When index dissemination goes wrong: How fast can traders add and multiply?

This paper studies an episode of dissemination of wrong stock index values in real time due to a software bug in the Indian Nifty index futures market on the morning of January 18, 2006. The episode provides an opportunity to test various models of cognitive biases and bounded rationality highlighted in behavioural finance. The paper provides strong evidence against cognitive biases like “anchoring and adjustment” (Tversky and Kahneman, 1974) that one might expect under such situations even though the cognitive task involved is quite simple. The futures market tracked the true Nifty index which it could not see while completely ignoring the wrong Nifty index that it could see. However, the paper demonstrates that market efficiency failed in more subtle ways. There is evidence of a partial breakdown of price discovery in the futures markets and a weakening of the bonds linking futures and cash markets. This evidence is consistent with the centrality of “market devices” as argued in “actor network theory” in economic sociology (Muniesa, Millo and Callon, 2007 and Preda, 2006). Well functioning markets today depend critically on a whole set of information and communication technologies. Any failures in these material, socio-technical aspects of markets can make markets quite fragile even if behavioural biases are largely absent.

Rupee-Dollar Option Pricing and Risk Measurement: Jump Processes, Changing Volatility and Kurtosis Shifts

Exchange rate movements in the Indian rupee (and many other emerging market currencies) are characterised by long periods of placidity punctuated by abrupt and sharp changes. Many, but by no means all, of these sharp changes are currency depreciations. This paper shows that econometric models of changing volatility like Generalised AutoRegressive Conditional Heteroscedasticity (GARCH) with non normal residuals which perform quite well in other financial markets fail quite miserably in the case of the INR-USD process because they do not allow for such jumps in the exchange rate. The empirical results very convincingly demonstrate the need to model the exchange rate process as a mixed jump-diffusion (or normal mixture) process. Equally importantly, the empirical results provide strong evidence that the jump probabilities are not constant over time. From a statistical point of view, changes in the jump probabilities induce large shifts in the kurtosis of the process. The failure of GARCH processes arises because they allow for changes in volatility but not for changes in kurtosis. The time varying mixture models are able to accommodate regime shifts by allowing both volatility and kurtosis (not to mention skewness) to change. This also shows that the periods of calm in the exchange rate are extremely deceptive; in these periods, the variance of rate changes is quite low, but the kurtosis is so high (in the triple digit range) that the probability of large rate changes is non trivial. The empirical results also show that the Black-Scholes-Garman-Kohlhagen model for valuation of currency options is quite inappropriate for valuing rupee-dollar options and that the Merton jump-diffusion model is the model of choice for this purpose.

ARM Wrestling with Big Data: A Study of Commodity ARM64 Server for Big Data Workloads

ARM processors have dominated the mobile device market in the last decade due to their favorable computing to energy ratio. In this age of Cloud data centers and Big Data analytics, the focus is increasingly on power efficient processing, rather than just high throughput computing. ARM's first commodity server-grade processor is the recent AMD A1100-series processor, based on a 64-bit ARM Cortex A57 architecture. In this paper, we study the performance and energy efficiency of a server based on this ARM64 CPU, relative to a comparable server running an AMD Opteron 3300-series x64 CPU, for Big Data workloads. Specifically, we study these for Intel's HiBench suite of web, query and machine learning benchmarks on Apache Hadoop v2.7 in a pseudo-distributed setup, for data sizes up to $20GB$ files, $5M$ web pages and $500M$ tuples. Our results show that the ARM64 server's runtime performance is comparable to the x64 server for integer-based workloads like Sort and Hive queries, and only lags behind for floating-point intensive benchmarks like PageRank, when they do not exploit data parallelism adequately. We also see that the ARM64 server takes $\frac{1}{3}^{rd}$ the energy, and has an Energy Delay Product (EDP) that is $50-71\%$ lower than the x64 server. These results hold promise for ARM64 data centers hosting Big Data workloads to reduce their operational costs, while opening up opportunities for further analysis.Comment: Accepted for publication in the Proceedings of the 24th IEEE International Conference on High Performance Computing, Data, and Analytics (HiPC), 201

A Valuation Model for Indeterminate Convertibles

Many issues of convertible debentures in India in recent years provide for a mandatory conversion of the debentures into an unspecified number of shares at an unspecified time; the conversion ratio (i.e., the number of shares per debenture) is to be determined by the Controller of Capital Issues (CCI). There are serious problems in arriving at a rational value for these "indeterminate convertibles". Even if the investor can make some estimate of the likely conversion terms, there is no valuation model available to arrive at a price. This paper applies the general theory of derivative securities (Cox, Ingersoll and Ross, 1985) to obtain a valuation model for these instruments. The model shows that the naive valuation model which sets the value of the debenture equal to the current stock price times the expected conversion ratio is likely to be a significant overestimate of the price. It also shows that changes in the stock price lead to less than proportionate changes in the debenture price unlike in the case of pre-specified conversion terms. Similarly, the CAPM beta of the debenture would be significantly lower than that of the share. While the model does not obviate the need for obtaining estimates of unobservable parameters related to the market expectations about the likely conversion ratio, the qualitative insights given by the model are quite useful. The model is successful in explaining some of the empirical patterns and anomalies that have been observed in ongoing empirical research into the market prices of these debentures.

Risk Management Lessons from the Global Financial Crisis for Derivative Exchanges

During the global financial turmoil of 2007 and 2008, no major derivative clearing house in the world encountered distress while many banks were pushed to the brink and beyond. An important reason for this is that derivative exchanges have avoided using value at risk, normal distributions and linear correlations. This is an important lesson. The global financial crisis has also taught us that in risk management, robustness is more important than sophistication and that it is dangerous to use models that are over calibrated to short time series of market prices. The paper applies these lessons to the important exchange traded derivatives in India and recommends major changes to the current margining systems to improve their robustness. It also discusses directions in which global best practices in exchange risk management could be improved to take advantage of recent advances in computing power and finance theory. The paper argues that risk management should evolve towards explicit models based on coherent risk measures (like expected shortfall), fat tailed distributions and non linear dependence structures (copulas).

The Galactic centre pulsar population

The recent discovery of a magnetar in the Galactic centre region has allowed Spitler et al. to characterize the interstellar scattering in that direction. They find that the temporal broadening of the pulse profile of the magnetar is substantially less than that predicted by models of the electron density of that region. This raises the question of what the plausible limits for the number of potentially observable pulsars - i.e., the number of pulsars beaming towards the Earth - in the Galactic centre are. In this paper, using reasonable assumptions - namely, (i) the luminosity function of pulsars in the Galactic centre region is the same as that in the field, (ii) the region has had a constant pulsar formation rate, (iii) the spin and luminosity evolution of magnetars and pulsars are similar, and (iv) the scattering in the direction of the Galactic centre magnetar is representative of the entire inner parsec - we show that the potentially observable population of pulsars in the inner parsec has a conservative upper limit of $\sim$ 200, and that it is premature to conclude that the number of pulsars in this region is small. We also show that the observational results so far are consistent with this number and make predictions for future radio pulsar surveys of the Galactic centre.Comment: 5 pages, 3 figures, Accepted for publication in MNRAS Letter

Value at Risk Models in the Indian Stock Market

This paper provides empirical tests of different risk management models in the Value at Risk (VaR) framework in the Indian stock market. It is found that the GARCH-GED (Generalised Auto-Regressive Conditional Heteroscedasticity with Generalised Error Distribution residuals) performs exceedingly well at all common risk levels (ranging from 0.25% to 10%). The EWMA (Exponentially Weighted Moving Average) model used in J. P. Morgan’s RiskMetrics� methodology does well at the 10% and 5% risk levels but breaks down at the 1% and lower risk levels. The paper then suggests a way of salvaging the EWMA model by using a larger number of standard deviations to set the VaR limit. For example, the paper suggests using 3 standard deviations for a 1% VaR while the normal distribution indicates 2.58 standard deviations and the GED indicates 2.85 standard deviations. With this modification the EWMA model is shown to work quite well. Given its greater simplicity and ease of interpretation, it may be more convenient in practice to use this model than the more accurate GARCH-GED specification. The paper also provides evidence suggesting that it may be possible to improve the performance of the VaR models by taking into account the price movements in foreign stock markets.

Mastershares: Enigmatic Performance

In this paper we have examined the performance of Mastershares, the first all equity close ended growth fund established by the Unit Trust of India (UTI) in the country, using the various portfolio performance measures that have been suggested in the literature. We found that while in terms of return on the Net Asset Value (NAV) the fund has out-performed the market, in terms of returns based on Market Prices it has shown a mixed performance. On further investigation, we inferred that the excellent performance in terms of NAV could neither be ascribed to selectivity nor to timing of decisions. The explanation possibly lies in UTI’s acquisition of stocks in the primary market as well below prevailing market prices and in the manner of allocation of stocks to various funds managed by the Trust. Our analysis also revealed that the market quite irrationally inflates the volatility of the market price of Mastershares as compared to the volatility observed in the NAV. This observation which implies market inefficiency is in line with the recent researches done in the developed capital markets.