Farida Titik Kristanti

Covid-19 has an impact on stock investment, especially in Indonesia, marked by the decline in the Jakarta Composite Index (JCI) at the beginning of the Covid-19 pandemic. During the Covid-19 era, there was a lot of negative information about the uncertainty of the market, which made investors irrational about the choice of stocks in the portfolio. So this research will have a hypothesis that the High Volatility stock group will be the best portfolio in Covid-19 conditions. The sample used is the Group of stocks that have the largest market capitalization value in JCI. Stocks with large market caps are chosen because of one of the indicators of blue chip stock. The sample will be divided into three portfolio groups, High Volatility, Moderate Volatility, and Low Volatility. The results obtained that High Volatility became the best portfolio during the Covid-19 period. The results of this study prove that the group of stocks with High Volatility will get positive returns and sharpe performance results are the highest and positive, compared to moderate volatility and low volatility portfolios. This result arises because stocks with High Volatility are subject to large price fluctuations and in this situation, investors can invest in these stocks in a short time frame. The short-term process is carried out regularly so that it can be in accordance with investors' expectations for investments in the portfolio.

Construction companies are very dependent on the projects carried out by a company. Therefore, measuring whether a company is distressed or non-distressed can be done by looking at the ratios derived from the components of the financial statements from both the balance sheet and the company’s profit and loss. This study offers a new method for measuring financial distress in companies with Artificial Neural Networks (ANN). The model provided comes from several financial ratios in 17 construction companies listed on the Indonesia Stock Exchange. The model is expected to produce the best model by showing the lowest prediction error rate. The results showed that the best ANN model has 25 inputs, 20 hidden layer neurons, and 1 best model output. The model obtained will be tested directly on the sample used; the results are that 6 construction companies in Indonesia have financial distress and 11 non-distress problems. This result proves that the best model obtained can predict the level of financial distress of companies with a small error rate to produce 6 companies identified as financially distressed. This result can be a warning for companies to increase revenue by adding new projects to get out of financial distress status. Traditional financial distress models such as Altman, Zmijewski, Springate, and Fulmer, which have become researchers’ guidelines for measuring financial distress, can be added to the ANN 25-20-1 model as a comparison to strengthen the research results.

The banking industry plays a crucial role in driving the Indonesian economy. Therefore, any financial upheaval within this sector would have a significant influence on the overall economy. Hence, this study examines the capital composition of banking institutions in Indonesia to assess the financial soundness of the banks. A bank’s susceptibility to default will adversely affect client confidence in the bank. This study investigates the influence of governance attributes, such as board size, board meeting frequency, risk committee presence, institutional ownership, and independent committee existence, on the capital structure of Indonesian banks. 31 samples were intentionally chosen using purposeful sampling. Data estimation was performed using a two-step Arellano-Bond Generalized Method of Moments (GMM) estimator. The findings suggest that the bank risk committee, institutional ownership, and independent committee exert a notable and favorable influence on the capital structure of banks in Indonesia. Nevertheless, the size of the board and the frequency of board meetings do not exert a substantial impact. The size of the board and the use of leverage have no substantial impact. Developing efficient corporate governance procedures is essential for ensuring the bank’s financial stability. This involves maximizing the effectiveness of the risk committee, institutional ownership, and independent committee.

Acknowledgment
This paper is funded by PPM-PTM Grants of the Ministry of Education, Culture, Research and Technology of 2023 (03/SP2H/RT-MONO/LL4/2023).

Type of the article: Research Article

Abstract
This study evaluates and compares risk measurement models for ten major cryptocurrencies: Bitcoin, Ethereum, Tether, Ripple, Dogecoin, Cardano, Binance Coin, Polkadot, Solana, and USD Coin. Using daily log-return data from January 2017 to October 2024, the analysis applies Modified Cornish-Fisher Value-at-Risk and standard, exponential, threshold, and Markov-switching generalized autoregressive conditional heteroskedasticity models. The main comparison is conducted at the 99% confidence level, while model reliability is assessed through out-of-sample backtesting using 500 observations and the Kupiec unconditional coverage and Christoffersen conditional coverage tests. The results reveal substantial heterogeneity in cryptocurrency risk. Modified Cornish-Fisher Value-at-Risk produces highly sensitive estimates for assets with extreme skewness and kurtosis, particularly Ripple, Cardano, and Dogecoin. However, no single model performs consistently better across all assets. Bitcoin is the only cryptocurrency for which all tested models pass both backtesting procedures. The Markov-switching specification provides acceptable coverage for Bitcoin, Ripple, and Dogecoin but does not consistently outperform conventional volatility models. Standard and asymmetric volatility models provide stronger support for Cardano, Binance Coin, and Polkadot, whereas Ethereum, Solana, and USD Coin remain difficult to model under the examined specifications. These findings demonstrate that cryptocurrency risk measurement requires asset-specific model selection based on both estimated loss magnitude and formal backtesting evidence.