scholarly journals VALUE AT RISK OF MOMENTUM INVESTMENT STRATEGY: INDONESIA'S LIQUID STOCKS PORTFOLIO

2019 ◽  
Vol 19 (1) ◽  
pp. 30
Author(s):  
A.Rowland Bismark Fernando Pasaribu
Keyword(s):  
At Risk ◽  
Risk Reduction ◽  
Value At Risk ◽  
Stock Exchange ◽  
Investment Strategy ◽  
Selection Method ◽  
Portfolio Risk ◽  
Result Show ◽  
Risk Approach

The capability of momentum investment strategy was explore through portfolio risk reduction by value at risk method at liquid shares in Indonesia stock exchange period 2008-2016. The purpose of this study are to analyse the value of momentum investment strategy risk reduction with the Value at Risk approach to historical-volatility approach and examine differences in risk reduction performance by winner and loser portfolios formed from a collection of liquid shares in the Indonesia stock exchange for the period 2008-2016. The stocks selection method in forming winners and losers portfolio done by Jegadesh and Titman procedure (1993) followed by calculation of risk reduction with the VaR-HisVol approach. The result show for quarterly and semester period winner portfolio has superior capacity of portfolio risk reduce than loser. Keywords—Investment; Strategy; Portfolio, VaR. Abstrak Kemampuan strategi investasi momentum dieksplorasi dalam teminologi pengurangan risiko portofolio dengan metode value at risk pada portofolio saham saham likuid di Bursa Efek Indonesia periode 2008-2016. Metode pemilihan saham pembentuk portofolio pemenang dan pecundang dilakukan dengan prosedur Jegadesh dan Titman (1993) dilanjutkan dengan kalkulasi pengurangan risiko dengan pendekatan VaR-HisVol. Hasil menunjukkan untuk portofolio pemenang periode triwulanan dan semester memiliki kapasitas superior mengurangi risiko portofolio daripada pecundang. Kata kunci— Investasi, Strategi, Portofolio, VaR

The risk analysis of Bitcoin and major currencies: value at risk approach

2019 ◽  
Vol 22 (1) ◽  
pp. 38-52 ◽  
Author(s):  
Umut Uyar ◽  
Ibrahim Korkmaz Kahraman
Keyword(s):  
At Risk ◽  
Risk Analysis ◽  
Value At Risk ◽  
Risk Measure ◽  
Risk Level ◽  
Portfolio Risk ◽  
Content Type ◽  
Historical Simulation ◽  
Digital Currency ◽  
Risk Approach

Purpose This study aims to compare investors of major conventional currencies and Bitcoin (BTC) investors by using the value at risk (VaR) method common risk measure. Design/methodology/approach The paper used a risk analysis named as VaR. The analysis has various computations that Historical Simulation and Monte Carlo Simulation methods were used for this paper. Findings Findings of the analysis are assessed in two different aspects of singular currency risk and portfolios built. First, BTC is found to be significantly risky with respect to the major currencies; and it is six times riskier than the singular most risky currency. Second, in terms of inclusion of BTC into a portfolio, which equally weights all currencies, it elevates overall portfolio risk by 98 per cent. Practical implications In spite of the remarkable risk level, it could be considered that investors are desirous of making an investment on BTC could mitigate their overall exposed risk relatively by building a portfolio. Originality/value The paper questions the risk level of Bitcoin, which is a digital currency. BTC, a matter of debate in the contemporary period, is seen as a digital currency free from control or supervision of a regulatory board. With the comparison of major currencies and BTC shows that how could be risky of a financial instrument without regulations. However, there is some advice for investors who would like to invest digital currencies despite the risk level in this study.

Volatility Forecast by Volatility Index and Its Use as a Risk Management Tool Under a Value-at-Risk Approach

2018 ◽  
Vol 21 (02) ◽  
pp. 1850010 ◽  
Author(s):  
Yam Wing Siu
Keyword(s):  
At Risk ◽  
Risk Management ◽  
Risk Premium ◽  
Value At Risk ◽  
Realized Volatility ◽  
Management Tool ◽  
Spot Price ◽  
Pricing Options ◽  
Significance Levels ◽  
Risk Approach

This paper examines the predicting power of the volatility indexes of VIX and VHSI on the future volatilities (or called realized volatility, [Formula: see text] of their respective underlying indexes of S&P500 Index, SPX and Hang Seng Index, HSI. It is found that volatilities indexes of VIX and VHSI, on average, are numerically greater than the realized volatilities of SPX and HSI, respectively. Further analysis indicates that realized volatility, if used for pricing options, would, on some occasions, result in greatest losses of 2.21% and 1.91% of the spot price of SPX and HSI, respectively while the greatest profits are 2.56% and 2.93% of the spot price of SPX and HSI, respectively, making it not an ideal benchmark for validating volatility forecasting techniques in relation to option pricing. Hence, a new benchmark (fair volatility, [Formula: see text] that considers the premium of option and the cost of dynamic hedging the position is proposed accordingly. It reveals that, on average, options priced by volatility indexes contain a risk premium demanded by the option sellers. However, the options could, on some occasions, result in greatest losses of 4.85% and 3.60% of the spot price of SPX and HSI, respectively while the greatest profits are 4.60% and 5.49% of the spot price of SPX and HSI, respectively. Nevertheless, it can still be a valuable tool for risk management. [Formula: see text]-values of various significance levels for value-at-risk and conditional value-at-value have been statistically determined for US, Hong Kong, Australia, India, Japan and Korea markets.

scholarly journals Value at risk: Evidence from Pakistan Stock Exchange

2011 ◽  
Vol 5 (17) ◽  
pp. 7474-7480 ◽  
Author(s):  
Nawaz Faisal ◽  
Afzal Muhammad
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Stock Exchange

scholarly journals Using HMM Approach for Assessing Quality of Value at Risk Estimation: Evidence from PSE Listed Company

2017 ◽  
Vol 65 (5) ◽  
pp. 1687-1694
Author(s):  
Tomáš Konderla ◽  
Václav Klepáč
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Stock Exchange ◽  
Risk Estimation ◽  
Listed Company ◽  
Mixture Distribution ◽  
Normal Mixture ◽  
Development Characteristic ◽  
The Common

The article points out the possibilities of using Hidden Markov model (abbrev. HMM) for estimation of Value at Risk metrics (abbrev. VaR) in sample. For the illustration we use data of the company listed on Prague Stock Exchange in range from January 2011 to June 2016. HMM approach allows us to classify time series into different states based on their development characteristic. Due to a deeper shortage of existing domestic results or comparison studies with advanced volatility governed VaR forecasts we tested HMM with univariate ARMA‑GARCH model based VaR estimates. The common testing via Kupiec and Christoffersen procedures offer generalization that HMM model performs better that volatility based VaR estimation technique in terms of accuracy, even with the simpler HMM with normal‑mixture distribution against previously used GARCH with many types of non‑normal innovations.

Sign in / Sign up

Export Citation Format

Share Document