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ID:
140012
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| Summary/Abstract |
Value at risk (VaR) is used by financial experts to calculate and predict the risk of financial exposure. In the presence of volatility and long memory, it is a model useful for the prediction of loss in the equity index return series. Checking the accuracy of this model is necessary from the practitioners’ point of view. This article initially checks the presence of autoregressive conditional heteroscedastic (ARCH) and long-memory effects in the daily closing price of the Bombay Stock Exchange (BSE)-BANKEX return series. After confirming the ARCH and long-memory presence, it analyses the different methods of VaR calculation such as asymmetric power ARCH (APARCH), fractionally integrated exponential generalised ARCH (FIEGARCH), hyperbolic generalised GARCH (HYGARCH) and risk metrics. Then, it empirically tests the forecasting capacity of these VaR methods through techniques such as the Kupiec likelihood ratio (LR test) and dynamic quantile test. Furthermore, it checks the root-mean-squared error (RMSE) and mean absolute error (MAE) to determine the model with the least error. From the set of VaR models used here, by and large it concludes that the BANKEX return series has both long-memory and asymmetry effects. By comparing these models, it is implied that the HYGARCH model gives a better result, although the other models have their significance in the estimation and forecasting of the BANKEX return series.
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| 2 |
ID:
150752
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| Summary/Abstract |
The considerable amount of required infrastructure and renewable energy investments expected in the forthcoming years also implies an increasingly relevant contribution of private and institutional investors. In this context, especially regulatory and policy risks have been shown to play a major role for investors when evaluating investments in renewable energy and should thus also be taken into account in risk assessment and when deriving risk-return profiles. In this paper, we provide a stochastic model framework to quantify policy risks associated with renewable energy investments (e.g. a retrospective reduction of a feed-in tariff), thereby also taking into account energy price risk, resource risk, and inflation risk. The model is illustrated by means of simulations and scenario analyses, and it makes use of expert estimates and fuzzy set theory for quantifying policy risks. Our numerical results for a portfolio of onshore wind farms in Germany and France show that policy risk can strongly impact risk-return profiles, and that cross-country diversification effects can considerably decrease the overall risk for investors.
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