scholarly journals The Cross-Sectional Risk Premium of Decomposed Market Volatility in UK Stock Market

2014 ◽  
Vol 02 (07) ◽  
pp. 30-38 ◽  
Author(s):  
Yan Yang ◽  
Laurence Copeland
Keyword(s):  
Stock Market ◽  
Risk Premium ◽  
Market Volatility ◽  
Cross Sectional ◽  
The Cross

Cross-herding behavior between the stock market and the crude oil market during financial distress

2018 ◽  
Vol 44 (4) ◽  
pp. 439-458 ◽  
Author(s):  
Houda BenMabrouk
Keyword(s):  
Stock Market ◽  
Crude Oil ◽  
Investor Sentiment ◽  
Market Volatility ◽  
Herding Behavior ◽  
Absolute Deviation ◽  
Cross Sectional ◽  
Content Type ◽  
Oil Market ◽  
The Cross

Purpose The purpose of this paper is to investigate herding behavior around the crude oil market and the stock market and the possible cross-herding behavior between the two markets. The analysis examines also the herding behavior during financial turmoil and includes the investor sentiment and market volatility. Design/methodology/approach The authors use a modified version of the cross-sectional standard deviation and the cross-sectional absolute deviation to include investor sentiment, financial crisis and market volatility. Findings The authors find that the volatility of the stock market reduces the herding behavior around the oil market and boosts that around the stock market. However, the investors’ sentiment reduces the herding around the stock market and boosts that around the crude oil market. Consequently, the authors can conclude that the herding behavior around the two markets moves inversely and the herding in each market is enhanced by the lack of information in the other market. Research limitations/implications This paper is limited to the herding of stocks around the crude oil market and ignores the possible herding of commodities around the oil market. Originality/value The originality of the paper rests on the study of the possible cross-herding behavior between the oil market and the stock market especially during financial turmoil.

scholarly journals Volatility Modeling: An Overview of Equity Markets in the Euro Area during COVID-19 Pandemic

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1212
Author(s):  
Pierdomenico Duttilo ◽  
Stefano Antonio Gattone ◽  
Tonio Di Di Battista
Keyword(s):  
Stock Market ◽  
Euro Area ◽  
Risk Premium ◽  
Stock Markets ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Volatility Modeling ◽  
The Impact ◽  
Second Wave ◽  
Daily Returns

Volatility is the most widespread measure of risk. Volatility modeling allows investors to capture potential losses and investment opportunities. This work aims to examine the impact of the two waves of COVID-19 infections on the return and volatility of the stock market indices of the euro area countries. The study also focuses on other important aspects such as time-varying risk premium and leverage effect. This investigation employed the Threshold GARCH(1,1)-in-Mean model with exogenous dummy variables. Daily returns of the euro area stock markets indices from 4th January 2016 to 31st December 2020 has been used for the analysis. The results reveal that euro area stock markets respond differently to the COVID-19 pandemic. Specifically, the first wave of COVID-19 infections had a notable impact on stock market volatility of euro area countries with middle-large financial centres while the second wave had a significant impact only on stock market volatility of Belgium.

The Cross Section of Expected Returns with MIDAS Betas

2011 ◽  
Vol 47 (1) ◽  
pp. 115-135 ◽  
Author(s):  
Mariano González ◽  
Juan Nave ◽  
Gonzalo Rubio
Keyword(s):  
Cross Section ◽  
Risk Premium ◽  
Market Risk ◽  
Ordinary Least Squares ◽  
Mixed Data ◽  
Expected Returns ◽  
Data Sampling ◽  
Cross Sectional ◽  
Market Risk Premium ◽  
The Cross

AbstractThis paper explores the cross-sectional variation of expected returns for a large cross section of industry and size/book-to-market portfolios. We employ mixed data sampling (MIDAS) to estimate a portfolio’s conditional beta with the market and with alternative risk factors and innovations to well-known macroeconomic variables. The market risk premium is positive and significant, and the result is robust to alternative asset pricing specifications and model misspecification. However, the traditional 2-pass ordinary least squares (OLS) cross-sectional regressions produce an estimate of the market risk premium that is negative, and significantly different from 0. Using alternative procedures, we compare both beta estimators. We conclude that beta estimates under MIDAS present lower mean absolute forecasting errors and generate better out-of-sample performance of the optimized portfolios relative to OLS betas.

MODELING THE RISK AND RETURN RELATION CONDITIONAL ON MARKET VOLATILITY AND MARKET CONDITIONS

2005 ◽  
Vol 08 (01) ◽  
pp. 75-95 ◽  
Author(s):  
DON U. A. GALAGEDERA ◽  
ROBERT FAFF
Keyword(s):  
Risk Premium ◽  
Market Volatility ◽  
Market Model ◽  
Pricing Model ◽  
Market Returns ◽  
Market Conditions ◽  
Cross Sectional ◽  
Beta Risk ◽  
Market Regimes ◽  
Volatility Regimes

This paper investigates whether the risk-return relation varies, depending on changing market volatility and up/down market conditions. Three market regimes based on the level of conditional volatility of market returns are specified — "low", "neutral" and "high". The market model is extended to allow for these three market regimes and a three-beta asset-pricing model is developed. For a set of US industry sector indices using a cross-sectional regression, we find that the beta risk premium in the three market volatility regimes is priced. These significant results are uncovered only in the pricing model that accommodates up/down market conditions.

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