Asset Pricing with Liquidity Risk: A Replication and Out-of-Sample Tests with the Recent US and the Japanese Market Data

2019 ◽  
Vol 8 (1-2) ◽  
pp. 73-110 ◽  
Author(s):  
Eiichiro Kazumori ◽  
Fei Fang ◽  
Raj Sharman ◽  
Fumiko Takeda ◽  
Hong Yu
Keyword(s):  
Asset Pricing ◽  
Liquidity Risk ◽  
Japanese Market ◽  
Market Data ◽  
Out Of Sample

Liquidity Risk And Asset Pricing :, Evidence From Amman Stock Exchange

2017 ◽  
pp. 129
Author(s):  
محمد أحمد بني هاني ◽  
منى ممدوح المولا
Keyword(s):  
Asset Pricing ◽  
Stock Exchange ◽  
Liquidity Risk ◽  
Amman Stock Exchange

scholarly journals Further tests of asset pricing models: Liquidity risk matters

2021 ◽  
Vol 95 ◽  
pp. 255-273
Author(s):  
Xiuli Ma ◽  
Xindong Zhang ◽  
Weimin Liu
Keyword(s):  
Asset Pricing ◽  
Liquidity Risk ◽  
Pricing Models ◽  
Asset Pricing Models

Real-Time Profitability of Published Anomalies: An Out-of-Sample Test

2013 ◽  
Vol 03 (03n04) ◽  
pp. 1350016 ◽  
Author(s):  
Jing-Zhi Huang ◽  
Zhijian Huang
Keyword(s):  
Asset Pricing ◽  
Transaction Costs ◽  
Real Time ◽  
Empirical Evidence ◽  
Equity Market ◽  
Data Snooping ◽  
Asset Pricing Anomalies ◽  
Pricing Anomalies ◽  
Out Of Sample ◽  
Sample Test

Empirical evidence on the out-of-sample performance of asset-pricing anomalies is mixed so far and arguably is often subject to data-snooping bias. This paper proposes a method that can significantly reduce this bias. Specifically, we consider a long-only strategy that involves only published anomalies and non-forward-looking filters and that each year recursively picks the best past-performer among such anomalies over a given training period. We find that this strategy can outperform the equity market even after transaction costs. Overall, our results suggest that published anomalies persist even after controlling for data-snooping bias.

scholarly journals Improved Dividend Estimation from Intraday Quotes

Entropy ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. 95
Author(s):  
Pontus Söderbäck ◽  
Jörgen Blomvall ◽  
Martin Singull
Keyword(s):  
Least Squares ◽  
Weighted Least Squares ◽  
Statistical Significance ◽  
Ordinary Least Squares ◽  
Options Market ◽  
Market Data ◽  
Out Of Sample ◽  
Intraday Data ◽  
Least Squares Estimates ◽  
Estimation Quality

Liquid financial markets, such as the options market of the S&P 500 index, create vast amounts of data every day, i.e., so-called intraday data. However, this highly granular data is often reduced to single-time when used to estimate financial quantities. This under-utilization of the data may reduce the quality of the estimates. In this paper, we study the impacts on estimation quality when using intraday data to estimate dividends. The methodology is based on earlier linear regression (ordinary least squares) estimates, which have been adapted to intraday data. Further, the method is also generalized in two aspects. First, the dividends are expressed as present values of future dividends rather than dividend yields. Second, to account for heteroscedasticity, the estimation methodology was formulated as a weighted least squares, where the weights are determined from the market data. This method is compared with a traditional method on out-of-sample S&P 500 European options market data. The results show that estimations based on intraday data have, with statistical significance, a higher quality than the corresponding single-times estimates. Additionally, the two generalizations of the methodology are shown to improve the estimation quality further.

scholarly journals AN EMPIRICAL APPROACH TO FINANCIAL CRISIS INDICATORS BASED ON RANDOM MATRICES

2018 ◽  
Vol 21 (03) ◽  
pp. 1850022 ◽  
Author(s):  
RAPHAEL DOUADY ◽  
ANTOINE KORNPROBST
Keyword(s):  
Decision Making ◽  
Financial Crisis ◽  
Correlation Matrix ◽  
Spectral Properties ◽  
Predictive Power ◽  
Dynamic Instability ◽  
Market Data ◽  
Rolling Window ◽  
Out Of Sample ◽  
Weighted Correlation

The aim of this work is to build a class of financial crisis indicators based on the spectral properties of the dynamics of market data. After choosing an appropriate size for a rolling window, the historical market data inside this rolling window are seen every trading day as a random matrix from which a correlation matrix is obtained. Our goal is to study the correlations between the assets that constitute this market and look for reproducible patterns that are indicative of an impending financial crisis. A weighting of the assets in the market is then introduced and is proportional to the daily traded volumes. This manipulation is realized in order to give more importance to the most liquid assets. Our financial crisis indicators are based on the spectral radius of this weighted correlation matrix. The idea behind this type of financial crisis indicators is that large eigenvalues are a sign of dynamic instability. The out-of-sample predictive power of the financial crisis indicators in this framework is then demonstrated, in particular by using them as decision-making tools in a protective put strategy.

scholarly journals Asset Pricing with Liquidity Risk

2012 ◽  
pp. 137-184 ◽  
Author(s):  
Yakov Amihud ◽  
Haim Mendelson ◽  
Lasse Heje Pedersen
Keyword(s):  
Asset Pricing ◽  
Liquidity Risk

Liquidity risk premium and asset pricing in US water transportation

2013 ◽  
Vol 52 ◽  
pp. 3-15 ◽  
Author(s):  
Photis M. Panayides ◽  
Neophytos Lambertides ◽  
Kevin Cullinane
Keyword(s):  
Asset Pricing ◽  
Risk Premium ◽  
Liquidity Risk ◽  
Water Transportation

scholarly journals Asset Pricing with Liquidity Risk

2003 ◽  
Author(s):  
Viral V. Acharya ◽  
Lasse Heje Pedersen
Keyword(s):  
Asset Pricing ◽  
Liquidity Risk
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