MEASURING THE MARKET RISK OF FREIGHT RATES: A VALUE-AT-RISK APPROACH

2008 ◽  
Vol 11 (05) ◽  
pp. 447-469 ◽  
Author(s):  
TIMOTHEOS ANGELIDIS ◽  
GEORGE SKIADOPOULOS
Keyword(s):  
At Risk ◽  
Hedge Funds ◽  
Value At Risk ◽  
Statistical Tests ◽  
Market Risk ◽  
Freight Rate ◽  
Freight Rates ◽  
Rate Risk ◽  
Risk Approach ◽  
Non Parametric

The fluctuation of shipping freight rates (freight rate risk) is an important source of market risk for all participants in the freight markets including hedge funds, commodity and energy producers. We measure the freight rate risk by the Value-at-Risk (VaR) approach. A range of parametric and non-parametric VaR methods is applied to various popular freight markets for dry and wet cargoes. Backtesting is conducted in two stages by means of statistical tests and a subjective loss function that uses the Expected Shortfall, respectively. We find that the simplest non-parametric methods should be used to measure freight rate risk. In addition, freight rate risk is greater in the wet cargoes markets. The margins in the growing freight derivatives markets should be set accordingly.

scholarly journals Spillover Effects among Financial Institutions: A State-Dependent Sensitivity Value-at-Risk Approach

2014 ◽  
Vol 49 (3) ◽  
pp. 575-598 ◽  
Author(s):  
Zeno Adams ◽  
Roland Füss ◽  
Reint Gropp
Keyword(s):  
At Risk ◽  
Financial Institutions ◽  
Hedge Funds ◽  
Commercial Banks ◽  
Value At Risk ◽  
Spillover Effects ◽  
Investment Banks ◽  
Insurance Companies ◽  
State Dependent ◽  
Risk Approach

AbstractIn this paper, we develop a state-dependent sensitivity value-at-risk (SDSVaR) approach that enables us to quantify the direction, size, and duration of risk spillovers among financial institutions as a function of the state of financial markets (tranquil, normal, and volatile). For four sets of major financial institutions (commercial banks, investment banks, hedge funds, and insurance companies), we show that while small during normal times, equivalent shocks lead to considerable spillover effects in volatile market periods. Commercial banks and, especially, hedge funds appear to play a major role in the transmission of shocks to other financial institutions.

Non-Parametric Future Looking Value-at-Risk

2011 ◽  
Author(s):  
Anders Wilhelmsson ◽  
Marcus Nossman
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Non Parametric

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.

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