scholarly journals An Algorithmic Look at Financial Volatility

Algorithms ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 185 ◽  
Author(s):  
Lin Ma ◽  
Jean-Paul Delahaye
Keyword(s):  
Kolmogorov Complexity ◽  
Financial Data ◽  
Stylized Facts ◽  
Volatility Clustering ◽  
Financial Volatility ◽  
Universal Distribution ◽  
Daily Data ◽  
Auto Regressive ◽  
Binary Strings

In this paper, we attempt to give an algorithmic explanation to volatility clustering, one of the most exploited stylized facts in finance. Our analysis with daily data from five exchanges shows that financial volatilities follow Levin’s universal distribution Kirchherr et al. (1997) once transformed into equally proportional binary strings. Frequency ranking of binary trading weeks coincides with that of their Kolmogorov complexity estimated byDelahaye et al. (2012). According to Levin’s universal distribution, large (resp. small) volatilities are more likely to be followed by large (resp. small) ones since simple trading weeks such as “00000” or “11111” are much more frequently observed than complex ones such as “10100” or “01011”. Thus, volatility clusters may not be attributed to behavioral or micro-structural assumptions but to the complexity discrepancy between finite strings. This property of financial data could be at the origin of volatility autocorrelation, though autocorrelated volatilities simulated from Generalized Auto-Regressive Conditional Heteroskedacity (hereafter GARCH) cannot be transformed into universally distributed binary weeks.

Asymmetric heavy-tailed vector auto-regressive processes with application to financial data

2019 ◽  
Vol 90 (2) ◽  
pp. 324-340 ◽  
Author(s):  
Mohsen Maleki ◽  
Darren Wraith ◽  
Mohammad R. Mahmoudi ◽  
Javier E. Contreras-Reyes
Keyword(s):  
Financial Data ◽  
Auto Regressive ◽  
Heavy Tailed

Do Global Financial, Oil and Gold Volatility Shocks Affect the GCC Stock Markets?

2019 ◽  
Vol 5 (2) ◽  
pp. 157-175 ◽  
Author(s):  
Abdullah Alqahtani
Keyword(s):  
Saudi Arabia ◽  
Stock Market ◽  
Stock Markets ◽  
Structural Var ◽  
Causality Test ◽  
Data Set ◽  
Financial Volatility ◽  
Daily Data ◽  
Positive Shock ◽  
The Impact

This study employed the non-structural VAR econometrics approach to examine the impact of Global Oil (OVX), Financial (VIX), and Gold (GVZ) volatility indices on GCC stock markets using a daily data set spanning from January 5, 2009 to August 16, 2018. From the VAR result obtained, disequilibrium in the global financial volatility (VIX) was able to significantly transmit negative shock to Bahrain and Kuwait stock markets and positive shock on GVZ. While the global Gold volatility was capable of transmitting fairly positive shock to the UAE and VIX market. The OLS also revealed more to the result obtained from VAR as it shows that OVX and VIX can have impact on the GCC stock markets. The causality test revealed that there is a unidirectional causality running from Qatar and UAE to OVX; none of the variables was able to granger cause VIX, while unidirectional causality exist from VIX and UAE to GVZ and VIX and Qatar to Bahrain. VIX and Qatar can granger cause Kuwait stock market, and only Saudi Arabia and Oman have bidirectional causality. Unidirectional causality exists from Saudi Arabia to Qatar, and Qatar is the only stock market capable of causing UAE unidirectionally. Hence, the study concludes that VIX and GVZ are capable of transmitting shocks to three of the six GCC stock markets—(Bahrain, Kuwait and The UAE) negatively (Bahrain and Kuwait) and positively (The UAE). And on this note, the study recommends that appropriate financial and gold transaction policies should be institutionalized so as to mitigate the transmission of shocks into the markets. Also, financial and gold experts who regulate the stock and gold markets especially in Bahrain and Kuwait should watch for any abnormality changes in the volatility movement of the financial and gold markets.

scholarly journals Bitcoin Analysis and Forecasting through Fuzzy Transform

Axioms ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 139
Author(s):  
Maria Letizia Guerra ◽  
Laerte Sorini ◽  
Luciano Stefanini
Keyword(s):  
Time Series ◽  
Sentiment Analysis ◽  
Test Statistics ◽  
Local Smoothing ◽  
Fuzzy Transform ◽  
Short Term ◽  
Stylized Facts ◽  
Lp Norm ◽  
Daily Data ◽  
Short Term Forecasting

Sentiment analysis to characterize the properties of Bitcoin prices and their forecasting is here developed thanks to the capability of the Fuzzy Transform (F-transform for short) to capture stylized facts and mutual connections between time series with different natures. The recently proposed Lp-norm F-transform is a powerful and flexible methodology for data analysis, non-parametric smoothing and for fitting and forecasting. Its capabilities are illustrated by empirical analyses concerning Bitcoin prices and Google Trend scores (six years of daily data): we apply the (inverse) F-transform to both time series and, using clustering techniques, we identify stylized facts for Bitcoin prices, based on (local) smoothing and fitting F-transform, and we study their time evolution in terms of a transition matrix. Finally, we examine the dependence of Bitcoin prices on Google Trend scores and we estimate short-term forecasting models; the Diebold–Mariano (DM) test statistics, applied for their significance, shows that sentiment analysis is useful in short-term forecasting of Bitcoin cryptocurrency.

scholarly journals Aplikasi GARCH dalam Mengatasi Volatilitas Pada Data Keuangan

2017 ◽  
Vol 7 (2) ◽  
pp. 107
Author(s):  
, Hartati ◽  
Imelda Saluza
Keyword(s):  
Time Series ◽  
Garch Model ◽  
Practical Experience ◽  
Financial Data ◽  
Demand And Supply ◽  
Volatility Clustering ◽  
Wide Range ◽  
One Year ◽  
The Garch Model

The financial market is a place or means convergence between demand and supply of a wide range of financial instruments Long-term (over one year). Activities that occur in the financial markets in the long term will form a series of data is often called a time series that contains a set of information from time to time. Practical experience shows that many time series exhibit their periods with great volatility. The greater the volatility, the greater the chance to experience a gain or loss. Important properties are often owned by the data time series in finance, especially to return data that the probability distribution of returns are fat tails (tail fat) and volatility clustering or often referred to as a case heteroskedastisitas. Not all models are able to capture the nature of heteroscedasticity, one of the models that are able to do is Generalized Autoregressive Heteroskedasticity Condition (GARCH). So the purpose of this study was to determine the GARCH model in dealing with the volatility that occurred in the financial data. The results showed that the GARCH model is best suited to see volatility in the financial data.

Calculation of VaR with the Bootstrap Method Based on Experts Predict in Mechanical Engineering

2013 ◽  
Vol 681 ◽  
pp. 60-64
Author(s):  
Xia Shi
Keyword(s):  
Risk Management ◽  
Empirical Research ◽  
Predictive Value ◽  
Bootstrap Method ◽  
Composite Index ◽  
Financial Data ◽  
Short Term ◽  
Volatility Clustering ◽  
Auto Correlation ◽  
The Bootstrap Method

Based on GARCH model to catch the financial data of auto correlation and volatility clustering, while the use of expert predictive value and the short term newer data using the Bootstrap method for Vary estimation. Through the SSE Composite Index of empirical research, the results show that this method can avoid the old data invalid information, at the same time the financial experts forecast was introduced risk management, improve the accuracy of the computation

ON THE QUANTUM KOLMOGOROV COMPLEXITY OF CLASSICAL STRINGS

2009 ◽  
Vol 07 (04) ◽  
pp. 701-711 ◽  
Author(s):  
MARKUS MÜLLER
Keyword(s):  
Kolmogorov Complexity ◽  
Quantum Computer ◽  
Mathematical Proof ◽  
Minimal Length ◽  
Additive Constant ◽  
Quantum Operations ◽  
Universal Quantum ◽  
Probabilistic Error ◽  
Quantum Complexity ◽  
Binary Strings

We show that classical and quantum Kolmogorov complexity of binary strings agree up to an additive constant. Both complexities are defined as the minimal length of any (classical resp. quantum) computer program that outputs the corresponding string. It follows that quantum complexity is an extension of classical complexity to the domain of quantum states. This is true even if we allow a small probabilistic error in the quantum computer's output. We outline a mathematical proof of this statement, based on an inequality for outputs of quantum operations and a classical program for the simulation of a universal quantum computer.

scholarly journals PEMODELAN AUTOREGRESSIVE FRACTIONALLY INTEGRATED MOVING AVERAGE DENGAN EFEK EXPONENTIAL GARCH (ARFIMA-EGARCH) UNTUK PREDIKSI HARGA BERAS DI KOTA SEMARANG

2021 ◽  
Vol 10 (2) ◽  
pp. 279-292
Author(s):  
Rezky Dwi Hanifa ◽  
Mustafid Mustafid ◽  
Arief Rachman Hakim
Keyword(s):  
Time Series ◽  
Long Memory ◽  
Time Series Data ◽  
Moving Average ◽  
Financial Data ◽  
Series Data ◽  
Leverage Effect ◽  
Standard Equipment ◽  
Conditional Heteroscedasticity ◽  
Auto Regressive

Time series data is a type of data that is often used to estimate future values. Long memory phenomenon often occurs in time series data. Long memory is a condition that shows a strong correlation between observations even though they are quite far away. This phenomenon can be overcome by modeling time series data using the Autoregressive Fractional Integrated Moving Average (ARFIMA) model. This model is characterized by a fractional difference value. ARFIMA (Autoregressive Fractional Integrated Moving Average) model assumes that the residuals are normally distributed, mutually independent, and homogeneous. However, usually in financial data, the residual variants are not constant. This can be overcome by modeling variants. Standard equipment that can be used to model variants is the ARCH / GARCH (Auto Regressive Conditional Heteroscedasticity / Generalized Auto Regressive Conditional Heteroscedasticity) model. Another phenomenon that often occurs in GARCH models is the leverage effect on the residuals of the model. EGARCH (Exponential General Auto Regessive Conditional Heteroscedasticity) is a development of the GARCH model that is appropriate for data that has an leverage effect. The implementation of this model is by modeling financial data, so this study takes 136 monthly data on rice prices in Semarang City from January 2009 to April 2020. The purpose of this study is to create a long memory data forecasting model using the Exponential method. Generalized Autoregressive Conditional Heteroscedasticity (EGARCH). The best model obtained is ARFIMA (1, d, 1) EGARCH (1,1) which is capable of forecasting with a MAPE value of 3.37%.Keyword : Rice price, forecasting , long memory, leverage effect, GARCH, EGARCH

Short-term prediction model for daily COVID-19 reported positive cases in Senegal

2021 ◽  
Vol 8 (1) ◽  
pp. 1111-1126
Author(s):  
Aba Diop ◽  
Abdourahmane Ndao ◽  
Cheikh Tidiane Seck ◽  
Ibrahima Faye
Keyword(s):  
Prediction Model ◽  
Moving Average ◽  
Arima Model ◽  
Short Term ◽  
Ministry Of Health ◽  
Term Prediction ◽  
Daily Data ◽  
Auto Regressive ◽  
Short Term Prediction ◽  
Second Wave

In this work, we use an Auto-Regressive Integrated Moving Average (ARIMA) model to study the evolution of COVID-19 disease in Senegal and then make short-term predictions about the number of people likely to be infected by the coronavirus. We are dealing with daily data provided by the Senegalese Ministry of Health during the period from March 2, 2020 to March 2, 2021.Our results show that the peak of the disease appearsduring the second wave seems to be reached on February 12 2021. But they also show that the number of COVID-19 infections will be around 200 cases per day during the next 30 days if the trend of the total number of tests performed is maintained.

Short-term prediction model for daily COVID-19 reported positive cases in Senegal

2021 ◽  
Vol 8 (1) ◽  
pp. 1507-1523
Author(s):  
Aba Diop ◽  
Abdourahmane Ndao ◽  
Cheikh Tidiane Seck ◽  
Ibrahima Faye
Keyword(s):  
Prediction Model ◽  
Moving Average ◽  
Arima Model ◽  
Short Term ◽  
Ministry Of Health ◽  
Term Prediction ◽  
Daily Data ◽  
Auto Regressive ◽  
Short Term Prediction ◽  
Second Wave

In this work, we use an Auto-Regressive Integrated Moving Average (ARIMA) model to study the evolution of COVID-19 disease in Senegal and then make short-term predictions about the number of people likely to be infected by the coronavirus. We are dealing with daily data provided by the Senegalese Ministry of Health during the period from March 2, 2020 to March 2, 2021.Our results show that the peak of the disease appearsduring the second wave seems to be reached on February 12 2021. But they also show that the number of COVID-19 infections will be around 200 cases per day during the next 30 days if the trend of the total number of tests performed is maintained.

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