Pricing Risky Assets in Indian Market under Unconditional Value Weighted Method

India is a very vast market for internet services as it has over 480 million active internet users in the country. Music streaming services in India is emerging day by day. The competition in the market is so high that even two giants Jio Music and Saavn join their hand in 2018 to provide a combine service all across the globe. In, 2019 a global giant Spotify entered into music streaming market in India and affected the each music service in India. Gaana owned by Times Internet have over 150 million active monthly users in the country while JioSaavn reported 100 million active monthly users as per a website. This research is going to study the market capture of various music streaming services in India. Currently, as per the research, Spotify is the most popular streaming service. As per the literature available on various platforms other streaming services were holding the major proportion of the Indian market but after the launch of Spotify, it became most loved streaming service. The research is being done to find out the existing music streaming services are affected by the entrance of Spotify or not

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Assessment of Reverse Mortgage Products in Indian Market

SSRN Electronic Journal ◽

10.2139/ssrn.1330056 ◽

2009 ◽

Author(s):

Gireesh Chandra Tripathi

Keyword(s):

Indian Market ◽

Reverse Mortgage

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Does Indian Market Smile: Implied Risk Neutral Density for the Indian Options Market

SSRN Electronic Journal ◽

10.2139/ssrn.2482013 ◽

2014 ◽

Author(s):

Abhishek Kumar ◽

S. Kumar

Keyword(s):

Neutral Density ◽

Options Market ◽

Indian Market ◽

Risk Neutral ◽

Risk Neutral Density

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The Expected Return on Risky Assets: International Long-run Evidence

SSRN Electronic Journal ◽

10.2139/ssrn.3546005 ◽

2020 ◽

Cited By ~ 1

Author(s):

Dmitry Kuvshinov ◽

Kaspar Zimmermann

Keyword(s):

Expected Return ◽

Risky Assets ◽

Long Run

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DYNAMIC ASSET ALLOCATION FOR TARGET DATE FUNDS UNDER THE BENCHMARK APPROACH

Astin Bulletin ◽

10.1017/asb.2021.6 ◽

2021 ◽

pp. 1-26

Author(s):

Jin Sun ◽

Dan Zhu ◽

Eckhard Platen

Keyword(s):

Asset Allocation ◽

Dynamic Asset Allocation ◽

Growth Optimal Portfolio ◽

Risky Assets ◽

Target Date ◽

Target Date Funds ◽

Asset Allocations ◽

Investment Choices ◽

Glide Paths

ABSTRACT Target date funds (TDFs) are becoming increasingly popular investment choices among investors with long-term prospects. Examples include members of superannuation funds seeking to save for retirement at a given age. TDFs provide efficient risk exposures to a diversified range of asset classes that dynamically match the risk profile of the investment payoff as the investors age. This is often achieved by making increasingly conservative asset allocations over time as the retirement date approaches. Such dynamically evolving allocation strategies for TDFs are often referred to as glide paths. We propose a systematic approach to the design of optimal TDF glide paths implied by retirement dates and risk preferences and construct the corresponding dynamic asset allocation strategy that delivers the optimal payoffs at minimal costs. The TDF strategies we propose are dynamic portfolios consisting of units of the growth-optimal portfolio (GP) and the risk-free asset. Here, the GP is often approximated by a well-diversified index of multiple risky assets. We backtest the TDF strategies with the historical returns of the S&P500 total return index serving as the GP approximation.

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Light a lamp and look at the stock market

Financial Innovation ◽

10.1186/s40854-021-00232-6 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Radeef Chundakkadan

Keyword(s):

Stock Market ◽

Financial Distress ◽

Investor Sentiment ◽

Downside Risk ◽

Bond Yields ◽

Risky Assets ◽

Market Participants ◽

Market Return ◽

The Impact

AbstractIn this study, we investigate the impact of the light-a-lamp event that occurred in India during the COVID-19 lockdown. This event happened across the country, and millions of people participated in it. We link this event to the stock market through investor sentiment and misattribution bias. We find a 9% hike in the market return on the post-event day. The effect is heterogeneous in terms of beta, downside risk, volatility, and financial distress. We also find an increase (decrease) in long-term bond yields (price), which together suggests that market participants demanded risky assets in the post-event day.

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