Risk-free rate effects on conditional variances and conditional correlations of stock returns

2014 ◽  
Vol 25 ◽  
pp. 95-111 ◽  
Author(s):  
Alessandro Palandri
Keyword(s):  
Stock Returns ◽  
Rate Effects ◽  
Conditional Correlations ◽  
Risk Free Rate ◽  
Free Rate

scholarly journals Long Run Returns Predictability and Volatility with Moving Averages

Risks ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 105 ◽  
Author(s):  
Chia-Lin Chang ◽  
Jukka Ilomäki ◽  
Hannu Laurila ◽  
Michael McAleer
Keyword(s):  
Financial Performance ◽  
Stock Returns ◽  
Moving Average ◽  
Long Run ◽  
Returns Predictability ◽  
Rolling Window ◽  
Risk Free Rate ◽  
Data Frequency ◽  
Predictability Of Returns ◽  
Free Rate

This paper examines how the size of the rolling window, and the frequency used in moving average (MA) trading strategies, affects financial performance when risk is measured. We use the MA rule for market timing, that is, for when to buy stocks and when to shift to the risk-free rate. The important issue regarding the predictability of returns is assessed. It is found that performance improves, on average, when the rolling window is expanded and the data frequency is low. However, when the size of the rolling window reaches three years, the frequency loses its significance and all frequencies considered produce similar financial performance. Therefore, the results support stock returns predictability in the long run. The procedure takes account of the issues of variable persistence as we use only returns in the analysis. Therefore, we use the performance of MA rules as an instrument for testing returns predictability in financial stock markets.

Long-run stock market returns

2021 ◽  
pp. 28-48
Author(s):  
Jesper Rangvid
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Risk Premium ◽  
Historical Data ◽  
Market Returns ◽  
Stock Market Returns ◽  
Long Run ◽  
International Data ◽  
Risk Free Rate ◽  
Free Rate

This chapter presents facts and concepts regarding long-run stock market returns. It starts out briefly defining stock returns.The chapter then looks at the historical data, starting with US data and then turning to international data. It decomposes stock returns into a risk-free rate and a risk premium. The chapter also introduces concepts that will be used repeatedly throughout the book, such as different kinds of averages (arithmetic and geometric), standard deviations, variances, and other important concepts in finance.The chapter presents stylized facts about long-run stock returns. It does not try to explain what generates these returns. This is the topic of subsequent chapters.

Robust Benchmarking of Indian Mutual Funds-A Partial Frontier Approach

2015 ◽  
Vol 5 (2) ◽  
Author(s):  
Ram Pratap Sinha
Keyword(s):  
Decision Making ◽  
Performance Evaluation ◽  
Mutual Funds ◽  
Mutual Fund ◽  
Multi Criteria Decision Making ◽  
Risk Framework ◽  
Programming Techniques ◽  
Risk Free Rate ◽  
Risk Ratios ◽  
Free Rate

Performance analysis of mutual funds is usually made on the basis of return-risk framework. Traditionally, excess return (over risk-free rate) to risk ratios were used for the purpose mutual fund evaluation. Subsequently, the application of non-parametric mathematical programming techniques in the context of performance evaluation facilitated multi-criteria decision making. However,the estimates of performance on the basis of conventional programming techniques like DEA and FDH are affected by the presence of outliers in the sample observations. The present, accordingly uses more robust benchmarking techniques for evaluating the performance od sectoral mutual fund schemes based on observations for the second half of 2010. The USP of the present study is that it uses two partial frontier techniques (Order-m and Order- a) which are less susceptible to the problem of extreme data.

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