Changes in precipitation rates over the last 70 years in Eastern Paraná State, Brazil

Temporal hydrometeorological series may present variations over time. The awareness of these characteristics is important to improve the monitoring of changes that these series may suffer along time. In this regard, the present paper aims to identify the existence of precipitation trends for the east portion of Paraná state, in Brazil, and also investigate the changes in the observed rates over the last 70 years. The statistical tests of Mann-Kendall and Pettitt, and also the Theil-Sen estimator, are applied for series of precipitation from 13 pluviometric stations of eastern Paraná state, Brazil, with 70 years of data records. By the results it was identified long-term linear positive trend for 11 of the precipitation series and also detected medium-term patterns in precipitation over 10 stations, characterizing the Joseph effect. These series have presented a behavior with higher rates in the most recent years in comparison from the first years of the historical data, sectioning the complete series into two shorter stationary periods, and presenting an abrupt change point.

Long memory investigation during demonetization in India

Long-range dependence (LRD) in financial markets remains a key factor in determining whether there is market memory, herding traces, or a bubble in the economy. Usually referred to as ‘Long Memory’, LRD has remained a key parameter even today since the mid-1970s. In November 2016, a sudden and drastic demonetization measure took place in the Indian market, aimed at curbing money laundering and terrorist funding. This study is an attempt to identify market behavior using long-range dependence during those few days in demonetization. Besides, it tries to identify nascent traces of bubble and embedded herding during that time. Auto Regressive Fractionally Integrated Moving Average (ARFIMA) is used for three consecutive days around the event. Tick-by-tick data from CNX Nifty High Frequency Trading (CNX Nifty HFT) is used for three consecutive days around demonetization (approximately, 5000 data points from morning trading sessions on each of the three days). The results show a clear and profound presence of herd behavior in all three data sets. The herd intensity remained similar, indicating a unique mixture of both ‘Noah Effect’ and ‘Joseph Effect’, proving a clear regime switch. However, the results on the event day show stable and prominent herding. Mandelbrot’s specified effects were tested on an uncertain and sudden financial event in India and proved to function perfectly.