Are Timber and Water Investments Safe-Havens? A Volatility Spillover Approach and Portfolio Hedging Strategies for Investors

2022 ◽  
pp. 102657
Author(s):  
Aristeidis Samitas ◽  
Spyros Papathanasiou ◽  
Drosos Koutsokostas ◽  
Elias Kampouris
Keyword(s):  
Volatility Spillover ◽  
Hedging Strategies

Volatility in frontier markets: a Multivariate GARCH analysis

2019 ◽  
Vol 16 (3) ◽  
pp. 294-312
Author(s):  
Neha Seth ◽  
Monica Singhania
Keyword(s):  
Financial Distress ◽  
Spillover Effect ◽  
Multivariate Garch ◽  
Volatility Spillover ◽  
Causality Test ◽  
Content Type ◽  
Hedging Strategies ◽  
Long Run ◽  
Short Run

Purpose The purpose of this paper is to analyze the existence of volatility spillover effect in frontier markets. This study also examines whether any linkages exist among these markets or not. Design/methodology/approach Monthly data of regional frontier markets, from 2009 to 2016, are analyzed using Multivariate GARCH (BEKK and Dynamic Conditional Correlation (DCC)) models. Findings The result of cointegration test shows that the sample frontier markets are not linked in long run, and Granger causality test reveals that the markets under consideration do not cause each other even in the short run. BEKK test says that the effect of the arrival of shock from the own market does not last for longer, whereas shock from other markets lasts with the stronger persistence, and according to DCC test, the volatility spillover exists for all the markets. Practical implications The results of present study suggest that the frontier markets are not cointegrated in the long run as well as in the short run, which opens the doors for long-term investments in these markets in future, which may lead to decent returns. Long-term investors may draw the benefits from including the financial assets in their portfolios from these non-integrated frontier markets; nevertheless, they have to consider and implement diversification and hedging strategies during the period of financial turmoil, so as to protect themselves against economic and financial distress. Originality/value Significant work has been done on developed, developing and emerging markets but frontier markets are not explored much so far. This paper is an attempt to see the status of frontier stock markets as potential financial markets for diversification benefits.

MENGELOLA RESIKO DENGAN PRODUK DERIVATIF

2008 ◽  
Author(s):  
Dandes Rifa
Keyword(s):  
Risk Management ◽  
Value At Risk ◽  
Foreign Currency ◽  
Balance Sheet ◽  
Cash Flows ◽  
Market Risks ◽  
Hedging Strategies ◽  
Operational Hedging ◽  
Potential Risks ◽  
Derivative Products

The main objective of risk management is to minimize the potential for losses (risk) arising from unexpected changes in currency rates, credit, commodities and equities. One of the risks faced by companies is market risk (value at risk). This article aims to explain that risk management can be one of them by using derivative products. Derivative transactions is very useful for business people who want to hedge (hedging) against a commodity, which always experience price changes from time to time. There are three strategies that can be used to hedge the balance sheet hedging strategy, operational hedging strategies and contractual hedging strategies. Staregi contractual hedging is a form of protection that is done by forming a contractual hedging instruments in order to provide greater flexibility to managers in managing the potential risks faced by foreign currency. Most of these contractual hedging instrument in the form of derivative products. The management can enhance shareholder value by controlling risk. -Party investors and other interested parties hope that the financial manager is able to identify and manage market risks to be faced. If the value of the firm equals the present value of future cash flows, then risk management can be justified. 

Does Commodity Derivatives Function Effectively? A lengthy Discussion

2021 ◽  
pp. 227797522098574
Author(s):  
Bhabani Sankar Rout ◽  
Nupur Moni Das ◽  
K. Chandrasekhara Rao
Keyword(s):  
Price Discovery ◽  
Spot Market ◽  
Futures Market ◽  
The Other ◽  
Volatility Spillover ◽  
Agricultural Commodities ◽  
Commodity Derivatives ◽  
Derivative Market ◽  
Negative Shock ◽  
Lengthy Discussion

The present work has been designed to intensely investigate the capability of the commodity futures market in achieving the aim of price discovery. Further, the downside of the cash and futures market and transfer of the risk to other markets has also been studied using VaR, and Bivariate EGARCH. The findings of the work point that the metal commodity derivative market helps in the efficient discovery of price in the spot market except for nickel. But, in the case of the agricultural commodities, the spot is found to be leading and thus there is no price discovery except turmeric. On the other hand, the volatility spillover is bidirectional for both agri and metal commodities except copper, where volatility spills only from futures to spot. Further, the effect of negative shock informational bias differs from commodity to commodity, irrespective of metal or agriculture.

scholarly journals Multi-Transformer: A New Neural Network-Based Architecture for Forecasting S&P Volatility

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1794
Author(s):  
Eduardo Ramos-Pérez ◽  
Pablo J. Alonso-González ◽  
José Javier Núñez-Velázquez
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
Short Term Memory ◽  
Risk Measures ◽  
Hybrid Models ◽  
Stock Volatility ◽  
Management Actions ◽  
Equity Risk ◽  
Hedging Strategies ◽  
Volatility Models

Events such as the Financial Crisis of 2007–2008 or the COVID-19 pandemic caused significant losses to banks and insurance entities. They also demonstrated the importance of using accurate equity risk models and having a risk management function able to implement effective hedging strategies. Stock volatility forecasts play a key role in the estimation of equity risk and, thus, in the management actions carried out by financial institutions. Therefore, this paper has the aim of proposing more accurate stock volatility models based on novel machine and deep learning techniques. This paper introduces a neural network-based architecture, called Multi-Transformer. Multi-Transformer is a variant of Transformer models, which have already been successfully applied in the field of natural language processing. Indeed, this paper also adapts traditional Transformer layers in order to be used in volatility forecasting models. The empirical results obtained in this paper suggest that the hybrid models based on Multi-Transformer and Transformer layers are more accurate and, hence, they lead to more appropriate risk measures than other autoregressive algorithms or hybrid models based on feed forward layers or long short term memory cells.

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