scholarly journals Dynamic cyber risk estimation with competitive quantile autoregression

2022 ◽  
Author(s):  
Raisa Dzhamtyrova ◽  
Carsten Maple
Keyword(s):  
Value At Risk ◽  
Arrival Time ◽  
Time Series Data ◽  
Risk Estimation ◽  
Series Data ◽  
Cyber Attack ◽  
Significance Level ◽  
Confidence Levels ◽  
Quantile Autoregression ◽  
Cyber Risk

AbstractThe increasing value of data held in enterprises makes it an attractive target to attackers. The increasing likelihood and impact of a cyber attack have highlighted the importance of effective cyber risk estimation. We propose two methods for modelling Value-at-Risk (VaR) which can be used for any time-series data. The first approach is based on Quantile Autoregression (QAR), which can estimate VaR for different quantiles, i. e. confidence levels. The second method, we term Competitive Quantile Autoregression (CQAR), dynamically re-estimates cyber risk as soon as new data becomes available. This method provides a theoretical guarantee that it asymptotically performs as well as any QAR at any time point in the future. We show that these methods can predict the size and inter-arrival time of cyber hacking breaches by running coverage tests. The proposed approaches allow to model a separate stochastic process for each significance level and therefore provide more flexibility compared to previously proposed techniques. We provide a fully reproducible code used for conducting the experiments.

scholarly journals The Role of Technical Innovation and Development of Industrial Sector in Korean International Business

2021 ◽  
Vol 22 (1) ◽  
pp. 55-73
Author(s):  
Ali Mohammed Khalel Al-Shawaf ◽  
Tahira Yasmin
Keyword(s):  
Time Series Data ◽  
Positive Impact ◽  
Generalized Method Of Moments ◽  
Value Added ◽  
Industrial Sector ◽  
Least Square ◽  
Policy Implications ◽  
Series Data ◽  
Significance Level

With the pace of development and competitiveness, innovation plays an important role to capture the market share. Various countries have effective strategies to enhance Research and Development (R&D) and exchange value added products in international market. So, based on this the aim of this research is to examine the role of R&D, industrial design and charges for intellectual property in innovative exports in South Korean economy. Time series data for the period 1998 to 2017, Ordinary Least Square (OLS) and Generalized Method of Moments (GMM) models are used to determine the dynamic interrelationship among the study variables. In summary, the overall results show that there is co-integration rank of in both trace test and value test at 1% significance level. Moreover, OLS and GMM findings depict that there is significant and positive coefficient for ID & RD which represent that they have positive impact on HT. Whereas, the IP displays a negative and significant relationship with high technology exports accordingly. Lastly, the diagnostic tests show that model is stable for the study time period and result is reliable. The current study also suggests some policy implications which can enhance innovative export products of South Korea while enhancing R&D.

The Impact of The Covid-19 Pandemic on Investor Risk Tendency: The Case of Turkey

2021 ◽  
Vol 15 (9) ◽  
pp. 3046-3049
Author(s):  
Abdulkadir Kaya
Keyword(s):  
Risk Perceptions ◽  
Time Series Data ◽  
Statistical Significance ◽  
Series Data ◽  
Causality Test ◽  
Significance Level ◽  
Risk Appetite ◽  
Central Registry ◽  
The Face ◽  
The Impact

Introduction and Aim: It is an important issue that what kind of changes occur in the risks that people face in the face of emerging problems and the role of people in possible pandemics in the last twenty years and in the future. The solution of the problems that arise in the control and management of these risks attracts the attention of many researchers. In this study, the causality effect of the COVID-19 pandemic on risk appetites representing the attitudes and behaviors of securities investors. Materials and Methods: In the study; To represent the pandemic, weekly time series data of the number of COVID-19 cases (COVID) and the Risk Appetite index (RISK) announced by the Central Registry Agency for the period 30.03.2019-30.08.2021 were used. In order to determine the causality relationship, the Hatemi-J Causality test was performed. Results: It was determined that the negative shocks of the COVID variable were a cause of the positive shocks of the RISK variable at a statistical significance level of 1%. Conclusion and Suggestions: The effect of the pandemic process on the investment decisions of the investors is reduced, with the expectation that the economy and financial markets will improve, positively affecting the behavior and risk perceptions of the investors, and this expectation causes the investment behavior and risk appetite to increase. can be expressed. Keywords: COVID-19, Risk appetite, Pandemic, Hatemi-J

scholarly journals Tsunami arrival time detection system applicable to discontinuous time-series data with outliers

2016 ◽  
Author(s):  
Jun-Whan Lee ◽  
Sun-Cheon Park ◽  
Duk Kee Lee ◽  
Jong Ho Lee
Keyword(s):  
Time Series ◽  
Arrival Time ◽  
Time Series Data ◽  
Detection System ◽  
Series Data ◽  
Gap Filling ◽  
2011 Tohoku Tsunami ◽  
Overall Performance ◽  
Tsunami Signal ◽  
Tohoku Tsunami

Abstract. Timely detection of tsunamis with water-level records is a critical but logistically challenging task because of outliers and gaps. We propose a tsunami arrival time detection system (TADS) that can be applied to discontinuous time-series data with outliers. TADS consists of three major algorithms that are designed to update at every new data acquisition: outlier detection, gap-filling, and tsunami detection. To detect a tsunami from a record containing outliers and gaps, we propose the concept of the event period. In this study, we applied this concept in our test of the TADS at the Ulleung-do surge gauge located in the East Sea. We calibrated the thresholds to identify tsunami arrivals based on the 2011 Tohoku tsunami, and the results show that the overall performance of TADS is effective at detecting a small tsunami signal superimposed on both an outlier and gap.

scholarly journals IMPACT OF PETROLEUM PRODUCT PRICE ON HUMAN WELFARE IN NIGERIA

10.23856/2906 ◽  
2018 ◽  
Vol 29 (4) ◽  
pp. 27-42
Author(s):  
Olukayode Emmanuel Maku ◽  
S. Adetayo Adetowubo-King ◽  
O., Oyelade Aduralere
Keyword(s):  
Market Failure ◽  
Inflation Rate ◽  
Time Series Data ◽  
Series Data ◽  
Product Price ◽  
Human Welfare ◽  
Dual Purpose ◽  
Significance Level ◽  
Economic Output ◽  
The Impact

The single most important issue confronting a growing number of world economies today is the price of oil and its attendant consequences on economic output. Therefore the study investigated the impact of petroleum pump price on human welfare in Nigeria over the period 1990 to 2015. The study employed expost facto research design. Secondary time series data were used for the study and these were sourced from World Development Indicator (WDI, 2015) and Central Bank of Nigeria statistical bulletin, (CBN, 2015). The data collected were analyzed using autoregressive distributed lag. The inferences were drown at 1% and 5% significance level. The result showed that premium motor spirit price and dual purpose kerosene price exert a long-run negative and significant impact on human welfare in Nigeria (β = -0.15299, t = -5.31141 and β = -0.471399, t = -1.8838 respectively) while premium motor spirit price, dual purpose kerosene price and inflation rate exert a short-run negative and significant impact on human welfare in Nigeria (β = -0.71735, t= -4.3766; β = -0.62562, t = -2.9188 and β = -0.050310, t = -2.1829 respectively). The study concluded that as premium motor spirit price and dual purpose kerosene price and inflation rate increases, human welfare will fall and vice versa. Therefore for human welfare to increase, there must be a fall in premium motor spirit price and dual purpose kerosene price and inflation rate in Nigeria. The study recommended that Government and it agencies should ensure that petroleum pump prices should be regulated because they have a long way on the market. An increase in the price of petroleum products will lead to market failure because most products use either of these products. Since inflation rate worsen the welfare of people, the policy maker should find a way of control inflation in the system so that the welfare of the people will improve (better-off).

scholarly journals Investment Risk Analysis On Bitcoin With Applied of VaR-APARCH Model

2021 ◽  
Vol 5 (1) ◽  
pp. 1
Author(s):  
Irwan Kasse ◽  
Andi Mariani ◽  
Serly Utari ◽  
Didiharyono D.
Keyword(s):  
Risk Analysis ◽  
Value At Risk ◽  
Time Series Data ◽  
Information Criterion ◽  
Series Data ◽  
Investment Risk ◽  
Conditional Heteroscedasticity ◽  
Additional Information ◽  
Asymmetric Power ◽  
Autoregressive Conditional Heteroscedasticity

Investment can be defined as an activity to postpone consumption at the present time with the aim to obtain maximum profits in the future. However, the greater the benefits, the greater the risk. For that we need a way to predict how much the risk will be borne. Modelling data that experiences heteroscedasticity and asymmetricity can use the Asymmetric Power Autoregressive Conditional Heteroscedasticity (APARCH) model. This research discusses the time series data risk analysis using the Value at Risk-Asymmetric Power Autoregressive Conditional Heteroscedasticity (VaR-APARCH) model using the daily closing price data of Bitcoin USD period January 1 2019 to 31 December 2019. The best APARCH model was chosen based on the value of Akaike's Information Criterion (AIC). From the analysis results obtained the best model, namely ARIMA (6,1,1) and APARCH (1,1) with the risk of loss in the initial investment of IDR 100,000,000 in the next day IDR 26,617,000. The results of this study can be used as additional information and apply knowledge about the risk of investing in Bitcoin with the VaR-APARCH model.

scholarly journals Non-extensive value-at-risk estimation during times of crisis

2021 ◽  
pp. 2150099
Author(s):  
Ahmad Hajihasani ◽  
Ali Namaki ◽  
Nazanin Asadi ◽  
Reza Tehrani
Keyword(s):  
At Risk ◽  
Financial Markets ◽  
Value At Risk ◽  
Risk Estimation ◽  
Specific Pattern ◽  
Gaussian Probability Density Function ◽  
Confidence Levels ◽  
Control Instrument ◽  
The Difference ◽  
Non Gaussian

Value-at-risk (VaR) is a crucial subject that researchers and practitioners extensively use to measure and manage uncertainty in financial markets. Although VaR is a standard risk control instrument, there are criticisms about its performance. One of these cases, which has been studied in this research, is the VaR underestimation during times of crisis. In these periods, the non-Gaussian behavior of markets intensifies, and the estimated VaRs by typical models are lower than the real values. A potential approach that can be used to describe the non-Gaussian behavior of return series is the Tsallis entropy framework and nonextensive statistical methods. This paper has used the nonextensive models for analyzing financial markets’ behavior during crisis times. By applying the q-Gaussian probability density function for emerging and mature markets over 20 years, we can see a better VaR estimation than the regular models, especially during crisis times. We have shown that the q-Gaussian models composed of VaR and Expected Shortfall (ES) estimate risk better than the standard models. By comparing the ES, VaR, [Formula: see text]-VaR and [Formula: see text]-ES for emerging and mature markets, we see in confidence levels more than 0.98, the outputs of q models are more real, and the [Formula: see text]-ES model has lower errors than the other ones. Also, it is evident that in the mature markets, the difference of VaR between normal condition and nonextensive approach increases more than one standard deviation during times of crisis. Still, in the emerging markets, we cannot see a specific pattern. The findings of this paper are useful for analyzing the risk of financial crises in different markets.

scholarly journals PERHITUNGAN VALUE AT RISK (VaR) DENGAN METODE HISTORIS DAN MONTE CARLO PADA SAHAM SUB SEKTOR ROKOK

2018 ◽  
Vol 11 (1) ◽  
pp. 1
Author(s):  
Achmad Dimas ◽  
Muhammad Azhari ◽  
Khairunnisa Khairunnisa
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Value At Risk ◽  
Time Series Data ◽  
Random Number Generation ◽  
Simulation Method ◽  
Series Data ◽  
Historical Simulation ◽  
Historical Method ◽  
Sample Data

The government’s policy, the Indonesian Ulema Council’s (MUI) fatwa, the rise of cigarette issues and anti-smoking campaigns have been a major challenge for the tobacco industry in managing risks. Through this research, the issues will be measured by VaR to know the risk of the company’s shares of cigarette sub sector by using time series data and analyzed by using the simulation method of Historis and Monte Carlo. The results showed the VaR value of GGRM and HMSP stock with the historical method is 3.28 and 2.54%. While the value of VaR shares GGRM and HMSP with Monte Carlo method is 3.52% and 3.14%. Monte Carlo simulation gives greater result than Historical Simulation, because Monte Carlo simulation do iteration repeatedly by involving random number generation and many synthesize the data so that sample data becomes more which makes the calculation is bigger.

Empirical Analysis of the Relationship Between Energy Consumption, CO2 Emissions and Economic Growth in Tunisia

2017 ◽  
Vol 8 (2) ◽  
pp. 79-90 ◽  
Author(s):  
Sana Essaber Jouini ◽  
Etidel Labidi
Keyword(s):  
Economic Growth ◽  
Energy Consumption ◽  
Co2 Emissions ◽  
Time Series Data ◽  
Output Growth ◽  
Pollutant Emissions ◽  
Series Data ◽  
Causality Analysis ◽  
Significance Level ◽  
Long Run

This paper examines the long run and causal relationship issues between economic growth, energy consumption and carbon emissions by using vector error correction model for the case of Tunisia within 1970-2010. Empirical results using time series data suggest an evidence of a long-run relationship between the variables at 5% significance level in Tunisia. A Granger causality analysis is conducted amongst the variables. The overall results indicate bidirectional causality between energy consumption and CO2 emissions and a unidirectional causality running from pollutant emissions to economic growth. But there is no direct relation between energy consumption and economic growth. Thus, our results reveal that in short term energy conservation policies, such as rationing energy consumption have no effect on the real output growth of Tunisia.

Trend Analysis on Precipitation Time Series Data in Munda Catchment, Pakistan

2014 ◽  
Vol 692 ◽  
pp. 97-102 ◽  
Author(s):  
Ijaz Ahmad ◽  
De Shan Tang ◽  
Mei Wang ◽  
Sarfraz Hashim
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Positive Trend ◽  
Precipitation Time Series ◽  
Significance Level ◽  
Annual Basis ◽  
Time Period ◽  
Seasonal Basis ◽  
Correlation Trend

This paper investigates the trends in precipitation time series of 10 stations for the time period of 51 years (1961-2011) in the Munda catchment, Pakistan. The Mann-Kendall (MK) and Spearman’s rho (SR) tests were employed for detection of the trend on the seasonal and annual basis at 5% significance level. For the removal of the serial correlation Trend Free Pre-Whitening approach was applied. The results show, a mixture of positive (increasing) and negative (decreasing) trends. A shift in precipitation time series is observed on seasonal scale from summer to autumn season. The Charbagh station exhibits the most number of significant cases on the seasonal basis while, no significant trends are found at Thalozom, Kalam and Dir stations. On the annual basis, only Charbagh station shows a significant positive trend, while on other stations, no significant trends are found annually. The performance of MK and SR tests was consistent in detecting the trend at different stations.

scholarly journals RHMX: Bus arrival time prediction via mixed model

2021 ◽  
Vol 5 (6) ◽  
pp. 30-43
Author(s):  
Fei Jia ◽  
Huibing Zhang ◽  
Xiaoli Hu
Keyword(s):  
Mixed Model ◽  
Arrival Time ◽  
Time Series Data ◽  
Stopping Time ◽  
Intelligent Transportation ◽  
Series Data ◽  
Running Time ◽  
Time Prediction ◽  
Arrival Time Prediction ◽  
Bus Arrival Time Prediction

With the widespread use of information technologies such as IoT and big data in the transportation business, traditional passenger transportation has begun to transition and upgrade into intelligent transportation, providing passengers with a better riding experience. Giving precise bus arrival times is a critical link in achieving urban intelligent transportation. As a result, a mixed model-based bus arrival time prediction model (RHMX) was suggested in this work, which could dynamically forecast bus arrival time based on the input data. First, two sub-models were created: bus station stopping time prediction and interstation running time prediction. The former predicted the stopping time of a running bus at each downstream station in an iterative manner, while the latter projected its running time on each downstream road segment (stations as the break points). Using the two models, a group of time series data on interstation running time and bus station stopping time may be predicted. Following that, the time series data from the two sub-models was fused using long short-term memory (LSTM) to generate an approximate bus arrival time. Finally, using Kalman filtering, the LSTM prediction results were dynamically updated in order to eliminate the influence of aberrant data on the anticipated value and obtain a more precise bus arrival time. The experimental findings showed that the suggested model's accuracy and stability were both improved by 35% and 17%, respectively, over AutoNavi and Baidu.

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