Separating microstructure noise from volatility

There are two variance components embedded in the returns constructed using high frequency asset prices: the time-varying variance of the unobservable efficient returns that would prevail in a frictionless economy and the variance of the equally unobservable microstructure noise. Using sample moments of high frequency return data recorded at different frequencies, we provide a simple and robust technique to identify both variance components.     

Dilip M. Nachane: Critique of the new consensus macroeconomics and implications for India

Journal of Quantitative Economics -     

Long-run risk-return trade-offs

Excess market returns are correlated with past market variance. This dependence is statistically mild at short horizons (thereby leading to a hard-to-detect risk-return trade-off, as in the existing literature) but increases with the horizon and is strong in the long run (i.e., between 6 and 10 years). From an econometric standpoint, we find that the long-run predictive power of past market variance is robust to the statistical properties of long-horizon stock-return predictive regressions. From an economic standpoint, we show that, when conditioning on past market variance, conditional versions of the traditional CAPM and consumption-CAPM yield considerably smaller cross-sectional pricing errors than their unconditional counterparts.     

Market microstructure noise,integrated variance estimators,and the accuracy of asymptotic approximations

A growing literature has been advocating consistent kernel estimation of integrated variance in the presence of financial market microstructure noise. We find that, for realistic sample sizes encountered in practice, the asymptotic results derived for the proposed estimators may provide unsatisfactory representations of their finite sample properties. In addition, the existing asymptotic results might not offer sufficient guidance for practical implementations. We show how to optimize the finite sample properties of kernel-based integrated variance estimators. Empirically, we find that their suboptimal implementation can, in some cases, lead to little or no finite sample gains when compared to the classical realized variance estimator. Significant statistical and economic gains can, however, be recovered by using our proposed finite sample methods.     

Realized volatility forecasting and option pricing

A growing literature advocates the use of microstructure noise-contaminated high-frequency data for the purpose of volatility estimation. This paper evaluates and compares the quality of several recently-proposed estimators in the context of a relevant economic metric, i.e., profits from option pricing and trading. Using forecasts obtained by virtue of alternative volatility estimates, agents price short-term options on the S&P 500 index before trading with each other at average prices. The agents’ average profits and the Sharpe ratios of the profits constitute the criteria used to evaluate alternative volatility estimates and the corresponding forecasts. For our data, we find that estimators with superior finite sample Mean-squared-error properties generate higher average profits and higher Sharpe ratios, in general. We confirm that, even from a forecasting standpoint, there is scope for optimizing the finite sample properties of alternative volatility estimators as advocated by Bandi and Russell [Bandi, F.M., Russell, J.R., 2005. Market microstructure noise, integrated variance estimators, and the accuracy of asymptotic approximations. Working Paper; Bandi, F.M., Russell, J.R., 2008b. Microstructure noise, realized variance, and optimal sampling. Review of Economic Studies 75, 339–369] in recent work.     

On the Dynamics of Inflation-Stock Returns in India

Journal of Quantitative Economics - In this paper, an attempt is made to examine the relationship between inflation and stock returns in India using spectral and time-frequency methods. Scale...     

Time-varying leverage effects

Vast empirical evidence points to the existence of a negative correlation, named ”leverage effect”, between shocks to variance and shocks to returns. We provide a nonparametric theory of leverage estimation in the context of a continuous-time stochastic volatility model with jumps in returns, jumps in variance, or both. Leverage is defined as a flexible function of the state of the firm, as summarized by the spot variance level. We show that its point-wise functional estimates have asymptotic properties (in terms of rates of convergence, limiting biases, and limiting variances) which crucially depend on the likelihood of the individual jumps and co-jumps as well as on the features of the jump size distributions. Empirically, we find economically important time-variation in leverage with more negative values associated with higher variance levels.     

Inflation and openness in India: an asymmetric approach

This paper examines the dynamic relationship between inflation and openness from 1970 to 2014 in the Indian context. In the first of its kind, this paper investigates the relationship within a nonlinear framework by employing NARDL cointegration test due to Shin, Yu, and Greenwood Nimmo (2014). The empirical results show that there is asymmetry in the relationship between openness and inflation both in short-run as well as in long-run. However, overall a positive relation (though weak) holds between inflation and openness and hence refutes well known Romer (1993) hypothesis that inflation falls with openness. The results further showed a positive relation between inflation and other variables in the study. The overall response of inflation towards the positive and negative changes in explanatory variables differed significantly.     

Some Basic Issues in Time Series Modelling

Journal of Quantitative Economics - The notion of individual freedom has been around in economics for a long time. The formal analysis of individual freedom in welfare economics, however, is of...     

Long-term effects of crop residues and fertility management on carbon sequestration and agronomic productivity of groundnut–finger millet rotation on an Alfisol in southern India

The data from long-term management and cropping systems experiments are needed to assess changes in soil quality, organic carbon pool, and agronomic sustainability. Thus, a 13-year-old soil fertility management experiment was used to assess the impact of crop residues carbon (C) inputs on SOC stock in a rainfed groundnut (Arachis hypogeae L.)–fingermillet (Eleusine coracana (L.) Gaertn) rotation in semiarid alfisol. The application of farmyard manure (FYM) alone or in a combination with chemical fertilizers contributed to higher amounts of C inputs and subsequently to build up a higher SOC pool. Application of 10?Mg?ha^?1 of FYM and a recommended dose of chemical fertilizer (25:21.8:20.7 and 50:21.8:20.7?kg?N, P, K ha^?1 for groundnut and fingermillet, respectively) increased soil SOC pool by 41.2% to 73.0?Mg?ha^?1 with an increase of 9.3?Mg?ha^?1 over 13 years. Both SOC pool and rates of its sequestration were positively correlated with cumulative C input and sustainable yield index. A minimal input of 1.62?Mg?C?ha^?1?yr^?1 is needed to maintain SOC pool at the antecedent level. Balanced application of NPK fertilizers is needed to reduce and reverse the depletion of SOC pool.