Single-stock futures (SSFs) represent a significant development in stock related derivatives. It is an academic as well as practical conundrum as to why SSFs, as a derivative product, have not gained widespread acceptance in most markets, particularly in developed markets. We analyze the Indian securities market for evidence about the role of SSFs and their effectiveness in terms of price information and transmission.
SSFs traded on the National Stock Exchange of India (NSE) have grown substantially since their inception in 2001. Why have other markets struggled to generate interest among investors for SSFs? A stock futures contract provides a way to take advantage of arbitrage, speculative, and hedging opportunities, reducing trading pressures on the underlying markets. Without futures contracts on individual stocks, arbitrageurs and investors must trade in the underlying assets, or trade options and index products.
As economies have become more reliant on private capital flows, they have also become more vulnerable to the volatility of capital flows, and to price and other shocks. A comprehensive framework is needed to analyze and hopefully help prevent large scale capital account crises and associated financial distress. A useful approach that has been gaining popularity since the Asian crisis is to assess the risk posed by potentially unstable positions in sectoral balance sheets, including in the corporate, financial, and public sectors.
In this paper, we assess the information content of volatility forecasts based on the VIX and VXN implied volatility indexes in a daily market risk evaluation framework. Our empirical application focuses on the S&P100 and NASDAQ100 indexes and we high-light the models’ performances in distinct historical time periods which include bull/bear markets and high/low volatility markets. The performance of the VaR models is evaluated using a wide range of tests which span LR, independence, conditional coverage and density forecast tests. Our results show that straightforward volatility forecasts based on the implied volatility indexes provide meaningful results when market risk must be quantified. Furthermore, the models’ performances do not deteriorate in challenging trading environments.
Forecasting volatility has been and still is one of the major success story in the quantitative finance and financial econometrics literature. Indeed, volatility forecasting models have enjoyed a tremendous success since the early 1980’s.1 In financial econometrics, the seminal paper by Engle (1982) has spurred considerable research into ARCH-type models, i.e. the attempt to forecast volatility based on the information given by (past) squared returns. More simple techniques rely on the use of ‘rolling window estimation’ for the variance of the asset returns.2 On the other hand, there is a growing trend in the applied finance literature to advocate the use of implied volatility as the best estimate of future volatility. In the framework of an option pricing model such as the Black and Scholes (1973) model, the expected volatility of the asset over the life of the option is the volatility embedded in the price of the option. If call or put option prices are available, then the Black and Scholes (1973) pricing formula can be inverted such that the expected volatility over the life of the option is computed from the observed market prices of the call or put options. Indeed, when all the other option parameters are known, there is a one-to-one relationship between the option prices and underlying (expected) asset volatility.
