An intelligent and secure system for predicting and preventing Zika virus outbreak using Fog computing

Zika virus is a mosquito-borne disease that spreads very quickly in different parts of the world. In this article, we proposed a system to prevent and control the spread of Zika virus disease using integration of Fog computing, cloud computing, mobile phones and the Internet of things (IoT)-based sensor devices. Fog computing is used as an intermediary layer between the cloud and end users to reduce the latency time and extra communication cost that is usually found high in cloud-based systems. A fuzzy k-nearest neighbour is used to diagnose the possibly infected users, and Google map web service is used to provide the geographic positioning system (GPS)-based risk assessment to prevent the outbreak. It is used to represent each Zika virus (ZikaV)-infected user, mosquito-dense sites and breeding sites on the Google map that help the government healthcare authorities to control such risk-prone areas effectively and efficiently. The proposed system is deployed on Amazon EC2 cloud to evaluate its performance and accuracy using data set for 2 million users. Our system provides high accuracy of 94.5% for initial diagnosis of different users according to their symptoms and appropriate GPS-based risk assessment.     

Firm Opacity Lies in the Eye of the Beholder

We classify and test empirical measures of firm opacity and document theoretical and empirical inconsistencies across these proxies by testing the relative opacity of banks versus non‐banks. We evaluate the effectiveness of these proxies by observing the effect of two cleanly identified shocks to firm‐specific information: credit rating initiation and inclusion in the S&P 500 index. Using a difference‐in‐difference approach, we compare firms that are newly rated and firms that are included in the S&P 500 index with a propensity matched sample of “unchanged” firms. We find that only the number of analysts and Amihud's illiquidity ratio provide consistent patterns across different estimation specifications and different econometric settings. These two proxies show that banks are more opaque than non‐banks. Based on our tests, we recommend that these proxies be used as the primary measures of firm opacity.     

Corporate governance,life cycle,and payout precommitment: An emerging market study

We analyze the role of firm‐level corporate governance in determining the precommitment payout policy of emerging market firms and investigate whether there is a precommitment life‐cycle effect. Unlike previous studies of U.S. firms, we find evidence of precommitment only among relatively well‐governed firms, which combine good governance with large dividend payouts to shareholders and large debt‐related repayments to creditors. We also document a strong precommitment life‐cycle effect. Firms in the growth and mature stages of their life cycle tend to use both debt and dividends to precommit to investors, with an increasing proportion of dividends in total payout measures. Our results are robust to an array of control variables, alternate payout proxies, market setting, and firm‐level corporate governance, and it addresses potential endogeneity concerns in the sample.     

Intrahousehold moral hazard frictions and household poverty traps in rural India

We empirically study the role of assets held by women in the creation of household wealth using data from rural India. We design a streamlined model of intrahousehold project funding where moral hazard frictions between spouses and women's asset control are the main ingredients. As predicted by the model, the data show that household asset accumulation depends on women's asset control in a non-monotonic way. Results indicate no presence of multiple equilibrium poverty traps, but do show that exogenous negative shocks will trigger assets aggregation within households where both spouses are present. This resilience mechanism is, however, not found in female headed household as these households have a monotonic relationship between women's wealth control and asset creation. We thus argue that policies to support women's empowerment need to distinguish women based on their individual wealth levels and headship status to enhance household well-being in remote Indian communities.     

Unconventional monetary policy in emerging markets

Channels of monetary transmission likely to work in an emerging market (EM) are presented. The Indian accommodative policy cycle, and the papers in this special issue, is used to analyse unconventional aspects of EM monetary policy. It is argued that conditions used to justify unconventional monetary policy in advanced economies routinely hold in EMs.     

European Inflation and the New Keynesian Phillips Curve

The majority of macroeconomists who have studied inflation dynamics in Europe argue that the New Keynesian Phillips Curve (NKPC) provides a good way to describe changes in the price level from the 1970 on. However the vast majority of these articles estimate the NKPC using the labor income share as the proxy for real marginal cost. This article argues that the labor share cannot serve as a proxy for real marginal cost because of its countercyclicality and the lack of labor adjustment costs. Once we account for labor adjustment costs, we are able to derive a new marginal cost proxy that is indeed procyclical; however, we find that the NKPC fails to produce coefficients consistent with its underlying structural parameters when a procyclical marginal cost proxy is used. This casts serious doubt whether the NKPC is empirically applicable to Europe or, for that matter, any other country.     

American options under stochastic volatility: control variates,maturity randomization & multiscale asymptotics

American options are actively traded worldwide on exchanges, thus making their accurate and efficient pricing an important problem. As most financial markets exhibit randomly varying volatility, in this paper we introduce an approximation of an American option price under stochastic volatility models. We achieve this by using the maturity randomization method known as Canadization. The volatility process is characterized by fast and slow-scale fluctuating factors. In particular, we study the case of an American put with a single underlying asset and use perturbative expansion techniques to approximate its price as well as the optimal exercise boundary up to the first order. We then use the approximate optimal exercise boundary formula to price an American put via Monte Carlo. We also develop efficient control variates for our simulation method using martingales resulting from the approximate price formula. A numerical study is conducted to demonstrate that the proposed method performs better than the least squares regression method popular in the financial industry, in typical settings where values of the scaling parameters are small. Further, it is empirically observed that in the regimes where the scaling parameter value is equal to unity, fast and slow-scale approximations are equally accurate.