RISK ASSESSMENT OF STOCKS PORTFOLIO THROUGH ENSEMBLE ARMA-GARCH AND VALUE AT RISK (CASE STUDY: INDF.JK AND ICBP.JK STOCK PRICE)

Stocks portfolio is a form of investment that can be used to minimize the risk of loss. In a stock portfolio, the Value at Risk (VaR) can be predicted through the portfolio return. If portfolio return variance is heteroskedastic risk prediction can be done by using VaR with ARIMA-GARCH or Ensemble ARIMA-GARCH model approach. Furthermore, the accuracy of VaR is tested through Backtesting test. In this study, the portfolio is formed from PT Indofood CBP Sukses Makmur (ICBP.JK) and PT Indofood Sukses Makmur Tbk (INDF.JK) stocks from 01/01/2018 to 07/30/2021. The results showed that the best model is Ensemble ARMA-GARCH with MSE 1.3231×10-6. At confidence level of 95% and 1 day holding period, the VaR of the Ensemble ARMA-GARCH was -0.0213. Based on the Backtesting test, it is proven to be very accurate to predict the value of loss risk because the value of the Violation Ratio (VR) is equal to 0.

A Closed-Form Pricing Formula for Log-Return Variance Swaps under Stochastic Volatility and Stochastic Interest Rate

At present, the study concerning pricing variance swaps under CIR the (Cox–Ingersoll–Ross)–Heston hybrid model has achieved many results ; however, due to the instantaneous interest rate and instantaneous volatility in the model following the Feller square root process, only a semi-closed solution can be obtained by solving PDEs. This paper presents a simplified approach to price log-return variance swaps under the CIR–Heston hybrid model. Compared with Cao’s work, an important feature of our approach is that there is no need to solve complex PDEs; a closed-form solution is obtained by applying the martingale theory and Ito^’s lemma. The closed-form solution is significant because it can achieve accurate pricing and no longer takes time to adjust parameters by numerical method. Another significant feature of this paper is that the impact of sampling frequency on pricing formula is analyzed; then the closed-form solution can be extended to an approximate formula. The price curves of the closed-form solution and the approximate solution are presented by numerical simulation. When the sampling frequency is large enough, the two curves almost coincide, which means that our approximate formula is simple and reliable.

The Effect Of Stock Price, Share Return, Share Trading Volume, And Return Variant On Bid-Ask Spread On Textile And Garment Companies Listed On The Indonesia Stock Exchange, 2019-2020

This study aims to obtain empirical evidence of the effect of Stock Price, Stock Return, Stock Trading Volume, and Return Variant on the Bid-Ask Spread of Stocks in Textile and Garment Companies Listed in Indonesia Stock Exchange in 2019-2020. The stock price used is the stock price recorded at the end of each closing period (closing price), stock returns are measured using the difference between returns on the research day and before the study divided by returns on the day before the study, stock trading volume is measured by the number of shares traded at the time of the study. t is divided by the number of shares outstanding at the time of the study, the variance of stock returns is measured using the standard deviation, and the bid-ask spread is measured by the difference between the selling price and the purchase price divided by the difference between the selling price and the purchase price divided by two. The population in this study is 17 textile and garment companies listed on the IDX. Based on the purposive sampling method, a sample of 16 companies was obtained with 309 data. This research data is obtained from the company's monthly data from 2019 to 2020. The results of the analysis show that stock prices and stock trading volumes affect the bid-ask spread, while stock returns and return variances do not affect the bid-ask spread. Meanwhile, simultaneously, stock prices, stock returns, stock trading volume, and return variance affect the bid-ask spread. This research data is obtained from the company's monthly data from 2019 to 2020. The results of the analysis show that stock prices and stock trading volume affect the bid-ask spread, while stock returns and return variances do not affect the bid-ask spread. Meanwhile, simultaneously, stock prices, stock returns, stock trading volume, and return variance affect the bid-ask spread. This research data is obtained from the company's monthly data from 2019 to 2020. The results of the analysis show that stock prices and stock trading volumes affect the bid-ask spread, while stock returns and return variances do not affect the bid-ask spread. Meanwhile, simultaneously, stock prices, stock returns, stock trading volume, and return variance affect the bid-ask spread.

A Panel Data Analysis of Stock Returns and Accounting Information in Indian Paint Industry

The accounting ratios and published financial information serve as a critical tool for investors, creditors, and other stakeholders to ascertain companies' profitability, control, and financial status, which may significantly impact the Stock returns and performance on exchanges. This paper aims to examine whether crucial accounting information affects the price of paint companies in India. In this paper, nine-years (2012-2020) accounting ratios such as returns on asset, equity, and cash cycles for the five listed paint companies in India as explanatory (independent) variables to estimate stock returns. Secondary data is collected chronologically and at a regular yearly frequency. Variables data are derived from the company’s financial statements, Stock Exchange and related website. The study aims to assess and elaborate these accounting ratios effectiveness to substantiate the stock returns of these listed companies. The study uses three-panel data models, the pooled OLS, fixed and random effects, to assess stock returns for the cross-sectional data of these five paint companies. This research indicates that accounting information is significant and positively affects the price of Paint company stock returns on the stock exchange. Both Fixed and Random effect model found to fit the data, significance level of 0.05 (Fixed (FE) at F= 6.3625, p<0.000 and R2 of 0.5462, i.e., fixed effect elaborates for about 55% of the return variance. Random effect at F=10.8647 and p<0.000 and R2 of 0.4429, i.e., elaborates for about 44% of stock return variance. Based on the Hausman data test alternative hypothesis is found to be consistent and therefore Random Effect (RE) model is being used to conclude the findings. The paper's fundamental limitation includes use of limited regressors, companies, and time period. A further qualitative analysis together with other accounting performance indicators as regressors may be included in future studies. These ratios include interest coverage, debt ratios, effective tax rates, asset turnover ratios, dividend distribution ratios, sustainable growth, and top line revenue growth.

ARIMA-GARCH Model and ARIMA-GARCH Ensemble for Value-at-Risk Prediction on Stocks Portfolio

Stocks portfolio is a form of investment that can be used to minimize the risk of loss. In a stock portfolio, the value at risk (VaR) can be predicted through the portfolio return. If portfolio return variance is heteroscedastic risk prediction can be done by using VaR with ARIMA-GARCH or Ensemble ARIMA-GARCH model approach. Furthermore, the accuracy of VaR is tested through backtesting test. In this study, the portfolio formed from Astra Agro Lestari Ltd (AALI) and Indofood Ltd (INDF) stocks from 10/02/2012 to 10/01/2019. The results showed that the best model is ARIMA(0,0,[3])-GARCH(1,2) with AIC of -5.604 and MSE 1.874e-07.At confidence level of 95% and 1 day holding period, the VaR of the ARIMA(0,0,[3])-GARCH(1,2) was -0.3464. Based on the backtesting test, it is proven to be very accurate to predict the value of loss risk because the value of the violation ratio (VR) is equal to 0.

Risk-Averse Trust Region Optimization for Reward-Volatility Reduction

The use of reinforcement learning in algorithmic trading is of growing interest, since it offers the opportunity of making profit through the development of autonomous artificial traders, that do not depend on hard-coded rules. In such a framework, keeping uncertainty under control is as important as maximizing expected returns. Risk aversion has been addressed in reinforcement learning through measures related to the distribution of returns. However, in trading it is essential to keep under control the risk of portfolio positions in the intermediate steps. In this paper, we define a novel measure of risk, which we call reward volatility, consisting of the variance of the rewards under the state-occupancy measure. This new risk measure is shown to bound the return variance so that reducing the former also constrains the latter. We derive a policy gradient theorem with a new objective function that exploits the mean-volatility relationship. Furthermore, we adapt TRPO, the well-known policy gradient algorithm with monotonic improvement guarantees, in a risk-averse manner. Finally, we test the proposed approach in two financial environments using real market data.

Asset Variance Risk Premium and Capital Structure

This article investigates how the asset-return variance risk premium changes leverage. I find that the premium reduces leverage by increasing risk-neutral bankruptcy probability and costs in a model where asset returns have stochastic variance with the risk premium. Empirically, the model calibrations verify a significant reduction in optimal leverage, closer to observed leverage than the model without the premium. In model-free regressions, I document that leverage correlates negatively with the variance premium. The highest negative correlation is among investment-grade firms with low asset beta and historical variance but high variance premiums because their assets have high exposure to the market’s variance premium.

Realized Semicovariances

We propose a decomposition of the realized covariance matrix into components based on the signs of the underlying high‐frequency returns, and we derive the asymptotic properties of the resulting realized semicovariance measures as the sampling interval goes to zero. The first‐order asymptotic results highlight how the same‐sign and mixed‐sign components load differently on economic information related to stochastic correlation and jumps. The second‐order asymptotic results reveal the structure underlying the same‐sign semicovariances, as manifested in the form of co‐drifting and dynamic “leverage” effects. In line with this anatomy, we use data on a large cross‐section of individual stocks to empirically document distinct dynamic dependencies in the different realized semicovariance components. We show that the accuracy of portfolio return variance forecasts may be significantly improved by exploiting the information in realized semicovariances.

The impact of corporate reporting readability on informational efficiency

Purpose Informational efficiency is a fundamental aspect of capital market quality, and therefore, regulators, managers and practitioners attempt to find ways to improve the informational efficiency. Since prior studies primarily focus on the numerical attributes of corporate reporting, it is not yet adequately known whether or not the linguistic attributes of corporate reporting affect informational efficiency. Thus, the purpose of this paper is to examine whether corporate reporting readability (readability), as an important linguistic attribute of corporate reporting, affects informational efficiency. Design/methodology/approach To measure readability, this paper uses Fog index. Moreover, to measure informational efficiency, the paper uses stock return variance ratios. Findings The findings reveal a positive and significant association between readability and informational efficiency. Moreover, the findings show that the association of readability and informational efficiency is stronger for firms facing higher information asymmetry. The findings further document the spillover effect of readability, in the sense that the readability of economically related public firms affects a firm’s informational efficiency. Overall, the results support the arguments that readability enhances informational efficiency. Originality/value This study contributes to the literature by providing evidence on the internalities and externalities of readability in the context of informational efficiency. Thus, the study will be of interest to regulators, managers and practitioners, especially in emerging capital markets, who tend to find practical and easy ways to improve informational efficiency.

PENGARUH BID ASK SPREAD, VARIANS RETURN, VOLUME PERDAGANGAN , DAN HARGA SAHAM TERHADAP HOLDING PERIOD SAHAM

The capital market or stock market is a container to bring together sellers and buyers of financial instruments with investment objectives. The existence of the capital market provides a role for various parties such as companies, investors, and even for the national economy. The correct information about the company's shares in the stock market is needed by investors as a decision to buy and sell shares and also for the decision to hold or release ownership of financial assets. The purpose of this study is to analyze and provide empirical evidence about the effect of bid ask spread, return variance, trading volume, and stock price on the holding period of shares in companies listed on the Indonesia Indonesia Stock Exchange which are categorized as LQ45 companies. The research sample was 45 samples in the form of companies listed on the Indonesia Indonesia Stock Exchange which included the LQ45 company category in 2017. Hypothesis testing was done by path analysis using SPSS version 22. The results of the study were bid ask spread, variance return, trading volume, and stock price does not affect the holding period.