Predictability of Return Volatility Across Different Emerging Capital Markets: Evidence from Asia

2017 ◽  
Vol 6 (2) ◽  
pp. 157-177 ◽  
Author(s):  
Thushari N. Vidanage ◽  
Fabrizio Carmignani ◽  
Tarlok Singh
Keyword(s):  
Econometric Model ◽  
Investment Decision ◽  
Jel Classification ◽  
Policy Formation ◽  
Return Volatility ◽  
Investment Decision Making ◽  
Forecasting Performance ◽  
Out Of Sample ◽  
Egarch Model ◽  
Emerging Capital Markets

The importance of return volatility forecasts in policy formation and investment decision-making in emerging countries is growing considerably. However, from an operational perspective, there is no consensus in the literature on which econometric model has the best forecasting performance. To shed new light on this issue, this article compares forecasting models for a selected group of emerging Asian economies: India, Malaysia, Pakistan, Sri Lanka, Singapore and Thailand. Model’s performance is tested using both in-sample and out-of-sample forecasting methods. It is found that a relatively simple asymmetric EGARCH model clearly outperforms other models. JEL Classification: G12, G17

scholarly journals Sampling methods for investment portfolio formulation procedure at increased market volatility

2021 ◽  
Vol 43 ◽  
pp. 74-93
Author(s):  
Mateusz Dzicher ◽  
◽  
◽  
Keyword(s):  
Sampling Methods ◽  
Empirical Distribution ◽  
Investment Decision ◽  
Optimal Solution ◽  
Statistical Simulation ◽  
Jel Classification ◽  
Optimization Techniques ◽  
Market Volatility ◽  
Investment Portfolio ◽  
Investment Decision Making

Aim/purpose–In this paper, a market volatility-robust portfolio composition frame-work under the modified Markowitz’s approach with the use of sampling methods is developed in order to improve the allocation efficiency for a portfolio of financial in-struments formulation procedure at an increased market volatility.Design/methodology/approach–In order to overcome the risk of not receiving an optimal solution to the portfolio optimization (suboptimal outcomes of attribution of weights in allocation procedures) the developed model, first, implements the rationale that financial markets largely feature two states, i.e., quiescent (non-crisis; low market volatility) periods that are occasionally interspersed with stress (crisis; high market volatility) periods and, second, relies on many input samples of rates of return, either from an empirical distribution or a theoretical distribution (mitigating estimation risk). All computational results are reported for publicly available historical daily data sets on selected Polish blue-chip securities. Findings–Not only did the presented method produce more diversified allocation, but also successfully minimized the unfavorable effects of increased market volatility by providing less risky portfolios in comparison to Newton’s method, typically used for optimization under portfolio theory.Research implications/limitations–The research emphasized that in order to get a more diversified investment portfolio it is crucial to outdo the limitations of a single sample approach (utilized in Markowitz’s model) which may on some occasions be statistically biased. Thus it was proved that sampling methods allow to obtain a less concentrated and volatile allocation which contributes the investment decision-making. However, the current research focused solely on publicly available input data of particular securities. In this manner, an additional analysis can be prepared for other jurisdic-tions and asset classes. There can also be considered a use of other than variance risk measures.Originality/value/contribution–The suggested framework contributes to existing methods a wide array of quantitative data analysis and simulation tools for composing an unique approach that directly addresses the task of minimizing the adverse implications of increased market volatility that, in consequence, pertains to knowledgeable attributing of investment portfolio proportions of either individual or institutional investors. The prepared method is also proved to hold demanded computational quality and, important-ly, the capacity for further development. Keywords: investment decisions, optimization techniques, portfolio selection, statistical simulation methods. JEL Classification: C150, C610, G110

scholarly journals Forecasting Daily Dynamic Hedge Ratios of Indian Index Future Contracts

2019 ◽  
Vol 8 (3) ◽  
pp. 485-493
Keyword(s):  
Performance Measures ◽  
Minimum Mean Square Error ◽  
Multivariate Garch ◽  
Sample Analysis ◽  
Hedge Ratio ◽  
Hedge Ratios ◽  
Forecasting Performance ◽  
Out Of Sample ◽  
Egarch Model ◽  
T Distribution

Forecasting plays a crucial role in determining the direction of future trends and in making necessary investment decisions. This research presents the forecasting performance of three multivariate GARCH models: SGARCH, EGARCH, and GJR-GARCH based on Gaussian and Student’s t-distribution. The forecasting ability of the models is evaluated on the basis of forecasting performance measures: MAE, SSE, MSE, and RMSE. This is done by examining the hedged portfolios of three indices of NSE: NIFTY50, BANKNIFTY, and NIFTYIT. Daily data from Jan 2006 to Dec 2017 is taken and forecasts are conducted using out of sample data from Jan 2016-Dec 2017. Minimum mean square error (MMSE) forecasting method is used to generate conditional variance and covariance forecasts which in turn generate hedge ratios and corresponding hedged portfolio. Minimum variance hedge ratio framework of Ederington (1979) is used for hedging. The in-sample analysis shows that SGARCH with both the distribution performed better than the other models while out-of-sample analysis provides mixed results. EGARCH model assigns the lowest hedge ratio to NIFTY50 and BANKNIFTY while SGARCH model assigns the lowest hedge ratio to NIFTYIT. Forecasting performance measures show the least value for SGARCH and EGARCH model. In future these models are able to reduce maximum risk from the spot market. The results of this research has important implications for financial decision and policy makers.

Is fair value information relevant to investment decision-making: Evidence from the Australian agricultural sector?

2018 ◽  
Vol 43 (4) ◽  
pp. 555-574 ◽  
Author(s):  
Li Yu (Colly) He ◽  
Sue Wright ◽  
Elaine Evans
Keyword(s):  
Agricultural Sector ◽  
Investment Decision ◽  
Empirical Support ◽  
Accounting Standards ◽  
Fair Value ◽  
Cash Flows ◽  
Jel Classification ◽  
Fair Value Accounting ◽  
Operating Cash Flows ◽  
Investment Decision Making

Despite major accounting standards boards worldwide continuing to use fair value extensively, academic evidence on the relevance of fair value accounting has focused on financial assets. This study breaks new ground to provide the first empirical evidence for the agricultural sector on the relevance of fair value accounting. It examines the forecasting power of the fair value of biological assets for future operating cash flows. Using all agribusinesses listed in Australia, where fair value accounting was first implemented in the agricultural sector, we find that fair value of biological assets does not provide incremental forecasting power for future operating cash flows, whether market-determined prices or managerially estimated value is used. The findings of this study provide empirical support for the call by Elad and Herbohn in 2011 for the International Accounting Standards Board (IASB) to revisit the implementation of fair value accounting in the agricultural sector. JEL Classification: G14, G38, M41, Q18

Do Inflation Expectations Matter for Inflation Forecastability: Evidence from Pakistan

2016 ◽  
Vol 55 (3) ◽  
pp. 211-225 ◽  
Author(s):  
Fayyaz Hussain ◽  
Zafar Hayat
Keyword(s):  
Monetary Policy ◽  
Predictive Ability ◽  
Jel Classification ◽  
Inflation Expectations ◽  
Forecast Performance ◽  
Inflation Forecasts ◽  
Forward Guidance ◽  
Forecasting Performance ◽  
Out Of Sample ◽  
Effectiveness Of Monetary Policy

We empirically investigate if the incorporation of inflation expectations helps improve the forecasting performance of a suite of univariate inflation models. Since inflation forecasts are instrumental to the conduct of an effective monetary policy, any possible improvement in the inflation forecastability may tend to enhance the effectiveness of monetary policy—by providing forward guidance both to the monetary authority and the market to effectively anchor inflation expectations. Our results are robust across specifications of our baseline models, sample sizes and forecast horizons. The introduction of inflation expectations, whether contemporaneously or with a 6-months lead improves the predictive ability—both in-sample and out-of-sample for 6 and 12-month horizons. Deterioration however is observed for a 3- month horizon, which point towards the weak representation of the expectations data for a 3- month horizon. JEL Classification: E31, E37 Keywords: Inflation-expectations, Forecast-performance, Pakistan.

scholarly journals A STUDY ON IMPACT OF BEHAVIOURAL FINANCE ON INVESTMENT DECISION OF SINGLE PARENTS IN SOUTH ASIAN COUNTRIES

2021 ◽  
Vol 6 (2) ◽  
pp. 16-37
Author(s):  
Kannadas Sendilvelu ◽  
Manita Deepak Shah
Keyword(s):  
South Asian ◽  
Risk Taking ◽  
Investment Decision ◽  
Single Parent ◽  
Jel Classification ◽  
Asian Countries ◽  
Behavioural Finance ◽  
Investment Decision Making ◽  
The Impact ◽  
Second Chance

The purpose of this study is to find out the possible impact of behavioural finance on the investment decision of a single parent. As being an earning/working single parent who usually does not have other possible sources in their family, the decision which they take must be a reliable one and cannot afford to get a second chance. In the study, this study is also one of an effort to assess the impact of behavioural biases in the investment decision-making of a single parent. A questionnaire is designed and responses are collected from 203 respondents who prefer to invest where the level of risk is either low or moderate and are more concerned about losses in their investment than substantial gain. Also, most of the respondents were investing in order to meet some specific purpose, for their retirement plan as well as to educate their children. This study concludes by stating that investors’ risk-taking capacity is dependent on their level of income and the sources of income. Although every Individual is subject to some biases, they tend to think more rational way than an average investor in many ways as they know about their requirements and the investment they make. JEL Classification Codes: G40, G41.

Intraday Periodicity and Volatility Forecasting: Evidence from Indian Crude Oil Futures Market

2017 ◽  
Vol 16 (1) ◽  
pp. 1-28
Author(s):  
B.B. Chakrabarti ◽  
Vivek Rajvanshi
Keyword(s):  
Crude Oil ◽  
Jel Classification ◽  
Futures Contracts ◽  
Return Volatility ◽  
Oil Futures ◽  
Macroeconomic Announcements ◽  
Long Run ◽  
Forecasting Performance ◽  
Crude Oil Futures ◽  
Intraday Data

We estimate intraday periodicities in return volatility by implementing two time series procedures—flexible Fourier form and cubic spline. We use intraday data for more than five years for crude oil futures contracts traded at the Multi Commodity Exchange India Limited. Filtration of the intraday periodicities from the raw returns reveals long-run dependence in volatility. We observe the presence of recurring and consistent intraday patterns in return volatility. Further, we find that adjustment for the intraday periodicity in return volatility improves forecasting performance. Our results are robust after controlling for the scheduled macroeconomic announcements. JEL Classification: C14, C22, G10

scholarly journals The predictive power of stock market’s expectations volatility: A financial synchronization phenomenon

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0250846
Author(s):  
Nicolás Magner ◽  
Jaime F. Lavin ◽  
Mauricio Valle ◽  
Nicolás Hardy
Keyword(s):  
Implied Volatility ◽  
Predictive Power ◽  
Minimum Spanning Tree ◽  
Investment Decision ◽  
Financial Systems ◽  
Investment Decision Making ◽  
Diversification Strategies ◽  
Out Of Sample ◽  
Tree Length ◽  
Equity Indices

We explore the use of implied volatility indices as a tool for estimate changes in the synchronization of stock markets. Specifically, we assess the implied stock market’s volatility indices’ predictive power on synchronizing global equity indices returns. We built the correlation network of 26 stock indices and implemented in-sample and out-of-sample tests to evaluate the predictive power of VIX, VSTOXX, and VXJ implied volatility indices. To measure markets’ synchronization, we use the Minimum Spanning Tree length and the length of the Planar Maximally Filtered Graph. Our results indicate a high predictive power of all the volatility indices, both individually and together, though the VIX predominates over the evaluated options. We find that an increase in the markets’ volatility expectations, captured by the implied volatility indices, is a good Granger predictor of an increase in the synchronization of returns in the following month. Estimating, monitoring, and predicting returns’ synchronization is essential for investment decision-making, especially for diversification strategies and regulating financial systems.

The role of affective reactions on investment decision-making

2007 ◽  
Author(s):  
Enrico Rubaltelli ◽  
Giacomo Pasini ◽  
Rino Rumiati ◽  
Paul Slovic
Keyword(s):  
Decision Making ◽  
Investment Decision ◽  
Affective Reactions ◽  
Investment Decision Making
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