scholarly journals Inference robustness of ARIMA models under non-normality —Special application to stock price data

Metrika ◽  
1979 ◽  
Vol 26 (1) ◽  
pp. 43-56 ◽  
Author(s):  
J. Ledolter
Keyword(s):  
Stock Price ◽  
Arima Models ◽  
Price Data ◽  
Inference Robustness

The cost of presidential impeachment to politically connected firms

2019 ◽  
Vol 21 (2) ◽  
pp. 109-121
Author(s):  
Ha Na Lee ◽  
B. K. Song
Keyword(s):  
Stock Returns ◽  
Stock Prices ◽  
Stock Price ◽  
Constitutional Court ◽  
Korean History ◽  
South Korean ◽  
Price Data ◽  
Political Events ◽  
Abnormal Stock Returns ◽  
The Cost

AbstractThis study examines the ways political events can affect the stock prices of politically connected firms by studying one of the biggest corruption scandals in modern South Korean history, which led to the first-ever impeachment of a sitting president. We analyzed the stock returns of firms that donated money to foundations allegedly controlled by the president's confidante. We found that the abnormal stock returns of politically connected firms decreased when the president was removed from office. Using tick-by-tick stock price data, we were able to pinpoint the exact moments when the stock prices of firms that donated money fluctuated, as the president's fate was determined by the justices of the Constitutional Court.

scholarly journals MULTIVARIATE HEAVY-TAILED MODELS FOR VALUE-AT-RISK ESTIMATION

2012 ◽  
Vol 15 (04) ◽  
pp. 1250029 ◽  
Author(s):  
CARLO MARINELLI ◽  
STEFANO D'ADDONA ◽  
SVETLOZAR T. RACHEV
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Stock Price ◽  
Risk Estimation ◽  
Price Data ◽  
Heavy Tailed Distributions ◽  
Derivative Instruments ◽  
Heavy Tailed ◽  
Student’S T

For purposes of Value-at-Risk estimation, we consider several multivariate families of heavy-tailed distributions, which can be seen as multidimensional versions of Paretian stable and Student's t distributions allowing different marginals to have different indices of tail thickness. After a discussion of relevant estimation and simulation issues, we conduct a backtesting study on a set of portfolios containing derivative instruments, using historical US stock price data.

On Determining the Dimension of Real-Time Stock-Price Data

1992 ◽  
Vol 10 (3) ◽  
pp. 367 ◽  
Author(s):  
E. Scott Mayfield ◽  
Bruce Mizrach
Keyword(s):  
Real Time ◽  
Stock Price ◽  
Price Data

scholarly journals Peramalan dan deteksi outlier saham perusahaan angkutan laut umum di masa covid-19 dengan pemodelan arima

2021 ◽  
Vol 3 (1) ◽  
pp. 8
Author(s):  
Ilham Thaib ◽  
Gesit Thabrani ◽  
Silvia Netsyah
Keyword(s):  
Confidence Interval ◽  
Outlier Detection ◽  
Autocorrelation Function ◽  
Stock Price ◽  
Arima Model ◽  
Statistical Test ◽  
P Value ◽  
The Public ◽  
Price Data ◽  
Partial Autocorrelation Function

The public sea freight sector is one of the affected by COVID-19. PT. Samudera Indonesia Tbk is one of the sea transportations companies in Indonesia. The ARIMA model in the previous study provided a statistical test with the aim of evaluating the suitability of the model with a p value of less than 0.05 to determine ARIMA by guessing through ACF (Autocorrelation Function) and PACF (Partial Autocorrelation Function) through stationary data. Outlier detection can be done by plotting the residuals from the specified model. Forecasting data for the next 5 days using the ARIMA (3,1,2) model can be seen that the results of forecasting stock price data for PT. Samudera Indonesia Tbk using ARIMA (3,1,2) is within the 95% confidence interval with a forecast value that is close to the actual value. There are outliers that are detected which are related to economic phenomena.Keywords: Forecasting, Covid-19, stock, ARIMA, outlier

scholarly journals Analisis Sell in May and Go Away di Bursa Efek Indonesia dan Malaysia Periode 2017-2019

2021 ◽  
Vol 9 (1) ◽  
pp. 311
Author(s):  
Laila Marta Zarika ◽  
R.A. Sista Paramita
Keyword(s):  
Hypothesis Testing ◽  
Stock Price ◽  
Abnormal Return ◽  
Stock Exchange ◽  
T Test ◽  
The Other ◽  
Price Movement ◽  
Paired Sample ◽  
Price Data ◽  
The Difference

In May and Go Away (SMGA), Sell is a type of seasonal Anomaly, which historically originated in Europe and America that between May-October returns lower than the other periods from November to April. This research aims to determine the difference in abnormal return in the May-October (Worst period) period and November-April (Best period) in Indonesia and Malaysia Stock Exchange between 2017 to 2019. This test conducted using the company's stock price data samples listed on the LQ45 index in the Indonesia Stock Exchange and the FBMKLCI index in the Malaysia Stock Exchange period 2017 to 2019. Hypothesis testing using paired sample t-test to answer if there is a difference in return between the best period and the worst period, to prove the Sell's existence in May and Go Away. The results showed no difference returns between the best and worst periods in the Sell in May and Go Away phenomenon at the Indonesia and Malaysia Stock Exchange period 2017 to 2019. The Investor considers SMGA as not a phenomenon containing excellent or bad information that is capable of affecting the price movement of shares so that SMGA as a strategy to buy stocks in the best period and sell in the worst period is no longer relevant

scholarly journals Bayesian estimation of the fractional Ornstein-Uhlenbeck instantaneous rate of asset return process: Evidence from high-frequency stock price data

2016 ◽  
Author(s):  
Theodore Simos ◽  
Mike Tsionas
Keyword(s):  
Monte Carlo ◽  
High Frequency ◽  
Stock Price ◽  
Sampling Interval ◽  
Optimal Choice ◽  
Computational Statistics ◽  
Instantaneous Rate ◽  
Asset Return ◽  
Price Data ◽  
Return Process

Using recent developments in econometrics and computational statistics we consider the estimation of the instantaneous rate of asset return process when the underlying Data Generating Mechanism (DGM) is an Ornstein-Uhlenbeck process, driven by fractional noise, and sampled at fixed intervals of length h. To address the problem we adopt throughout the paper an exact discretization approach. This enable us to exploit the fact that a flow sampling scheme arises naturally when observing the DGM. For, while the instantaneous rate of return process is unobservable at points in time, its time integral over successive observations is observable since it equals the increment of log-prices. Exact discretization delivers an ARIMA(1,1,1) model for log-prices with a fractional driving noise. Building on the resulting exact discretization formulae and covariance function, a new Markov Chain Monte Carlo (MCMC) scheme is proposed and we examine the properties of both the time and frequency domain likelihoods / posteriors through Monte Carlo. For the exact discrete model we adopt a general sampling interval of length h. This allow us to determine the optimal choice of h independent of the sample size. An empirical application using high frequency stock price data is presented showing the relevance of aggregation over time issues in modelling asset prices.

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