Pemodelan Dan Peramalan Volatilitas Pada Return Saham Bank Bukopin Menggunakan Model Asymmetric Power Autoregressive Conditional Heteroskedasticity (Aparch)

Stock is a sign of ownership of an individual or entity within a corporation or limited liability company. While the stock price index is a reflection of the movement of the stock price. Stock investments can not avoid the risk, so we need a model that can predict stock returns and volatility. Models are often used is ARCH/GARCH models. On the stock market also shows asymmetric effect(leverage), which is a negative relationship between the change in the value of returns with volatility movement. So, the model can be used is Asymmetric Power Autoregressive Conditional Heteroscedasticity (APARCH) model. APARCH model chosen to modeling and forecasting the volatility of Bukopin return stock is APARCH (1,2) mode

FORECASTING STOCK PRICE INDEX USING BAYESIAN COMBINATION APPLIES IN INDONESIA STOCK EXCHANGE (IDX) JULY 1st 1997 - FEBRUARY 17th 2012

Forecasting of stock price index is measuring the level of stock prices; in addition, its practical application is to compare values at different points in time. Using Bayesian combination in this paper, it is a mixture approach to forecast based on a distribution state planetary of predictive models. We use Bayesian Model Averaging (BMA) to forecast real-time measures of stock price index, employing a large number of real and financial indicators. This aim of this study is to analyze forecasting stock price index in Indonesia Stock Exchange (IDX) index. Moreover, the forecasted time series data is an important issue in finance. It can put forward an up-to-date review of approximation approaches available for the Bayesian implication of Generalized Autoregressive Conditional Hereroskedasticity (GARCH) models. They may be important nonlinearities, asymmetries, and long memory properties in the volatility process. We will introduce GARCH models that give the alternative volatility forecasting models. They can involve that constant updating of parameter estimates. We will explain how to measure and model volatility is an important issue in finance. BMA can give us good reason to improve forecasting when we change away from linear models and average over requirement let GARCH effects in the modernizations to log-volatility. Therefore, BMA consistently dispenses a high posterior weight to models that infer of GARCH models

Information Content of Implied Probability Distributions: Empirical Studies of Japanese Stock Price Index Options

Empirical studies of the information content of option prices have focused on exploring whether implied volatility contains useful information regarding the future fluctuation of underlying asset prices. If expectation formation in the option markets reflects all the currently available information regarding future price movements, option prices will be useful in forecasting the price fluctuation of underlying assets. This paper extends such an analytical framework to implied probability distribution as a whole and examines its information content by using Japanese stock price index option data (on a daily basis) from mid-1989 to mid-1996. To this end, the following questions are examined: (1) whether the implied probability distribution is a good forecast of the subsequently realized distribution of stock price fluctuations, and (2) whether a leads and lags relationship exists between stock price changes and changes in the shape of the implied probability distribution. The estimation results show that (1) the implied probability distribution contains some information regarding future price movements, but its forecasting ability is not superior to that of the historical distribution, and (2) the shape of the implied probability distribution not only responds to stock price changes but also contains some information on forecasting stock price changes. However, it should be noted that such results are highly sensitive to the choice of sample period, suggesting that the information content depends on macroeconomic and financial market conditions. Therefore, the information contained in an implied probability distribution is difficult to interpret automatically as an information variable for monetary policy, and further studies are needed on how to make use of information contained in implied probability distributions.

Analysis of the Relationship Between Stock Prices and Their Volatilities

In this thesis, the main purpose is to test the relationship between the stock price index and their volatilities by using the returns from daily stock prices. Generally, the volatility of stock prices is shown by its standard deviation or variance, and the value of variance is chosen to represent volatility in this paper. Consequently, the relationship between the conditional variance, which is the volatility, and the stock price index is analyzed by applying different forecasting models to the log returns of the stock price indexes to perform correlation and regression tests and testing the conditional variance and residual terms from the models. For the chapter 2 of this paper, the portfolio of basic financial markets will be introduced. And there are the basic information about the 8 stock price indexes selected in Asia stock markets and their exchanged market. In the next section chapter 3, firstly there is the introduction the volatility of stock price index and the methodologies we used for analyzing the relationship between the indexes and volatilities. The main selected models are the time series models including the Exponentially Weighted Moving Average (EWMA), the Autoregressive conditional heteroskedasticity model (ARCH), Generalized Autoregressive ConditionalHeteroskedasticity model (GARCH), GARCH-in-mean model and the exponential general autoregressive conditional heteroskedastic model (EGARCH). Furthermore, by using an econometrics Software which is Econometrics Views (EViews) to calculate the selected models, it is observed that the relationships mentioned in chapter 3 have asymmetry and leverage effectIn this thesis, the main purpose is to test the relationship between the stock price index and their volatilities by using the returns from daily stock prices. Generally, the volatility of stock prices is shown by its standard deviation or variance, and the value of variance is chosen to represent volatility in this paper. Consequently, the relationship between the conditional variance, which is the volatility, and the stock price index is analyzed by applying different forecasting models to the log returns of the stock price indexes to perform correlation and regression tests and testing the conditional variance and residual terms from the models. For the chapter 2 of this paper, the portfolio of basic financial markets will be introduced. And there are the basic information about the 8 stock price indexes selected in Asia stock markets and their exchanged market. In the next section chapter 3, firstly there is the introduction the volatility of stock price index and the methodologies we used for analyzing the relationship between the indexes and volatilities. The main selected models are the time series models including the Exponentially Weighted Moving Average (EWMA), the Autoregressive conditional heteroskedasticity model (ARCH), Generalized Autoregressive ConditionalHeteroskedasticity model (GARCH), GARCH-in-mean model and the exponential general autoregressive conditional heteroskedastic model (EGARCH). Furthermore, by using an econometrics Software which is Econometrics Views (EViews) to calculate the selected models, it is observed that the relationships mentioned in chapter 3 have asymmetry and leverage effect154 - Katedra financídobř

Estimating and Forecasting Asset Volatility and Its Volatility: A Markov-Switching Range Model

This paper proposes a new model for modeling and forecasting the volatility of asset markets. We suggest to use the log range defined as the natural logarithm of the difference of the maximum and the minimum price observed for an asset within a certain period of time, i.e. one trading week. There is clear evidence for a regime-switching behavior of the volatility of the S&P500 stock market index in the period from 1962 until 2007. A Markov-switching model is found to fit the data significantly better than a linear model, clearly distinguishing periods of high and low volatility. A forecasting exercise leads to promising results by showing that some specifications of the model are able to clearly decrease forecasting errors with respect to the linear model in an absolute and mean square sense.Volatility, range, Markov-switching, GARCH, forecasting.

Analysis of market volatility via a dynamically purified option price process

The paper studies methods of dynamic estimation of volatility for financial time series. We suggest to estimate the volatility as the implied volatility inferred from some artificial ‘dynamically purified' price process that in theory allows to eliminate the impact of the stock price movements. The complete elimination would be possible if the option prices were available for continuous sets of strike prices and expiration times. In practice, we have to use only finite sets of available prices. We discuss the construction of this process from the available option prices using different methods. In order to overcome the incompleteness of the available option prices, we suggest several interpolation approaches, including the first order Taylor series extrapolation and quadratic interpolation. We examine the potential of the implied volatility derived from this proposed process for forecasting of the future volatility, in comparison with the traditional implied volatility process such as the volatility index VIX

Forecasting Volatility of Dhaka Stock Exchange: Linear Vs Non-linear Models

Prior information about a financial market is very essential for investor to invest money on parches share from the stock market which can strengthen the economy. The study examines the relative ability of various models to forecast daily stock indexes future volatility. The forecasting models that employed from simple to relatively complex ARCH-class models. It is found that among linear models of stock indexes volatility, the moving average model ranks first using root mean square error, mean absolute percent error, Theil-U and Linex loss function criteria. We also examine five nonlinear models. These models are ARCH, GARCH, EGARCH, TGARCH and restricted GARCH models. We find that nonlinear models failed to dominate linear models utilizing different error measurement criteria and moving average model appears to be the best. Then we forecast the next two months future stock index price volatility by the best (moving average) model

PEMODELAN DAN PERAMALAN VOLATILITAS PADA RETURN SAHAM BANK BUKOPIN MENGGUNAKAN MODEL ASYMMETRIC POWER AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY (APARCH)

Stock is a sign of ownership of an individual or entity within a corporation or limited liability company. While the stock price index is a reflection of the movement of the stock price. Stock investments can not avoid the risk, so we need a model that can predict stock returns and volatility. Models are often used is ARCH/GARCH models. On the stock market also shows asymmetric effect(leverage), which is a negative relationship between the change in the value of returns with volatility movement. So, the model can be used is Asymmetric Power Autoregressive Conditional Heteroscedasticity (APARCH) model. APARCH model chosen to modeling and forecasting the volatility of Bukopin return stock is APARCH (1,2) model, by the equation: σ_t^1.325308=0.000194+0.166886( |ε_(t-1) |-0.510180 ε_(t-1) )^1.325308+0.463148 σ_(t-1)^1.325308+0.347828 σ_(t-2)^1.325308 Keywords: Stock, volatility, asymmetric, return, APARC

Using Neural Networks to Forecast Volatility for an Asset Allocation Strategy Based on the Target Volatility

The objective of this study is to use artificial neural networks for volatility forecasting to enhance the ability of an asset allocation strategy based on the target volatility. The target volatility level is achieved by dynamically allocating between a risky asset and a risk-free cash position. However, a challenge to data-driven approaches is the limited availability of data since periods of high volatility, such as during financial crises, are relatively rare. To resolve this issue, we apply a stability-oriented approach to compare data for the current period to a past set of data for a period of low volatility, providing a much more abundant source of data for comparison. In order to explore the impact of the proposed model, the results of this approach will be compared to different volatility forecast methodologies, such as the volatility index, the historical volatility, the exponentially weighted moving average (EWMA), and the generalized autoregressive conditional heteroskedasticity (GARCH) model. Trading measures are used to evaluate the performance of the models for forecasting volatility. An empirical study of the proposed model is conducted using the Korea Composite Stock Price Index 200 (KOSPI 200) and certificate of deposit interest rates from January, 2006 to February, 2016