Dr. Ashok Patil

Assistant Professor

PhD (SIU, Pune), PGDM (IIM, Calcutta), BE (COEP)

Research Interests & Expertise

Finance, Asset Pricing,Market Efficiency.



Samagra: A Sanitable Sustainable Business Model
Authors: Roshan Raju, Azharshaheen Shaikh, Ashok Patil, Supriya Bhagat
Publication date: April 24, 2023
URL: Access Paper
Abstract :
In March 2013 Swapnil Chaturvedi revamped Samagra in Pune, which was later supported and funded by the Bill and Melinda Gates Foundation. His main motto was to tackle the sanitation problem in India where more than 600 million people defecated in the open. The mission of Swapnil’s venture was to create clean toilets for the underprivileged. But in his quest to achieve the mission of Samagra, he faced a major challenge. He wanted to create a Sanitation Business Model which becomes self-sustainable and provides a life refining value-added service to the urban poor. Will he be able to reach this dream and unleash the potential of his venture Samagra and see it thrive and scale it up?


Multifractal Analysis of Market Efficiency across Structural Breaks: Implications for the Adaptive Market Hypothesis
Authors: Patil, A.C., Rastogi, S.
Journal:Journal of Risk and Financial Management
Publication date: October 2020
Publisher: Multidisciplinary Digital Publishing Institute (MDPI)
URL: Open Access
Abstract :

The primary objective of this paper is to assess the behavior of long memory in price, volume, and price-volume cross-correlation series across structural breaks. The secondary objective is to find the appropriate structural breaks in the price series. The structural breaks in the series are identified using the Bai and Perron procedure, and in each segment, Multifractal Detrended Fluctuation Analysis (MFDFA) and Multifractal Detrended Cross-Correlation Analysis (MFDCCA) are conducted to capture the long memory in each series. The price series is persistent in small fluctuations and anti-persistent in large fluctuations across all the structural segments. This confirms that long memory in the series is not affected by the structural breaks. Both volume and price-volume cross-correlation are anti-persistent in all the structural segments. In other words, volume acts as a carrier of the information only in the non-volatile (normal) market. The varying Hurst exponent across the structural segments indicates the varying levels of persistence and signifies the volatile market. The findings of the study are useful for understanding the practical implications of the Adaptive Market Hypothesis (AMH). © 2020 by the authors.


Time-Varying Price–Volume Relationship and Adaptive Market Efficiency: A Survey of the Empirical Literature
Authors: Patil, A.C., Rastogi, S.
Journal:Journal of Risk and Financial Management
Publication date: June 2019
Publisher: Multidisciplinary Digital Publishing Institute (MDPI)
URL: Open Access
Abstract :

This paper conducts a review of the literature on the price–volume relationship and its relation with the implications of the adaptive market hypothesis. The literature on market efficiency is classified as efficient market hypothesis (EMH) studies or adaptive market hypothesis (AMH) studies. Under each class, studies are categorized either as return predictability studies or price–volume relationship studies. Finally, review in each category is analyzed based on the methodology used. Our review shows that the literature on return predictability and price–volume relationship in classical EMH approach is extensive while studies in return predictability in the AMH approach have gained increased attention in the last decade. However, the studies in price–volume relationship under adaptive approach are limited, and there is a scope for studies in this area. Authors did not find any literature review on time-varying price–volume relationship. Authors find that there is a scope to study the nonlinear cross–correlation between price and volume using detrended fluctuation analysis (DFA)-detrended cross–correlational analysis (DXA) in the AMH domain. Further, it would be interesting to investigate whether the same cross–correlation holds across different measures of stock indices within a country and across different time scales. © 2019 by the authors.

Different VAR for Developing and Developed Nations because of Market Efficiency
Authors: Dr Shailesh Rastogi ,Ashok Patil , Akanksha Goel
Journal:A Refereed Monthly International Journal of Management
Publication date: May 2019
Publisher: Multidisciplinary Digital Publishing Institute (MDPI)
URL: Open Access
Abstract :

The aim of this paper is to compare the performance of VaR among Nations. This paper employs the method proposed by Diebold, Schuermann, and Stroughair (1998) and McNeil and Frey (2000) in order to filter the return data to obtain i.i.d residuals by fitting ARMA-GARCH models.The model that shows the lowest percentage failure rate in VaR in out-of-sample period is identified as the best GARCH model to estimate VaR.


Oil price shocks and stock market performance: A case of Indian stock market

Authors: Patil, A., Madhuri, G.
Journal: Finance India
Publication date: June 2018
Publisher: Indian Institute of Finance
URL: Open Access

Abstract :

The paper studies the empirical relationship between Oil Price Shocks and Stock Market Index movement and their asymmetric responses to oil price shocks. The Indian stock market index was represented by Sensex, and daily closing prices of Sensex and crude oil prices for a ten-year period between 2006 and 2015 wereanalyzed using dynamic linear regression or ARIMAX. The study indicated that there is no significant evidence of correlation between oil price shocks and stock market index movement; however, stock market index movement is auto-correlated with its two lags. The findings of this paper also show statistically significant asymmetric responses of stock market index movement to oil price shocks. Stock market index movement was negatively correlated with positive oil price shocks, and positively correlated with negative oil price shocks. Subsequently, the equations of the models are used to forecast the stock market index movement.This study uniquely enhances the understanding of bivariate relationships. © Indian Institute of Finance.


Modeling Volatility Clustering of Bank Index: An Empirical Study of BankNifty
Authors: Ashok Patil, Gita Madhuri, Bidyanand Jha
Journal: Review of Integrative Business and Economics Research
Publication date: January 2017
URL: Open Access
Abstract :

Time series of asset-returns often exhibit volatility clustering, and it has been observed that volatility in indices are clustered too. Volatility determines the risk-profile of an index and in-turn the payoffs of derivative positions on those indices. The objective this paper is to capture the presence of volatility clustering and model the volatility profile over a period of 10 years of BankNifty index. BankNifty index represents the twelve most liquid and large capitalized stocks from the banking sector,


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