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The purpose of this paper is to assess trading strategies adopted by each large trader group and examine their effects on the volatility in the interest rate futures markets.

The Grinblatt et al.'s (1995) measure of momentum strategy is used to estimate the degree momentum and contrarian strategies. Then, regression analysis is used to determine the effects of trading strategies on volatility.

Up until 2005, the trades by non-clearing member firms in the futures market were separated from institutional traders providing us the opportunity to study trading strategies adopted by large distinct trading groups and its effects on volatility in the futures markets. It is found that individual traders use momentum strategy, whereas market makers and institutional traders use contrarian strategy. Momentum strategy adopted by individual traders increases volatility whereas contrarian strategy dampens volatility. Moreover, it is found that institutional traders engage more actively in contrarian trading when individual traders cause excessive volatility. The two distinct trading groups were separately tracked prior to 2005 giving us a unique window to determine the effect of the traders that conduct momentum trading as opposed to the ones that are contrarian traders. After the reclassification, the institutional trading group exhibited weaker contrarian strategy which can be attributed to the inclusion of non-clearing firm traders.

This study documents the first empirical evidence that shows off-exchange futures trader group is not composed of only pure noise makers, but there are short-term forecasters in its group. The authors also show a unique finding that noises caused by off-exchange group is from momentum strategy that they use, whereas contrarian strategy is used by institutional trader lower volatility.

In this paper we conduct tests for two different trading rules, namely, the Dual Moving Average (DMA) model and the Channel Breakout (CHB) rule. These rules are tested across five futures contracts – the S&P 500, British Pound, US T‐Bonds, COMEX Gold and Corn using daily data over the period 1990 to 1998. Overwhelmingly, we find that the trading rules are unable to produce (gross or net) profits at any statistical level. While positive gross and net profits were available in four of the five markets, the profits were neither economically or statistically significant.

The purpose of this paper is to examine cross-correlation in stock returns of SAARC countries, conditional and unconditional volatility of stock markets and to test efficient market hypothesis (EMH).

Stock indices of India, Bangladesh, Sri Lanka and Pakistan are considered to serve as proxy for stock markets in SAARC countries. Data consist of daily closing price of stock indices from 2000 to 2011. Since preliminary testing indicated presence of serial autocorrelation and volatility clustering, family of GARCH models is selected.

Results indicate presence of serial autocorrelation in stock market returns, implying dependence of current stock prices on stock prices of previous times and leads to rejection of EMH. Significant relationship between stock market returns and unconditional volatility indicates investors’ expectation of extra risk premium for exposing their portfolios to unexpected variations in stock markets. Cross-correlation revealed level of integration of South Asian economies with global market to be high.

Business cycles and other macroeconomic developments affect most companies and lead to unexplained relationships. The paper finds stock markets to exist at different levels of development as economic liberalization started at different points of time in SAARC countries.

Correlation between stock indices of SAARC economies are found to be low which is in line with intra-regional trade being one of lowest as compared to other regional groups. Results point towards greater need for economic cooperation and integration between SAARC countries. Greater financial integration leads to development of markets and institutions, effective price discovery, higher savings and greater economic progress.

The paper focuses on EMH and risk return relation for SAARC nations.

Previous studies have shown the VIX futures tend to roll-down the term structure and converge towards the spot as they grow closer to maturity. The purpose of this paper is to propose an approach to improve the volatility index fear factor-level (VIX-level) prediction.

First, the authors use a forward-looking technique, the Heath–Jarrow–Morton (HJM) no-arbitrage framework to capture the convergence of the futures contract towards the spot. Second, the authors use principal component analysis (PCA) to reduce dimensionality and save substantial computational time. Third, the authors validate the model with selected VIX futures maturities and test on value-at-risk (VAR) computations.

The authors show that the use of multiple factors has a significant impact on the simulated VIX futures distribution, as well as the computations of their VAR (gain in accuracy and computing time). This impact becomes much more compelling when analysing a portfolio of VIX futures of multiple maturities.

The authors’ approach assumes the variance to be stationary and ignores the volatility smile. Nevertheless, they offer suggestions for future research.

The VIX-level prediction (the fear factor) is of paramount importance for market makers and participants, as there is no way to replicate the underlying asset of VIX futures. The authors propose a procedure that provides efficiency to both pricing and risk management.

This paper is the first to apply a forward-looking method by way of a HJM framework combined with PCA to VIX-level prediction in a portfolio context.