Journal of Risk Management and Insurance: Vol. 22 No. 1 (2018)

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    The Halloween effect and other seasonal anomalies in the energy sector of the stock exchange of Thailand
    This research aims to explore the existence of three well - known seasonal anomalies – the January Effect, the April Effect, and the Halloween Eff ect – as pertains to monthly returns as well as to volatility. Effects on returns and volatility will further be studied within the SET Energy index as well as 9 selected energy stocks from the period April 2005 to July 2016. The objective of this study is to find seasonality hidden within the above Index and stocks, and establish a simple trading strategy to benefit investors. As in preceding studies, our methodology uses the dummy regression technique and the EGARCH model is employed to investigate the impact of these seasonal anomalies on the volatility of returns. The result found that Halloween Effect and the January Effect have a statistically negligible effect on returns within the smaller SET Energy Index. The April Effect does have statistical s ignificance on returns within the SET Energy Index. Buying the SET Energy index before April is likely to yield positive returns at the end of the month. Investors should accumulate positions during these seasonal anomalies – in light of low volatility – a nd take profit once volatility returns to normal.
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    Value at risk performance in cryptocurrencies
    Due to conclusion could not rely on only one test, in this study, we apply various approaches to verify the actuary of VaR model to find out whether VaR model, especially historical VaR and delta normal VaR model, can provide the accura te risk measurement results for cryptocurrencies risk , especially CRIX, BTC, ETH and XRP . We use Kupiec’s POF test, Independence Test - Christoffersen (1998) and Joint Test that widely use for backtesting VaR model. Performance test results for risk measurem ent by historical VaR provide a fairly accurate over delta normal VaR when we use Kupiec’s POF - test for the accuracy of VaR model. Christoffersen (1998) independence test, the exceptions (failures) of historical VaR and delta normal VaR model show independ ence exceptions in accordance with an only high confidence level of critical values (0.99). Otherwise , the low confidence level of critical values (0.90 and 0.95) appears dependence exceptions. For the Joint test, we combine POF - test and independence test because each model has different advantages and disadvantages. The results show that historical VaR model is suitable for measuring cryptocurrency risk over delta normal VaR only high confidence level of critical values.