azov Seas level anomalies (from satellite altimetry data) and surface atmospheric pressure and temperature in Europe (analysis) for the mentioned period. The dynamics of the catch rate and its relation to altimetry and atmospheric transport indicators in the north-eastern part of the Black Sea were investigated using graphical and correlation methods. This analysis showed that the main factor contributing to increased catches is intensification of northwest currents in the coastal 60-km zone. The effect of atmospheric transport on fishing efficiency depends on the mesoscale eddy structure of the nearshore current field. In the presence of an intense northwest current in the fishing area, southwest atmospheric transports have a positive effect on fishing, while in the presence of an anticyclonic meander of currents, northeast atmospheric transports become effective. The presence of maximum significant relationships when the determinants of fishing performance are shifted by 1–7 days allows making short-term predictions of fishing efficiency.

Simulating sub-daily hydrological process with SWAT: a review

ABSTRACT The Soil and Water Assessment Tool (SWAT) is a watershed-scale hydrologic model that integrates water quantity and quality modules. Despite the large amount of knowledge on the SWAT model, specific understanding of sub-daily applications remains limited. In this review, we identify the shortcomings and possible ways forward in simulating sub-daily processes with the model. A literature review was conducted, along with a participatory method based on a questionnaire. We reviewed 28 scientific articles and categorized them into: (i) model development, (ii) streamflow methods comparison, (iii) water quality, and (iv) other applications. We found that using sub-daily data improves hydrograph peak simulation, while for medium flows use of daily data was better. From all the reviewed studies, a 1-hour time step was the most suitable time scale for the sub-daily model application. The participatory questionnaire confirmed the hypothesis that the main challenge for using the sub-daily routine was the lack of high-resolution data.

Futures-based forecasts: How useful are they for oil price volatility forecasting?

Oil price volatility forecasts have recently attracted the attention of many studies in the energy finance field. The literature mainly concentrates its attention on the use of daily data, using GARCH-type models. It is only recently that efforts to use more informative intra-day data to forecast oil price realized volatility have been made. Despite all these previous efforts, no study has examined the usefulness of futures-based models for oil price realized volatility forecasting, although the use of such models is extensive for oil price predictions. This study fills this void and shows that futures-based forecasts based on intra-day data provide informative forecasts for horizons that span between 1-day and 66-days ahead. More importantly, these results hold true even during turbulent times for the oil market, such as the Global Financial Crisis of 2007–09 and the oil collapse period of 2014–15.

An Analysis of the Daily Changes in US Treasury Security Yields

This paper analyzes the dynamics of long-term US Treasury security yields from a Keynesian perspective using daily data. Keynes held that the short-term interest rate is the main driver of the long-term interest rate. In this paper, the daily changes in long-term Treasury security yields are empirically modeled as a function of the daily changes in the short-term interest rate and other important financial variables to test Keynes’s hypothesis. The use of daily data provides a long time series. It enables the extension of earlier Keynesian models of Treasury security yields that relied on quarterly and monthly data. Models based on higher-frequency daily data from financial markets — such as the ones presented in this paper — can be valuable to investors, financial analysts, and policymakers because they make it possible for a real-time fundamental assessment of the daily changes in long-term Treasury security yields based on a wide range of financial variables from a Keynesian perspective. The empirical findings of this paper support Keynes’s view by showing that the daily changes in the short-term interest rate are the main driver of the daily changes in the long-term interest rate on Treasury securities. Other financial variables, such as the daily changes in implied volatility of equity prices and the daily changes in the exchange rate, are found to have some influence on Treasury yields.

Duration Dependence, Behavioral Restrictions, and the Market Timing Ability of Commodity Trading Advisors

AbstractThis paper addresses a potential shortcoming in the work on the market timing ability of fund managers. We adapt the Henriksson‐Merton (1981) test for market timing by relaxing a behavioral assumption that is implicit in the use of daily data. To this end, we relax the assumption that managers base their market timing decisions on daily excess returns. Instead, we use results from the literature on bull and bear markets and test whether fund managers can successfully time such trends in financial markets. We make use of a proprietary dataset of daily Commodity Trading Advisors (CTAs) returns to show that CTAs, on average, are able to time the bull and bear markets we identify.

Air temperature in Novaya Zemlya Archipelago and Vaygach Island from 1832 to 1920 in the light of early instrumental data

ABSTRACTIn this article, the results of an investigation into the air temperature conditions in Novaya Zemlya Archipelago and Vaygach Island (NZR) from 1832 to 1920, on the basis of all available early instrumental data gathered during exploratory and scientific expeditions, are presented. Traditional analysis based on mean monthly data was supplemented by an approach less popular in the scientific literature, i.e. the additional use of daily data. Moreover, the daily data used were not limited only to mean daily air temperature, but include also maximum daily temperature, minimum daily temperature and diurnal temperature range. Such rich sets of data allowed for more comprehensive and precise recognition of air temperature conditions in the NZR. Based on these kinds of daily data, it was also possible to calculate the number of so‐called ‘characteristic days’ (i.e. the number of days with temperatures exceeding specified thresholds) and day‐to‐day temperature variability and, for the first time, to determine different characteristics of thermal seasons (duration, onset and end dates) according to Baranowski's (1968) proposition. The results were compared with contemporary temperature conditions (1981–2010) to estimate the range of their changes between historical and present times.Analysis reveals that in 1832–1920, the NZR was markedly colder than today in all seasons. Coldest was autumn (on average by ca 5 °C), and least – summer (by 1.6 °C). Mean annual air temperature was colder than today by about 3 °C. The majority of mean monthly air temperatures in historical times lie within two standard deviations from the modern mean. This means that values of air temperature in historical times lie within the range of contemporary air temperature variability. Different air temperature characteristics calculated on the basis of daily data for the NZR for historical/contemporary periods also confirm the occurrence of climate warming between the studied periods.

A process-oriented perspective examining the relationships among daily coping, stress, and affect

We assess the daily relationships between age, three functional coping strategies, and positive and negative affect while accounting for the individual's cognitive appraisal of the severity and controllability of their encountered stressor. We collected 56days of daily data from participants in the Notre Dame Study of Healthy & Well-Being assessing affect, the most bothersome event experienced each day, and coping strategies used to deal with that event (N=371; Age 44–87; M=67.41; SD=8.87). Multi-level modeling allowed us to explore and compare the between- and within-person effects. The main effects revealed that coping strategies relate to affect differently. The interaction terms revealed that the effectiveness of each coping strategy depended on characteristics of the encountered stressor and/or characteristics of the individual. Average values of stress severity, Altering the Meaning, and Dispelling the Effects more strongly related to PA or NA than daily fluctuations on these constructs. Findings illustrate that certain coping strategies target affect differently. Approaching research questions regarding stress and coping with a process-oriented perspective, through the use of daily data, allows for a more thorough understanding of the real-time, lived relationships among the individual, stress, and coping.

Do foreign institutional investors herd in emerging markets? A study of individual stocks

This paper attempts to examine the presence of herd behavior among the foreign institutional investors (FIIs) when investing in Indian stock market. We also examine the impact of herding in FIIs on stock returns using panel regression approach. We apply the measure suggested by Lakonishok et al. (J Financ Econ 32(1):23–43, 1992), hereafter referred as LSV, to detect the buy-side and the sell-side herding in the Indian stock market. In order to find out intertemporal trading pattern of FIIs, we also use SIAS model. This paper makes use of daily data of 50 stocks of S&P CNX Nifty to achieve the goals. The study also tries to develop a volume-based herding ratio to extend the findings. The results support the evidence of presence of both buy-side and sell-side herding in the Indian stock market. The results based on panel regression approach highlights the destabilizing effect of FIIs in the Indian stock market that may be an issue of concern for the regulators.

Impacts of Climate Variables on Residential Water Consumption in the Czech Republic

The paper investigates whether there is a statistically significant impact of short-term climate variables (specifically air temperature and rainfall) on residential water consumption at two selected case sites in the Czech Republic. The analysis is based on a unique time series of daily data from 2004–2009. The statistical methods used are CART methodology and a decomposition of these time series based on a locally weighted regression method. Apart from the data analysis results, the investigation raises several methodological questions regarding the use of daily data and the scope of analysis based on such data sets.

Forecasting Volatility of USD/MUR Exchange Rate using a GARCH (1,1) model with GED and Student’s-t errors

This paper aims at evaluating volatility forecasts for the US Dollar/Mauritian Rupee exchange rate obtained via a GARCH (1,1) model under two distributional assumptions: the Generalized Error Distribution (GED) and theStudent’s-t distribution. We make use of daily data to evaluate the parameters of each model and produce volatility estimates. The forecasting ability is subsequently assessed using the symmetric loss functions which are the Mean Absolute Error(MAE) and Root Mean Square Error (RMSE). The latter show that both distributions may forecast quite well with a slight advantage to the GARCH(1,1)- GED for out-of-sample forecasts.Key words and phrases: Volatility, GARCH, Student-t, GED, MAE, RMSE.

SWAT application to estimate design runoff curve number for South Korean conditions

AbstractRunoff estimations based on the standard USDA–NRCS curve number (CN) table without calibration have a tendency to give inaccurate results when the CN values are applied in South Korea which has many high slope watersheds and that has a continental monsoon climate. Particularly for the design flood estimation, accurately calibrated CN values are required because the estimated peak flow is very sensitive to the selection of CN. However, the lack of flood data makes it difficult to calibrate and assign runoff CNs to Korean watersheds. Even if sufficient data are available to estimate CN values, it is also difficult to obtain the direct flows by separating base flows from total runoff hydrographs due to the temporal irregularity of rainfall events and the resulting complex pattern of runoff. Therefore, an alternative method for estimating CNs needs to be developed to overcome these issues.The purpose of this study is to present a method for estimating runoff CNs using the soil and water assessment tool (SWAT) model which can take into account watershed heterogeneities such as climate conditions, land use and soil types. The proposed CN estimation method uses the simulated flow data by SWAT instead of using measured flow data. This method has advantages in estimating CN values spatially for each subbasin division considering watershed characteristics. The use of daily data can reduce the sensitivity to the abnormality that is commonly involved in flow data with a small time scale. The SWAT‐based CN estimation method, combined with the asymptotic CN method, was applied to the Chungju dam watershed in South Korea. A regression equation was then developed from this approach, which was used to estimate CN values that decrease exponentially as rainfall amounts increase and that converge to 60·6 and 79·4 without and with considering subsurface lateral flow, respectively. Furthermore, the CN values for the antecedent moisture conditions were determined using the probabilistic approach. The CN associated with the 50% probability for the Chungju dam watershed is 87·8 which can be taken to be representative of antecedent moisture condition (AMC) II. The CNI and CNIII associated with 90% and 10% probabilities are 78·9 and 94·1, respectively. The estimated CNII = 87·8 differs markedly from the geographic information system (GIS)‐based CN 65·0, which implies that the standard USDA–NRCS CN method should be calibrated to the studied area of interest. Copyright © 2010 John Wiley & Sons, Ltd.

Factors Affecting the Credit Spreads Behaviour of USD Malaysian Bonds

This paper addresses empirical analysis of Malaysian credit spreads in a number of directions. Firstly, the investigation of explanatory power of macroeconomic or market variables to the changes in the spreads. Secondly, use of daily data rather than data sampled to match typical macroeconomic data release. Third, a focused study on the market behaviour of bonds issued from a rapidly emerging market. Fourth, the inclusion of semi-parametric measures to better capture the behaviour of the credit spreads. This study finds that changes in credit spread of Malaysian bonds are only receptive to certain macroeconomic factors. Also changes in credit spreads are negatively correlated with the interest factor but this study could not find convincing evidence to support the argument of a negative relationship with the asset factor.

The evolution and retreat features of the summer monsoon over India

AbstractThe motivation for this study stems from the fact that there are no uniform criteria to identify the onset of the summer monsoon at a particular location. Furthermore, proper understanding of the events that culminate in the onset of the monsoon is crucial to allow its prediction over various time scales. An attempt is made to elucidate the characteristics of the onset and retreat of the monsoon using a global data assimilation and forecast system. For this purpose, the time series of net tropospheric large‐scale budgets of kinetic energy, heat and moisture are examined over the Arabian Sea, Bay of Bengal and some land locations in India. The study makes use of daily data comprising operational analysis (0000 UTC) and forecasts (day1 through day5) produced by the National Centre for Medium Range Weather Forecasting, India, for the summer season of 1994.The sequence of events associated with the advent of the monsoon is noticeably different at various locations. The horizontal fluxes of heat and moisture produce a divergence regime prior to the evolution and change to convergence during the onset of monsoon over the Arabian Sea. Similarly, intense diabatic cooling is noticed prior to the onset of monsoon and changes to heating subsequently. On the other hand, heat and moisture fluxes remain in the convergence regime well before the arrival of the monsoon over the Bay of Bengal. In turn, diabatic heating is noticed prior to the onset here. Onset characteristics at Bombay and Nagpur are similar to those of the Arabian Sea. However, features at Calcutta are identical to those over the Bay of Bengal. Further, the budgets of kinetic energy, heat and moisture depict monotonic decrease at various land locations corresponding to the retreat. Interestingly, the signature of retreat is very similar at the various locations considered here. These changes are observed in the analysis and further corroborated by the model forecasts. Despite the systematic biases, the model captures the essential signature of the onset at various forecast ranges. Copyright © 2005 Royal Meteorological Society

Exchange rate and stock prices in Japan

This paper explores whether export intensity and net foreign position of the Japanese firms are carefully watched by investors and are properly reflected in the stock prices. By estimating a multifactor model including the TOPIX, the call rate, the exchange rate, and other variables representing the characteristics of individual firms, the market efficiency of the Japanese stock market has been examined. Novelty of this paper is in that the channels of the effect of exchange rate on stock prices are explicitly formulated and estimated directly, and in that the use of daily data enables knowledge to be gained on the market efficiency. The main results are as follows: (i) Japanese investors adequately consider the characteristics of the firms, such as the exporting behaviour and net foreign position. (ii) The market efficiency of the semistrong form has been improved throughout the period. (iii) Stock investors correctly evaluate firms’ foreign asset position and appropriately respond to the change of the e...

U.S. multinationals and the home bias puzzle: an empirical analysis

While international portfolio investment has become much more common among U.S. investors, prior research has shown that they continue to exhibit a home bias in equity holdings. Hence, to gain the potential benefits of international diversification, they must turn to U.S. multinationals (MNCs), among other alternatives. The literature, however, contains mixed evidence on this, leading us to reexamine the issue here through the use of daily data covering the period of January 31, 1995, through May 31, 2001. Various tests applied to dollar returns for major U.S. and international stock market indices show weak correlations and no long-term cointegrating relationships between the U.S. sets and the foreign indices that exclude U.S. and Canadian stocks. The strong results obtained here imply that U.S. investors' portfolios can benefit from international diversification. To see if U.S. MNCs can provide such a benefit, a portfolio based on dollar returns of 47 U.S. multinationals, as well as a base portfolio represented by the broad S&P 500 index, are subjected to correlation tests vis-à-vis non-U.S. stock indices. Again, the findings provide strong evidence in favor of an internationally diversified portfolio. It appears that U.S. MNCs are not the reason for the continued existence of the home bias puzzle.