Forecasting Realised Volatility of Micex Index

2014 ◽  
Author(s):  
Alexandr Cecetov
Keyword(s):  
Realised Volatility

scholarly journals Forecasting realised volatility using ARFIMA and HAR models

2019 ◽  
Vol 19 (10) ◽  
pp. 1627-1638 ◽  
Author(s):  
Marwan Izzeldin ◽  
M. Kabir Hassan ◽  
Vasileios Pappas ◽  
Mike Tsionas
Keyword(s):  
Realised Volatility

Optimal combinations of realised volatility estimators

2009 ◽  
Vol 25 (2) ◽  
pp. 218-238 ◽  
Author(s):  
Andrew J. Patton ◽  
Kevin Sheppard
Keyword(s):  
Realised Volatility

Performance of Black-Scholes model with TSRV estimates

2015 ◽  
Vol 41 (8) ◽  
pp. 857-870 ◽  
Author(s):  
Shivam Singh ◽  
Vipul .
Keyword(s):  
Risk Premium ◽  
Market Prices ◽  
Volatility Risk ◽  
Content Type ◽  
Forecasting Error ◽  
Ex Post ◽  
Tick Data ◽  
Black Scholes ◽  
Realised Volatility ◽  
S Model

Purpose – The purpose of this paper is to test the pricing performance of Black-Scholes (B-S) model, with the volatility of the underlying estimated with the two-scale realised volatility measure (TSRV) proposed by Zhang et al. (2005). Design/methodology/approach – The ex post TSRV is used as the volatility estimator to ensure efficient volatility estimation, without forecasting error. The B-S option prices, thus obtained, are compared with the market prices using four performance measures, for the options on NIFTY index, and three of its constituent stocks. The tick-by-tick data are used in this study for price comparisons. Findings – The B-S model shows significantly negative pricing bias for all the options, which is dependent on the moneyness of the option and the volatility of the underlying. Research limitations/implications – The negative pricing bias of B-S model, despite the use of the more efficient TSRV estimate, and post facto volatility values, confirms its inadequacy. It also points towards the possible existence of volatility risk premium in the Indian options market. Originality/value – The use of tick-by-tick data obviates the nonsynchronous error. TSRV, used for estimating the volatility, is a significantly improved estimate (in terms of efficiency and bias), as compared to the estimates based on closing data. The use of ex post realised volatility ensures that the forecasting error does not vitiate the test results. The sample is selected to be large and varied to ensure the robustness of the results.

scholarly journals Determinants of the Forward Premium in the Nord Pool Electricity Market

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1111
Author(s):  
Erik Haugom ◽  
Peter Molnár ◽  
Magne Tysdahl
Keyword(s):  
Electricity Market ◽  
Implied Volatility ◽  
Renewable Energy Sources ◽  
Ordinary Least Squares ◽  
Spot Price ◽  
Forward Contracts ◽  
Forward Premium ◽  
Realised Volatility ◽  
The Difference ◽  
Efficient Power

Nord Pool is the leading power market in Europe. It has been documented that the forward contracts traded in this market exhibit a significant forward premium, which could be a sign of market inefficiency. Efficient power markets are important, especially when there is a goal to increase the share of the power mix stemming from renewable energy sources. We therefore contribute to the understanding of this topic by examining how the forward premium in the Nord Pool market depend on several economic and physical conditions. We utilise two methods: ordinary least squares and quantile regression. The results show that the reservoir level and the basis (the difference between the forward and spot price) have a significant impact on the forward premium. The realised volatility of futures prices and the implied volatility of the stock market have strong effects on both the conditional lower and upper tails of the forward premium. We also find that, as the market has matured, the forward premium has decreased, indicating an increase in market efficiency.

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