PERAMALAN VOLATILITAS DAN ESTIMASI VALUE AT RISK (VaR) SAHAM BLUE CHIP PADA SEKTOR PERBANKAN

Investment is an activity to invest an asset to obtain a greater profit. The investment there's in great demand by investors are stock investments. Based on market capitalization, stocks are classified into first-tier, second-tier, and third-tier stocks. Stocks that have the highest market capitalization are first-tier or blue-chip stocks. Blue-chip stocks are stocks that are classified as main shares on the listing board on the IDX. Before investing, it's important to know the level of investment risk in order to make the right investment decisions. The purpose of this study is to determine the risk of investing in blue-chip stocks namely BRI, BCA, and Bank Mandiri through volatility forecasting using the GARCH, EGARCH, or TGARCH models. The data used is the daily closing price of shares for the period of 25 May 2005 to 21 May 2021 which was obtained through the Yahoo Finance website. Based on the research results, it's known that Bank Mandiri has the highest investment risk and BCA has the lowest investment risk. Based on these results, it can be suggested that investors who like risk can choose to invest in Bank Mandiri shares, and those who don't like risk can invest in BCA shares.

Liquidity effects on oil volatility forecasting: From fintech perspective

Fin-tech is an emerging field, inspiring revolutionary innovations in the financial field. It may initiate the evolutionary episode of the financial research, where volatility forecasting is a crucial topic in finance. For forecasting volatility, GARCH model is a prevailing model, however, further improvement of the GARCH model is still challenging. In this paper, we demonstrate how Fintech can play a part in volatility forecasting by employing a metaheuristic procedure called Genetic Programming. On the basis, we are able to develop a new volatility forecasting model, which can beat GARCH family models (including GARCH, IGARCH and TGARCH models) in a significant way. Since genetic programming is an evolutionary algorithm based on the principles of natural selection, this innovative work will be a breakthrough point in the financial area. The innovation of this paper demonstrates how GP technology can be applied in the financial field, attempting to explore the volatility forecasting area from the combination of new technology and finance, known as fintech. More importantly, when the formula of volatility forecasting is unknown as we introduce a new factor, namely, the liquidity factor, we unveil that how GP method can be helpful in determining the specific volatility forecasting model format. We thereby exhibit the liquidity effects on volatility forecasting filed from the fintech perspective.

Better the Devil You Know: Improved Forecasts from Imperfect Models

Many important economic decisions are based on a parametric forecasting model that is known to be good but imperfect. We propose methods to improve out-of-sample forecasts from a mis-specified model by estimating its parameters using a form of local M estimation (thereby nesting local OLS and local MLE), drawing on information from a state variable that is correlated with the misspecification of the model. We theoretically consider the forecast environments in which our approach is likely to o¤er improvements over standard methods, and we find significant fore- cast improvements from applying the proposed method across distinct empirical analyses including volatility forecasting, risk management, and yield curve forecasting.

Volatility Forecasting Performance of Smooth Transition Exponential Smoothing Method: Evidence from Mutual Fund Indices in Malaysia

This paper aims to empirically compare the performance of the smooth transition exponential smoothing (STES) method against the well-known generalized autoregressive conditional heteroskedasticity (GARCH) model in one-step-ahead volatility forecasting. While the GARCH model captured most of the stylized facts of the financial time series, threats of outliers in the leptokurtic distributed series remain unresolved. The study compared volatility forecasting performance of a total of 22 models and methods comprising STES, GARCH, and some ad-hoc forecasting. The daily returns of seven mutual fund indices (derived from 57 individual equity mutual funds) under two different economic conditions (sub-periods) were applied across all competing models. Findings revealed that the STES method with error and absolute error as transition variables emerged as the best post-sample volatility forecasting model in both sub-periods with and without financial crisis impact, as verified by model confidence set (MCS) procedure. The implications based on the results are: (1) both the sign and size of yesterday’s news shock have an impact on today’s volatility; (2) the STES method is resilient to outliers, and hence superior to GARCH and other volatility forecasting approaches examined. This study contributes an empirical approach in forecasting the risk of mutual funds investment for investors and fund managers, as well as extending the scope of volatility forecasting literature into the less explored mutual funds.