scholarly journals On Time-Series Properties of Time-Varying Risk Premium in the Yen/Dollar Exchange Market

10.3386/w2678 ◽  
1988 ◽  
Author(s):  
Fabio Canova ◽  
Takatoshi Ito
Keyword(s):  
Time Series ◽  
Risk Premium ◽  
Time Varying ◽  
Exchange Market

scholarly journals Anticipated Volatility, Unanticipated Volatility Shocks, Excess Monthly Returns and Stock Market Behaviour of Pakistan

2021 ◽  
Vol 7 (1) ◽  
pp. 77-91
Author(s):  
Muhammad Ramzan Sheikh ◽  
Sahrish Zameer ◽  
Sulaman Hafeez Siddiqui
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Portfolio Choice ◽  
Risk Premium ◽  
Time Series Data ◽  
Stock Exchange ◽  
Series Data ◽  
Financial Assets ◽  
Exchange Market ◽  
Risk Return

An investor considers various factors to choose the financial assets. The portfolio theory suggests that risk, return, taxes, information and liquidity are vital factors in portfolio choice. The study is based on risk premium, uncertainty, shocks and volatility of Pakistan stock exchange market. The study has used monthly time series data of returns of ten sectors of Pakistan stock market ranging from 2006 to 2014 to measure the anticipated and unanticipated factors of risk, return and uncertainty. Using CAPM, it is pointed out that volatility factor is present and high in overall stock market and the level of volatility in different sectors of the market moves in the same direction which suggest that speculative activities are widely spread in every sector and in overall market as well.

scholarly journals Estimation of Market Risk Premium for Japan

2009 ◽  
Vol 1 (1) ◽  
Author(s):  
Seoungpil AHN ◽  
Keshab SHRESTHA
Keyword(s):  
Time Series ◽  
Risk Aversion ◽  
Kalman Filtering ◽  
Risk Premium ◽  
Time Varying ◽  
Random Walk Process ◽  
Market Risk Premium ◽  
Time Varying Parameter ◽  
Variance Risk ◽  
Over Time

In this paper, the time series of risk aversion parameter is estimated for the Japanese stock market using weekly return data covering 2/7/1973 to 12/27/2000. The time series of risk aversion parameter is estimated with the Time Varying Parameter (EVP) GARCH-M model proposed by Chou, Engle and Kane (1992), which allows for the risk aversion parameter to change over time by modeling the risk aversion parameter to follow a random walk process. The risk aversion parameter is found to range between 3.5 to 2.2. We also find that the risk aversion parameter has not significantly changed over time. This implies that most of the variation in excess return can be explained by the variation in the market (variance) risk. Keywords: GARCH-M, Kalman Filtering, risk aversion, time-varying parameter, volatility.

Risk Premium Estimation in the Colombian Foreign Exchange Market

2016 ◽  
Author(s):  
Javier Orlando Pantoja ◽  
Federico Mejja-Posada ◽  
Sebastiin Bedoya-RRos
Keyword(s):  
Foreign Exchange ◽  
Risk Premium ◽  
Foreign Exchange Market ◽  
Exchange Market

Selective Linear Segmentation for Detecting Relevant Parameter Changes*

2020 ◽  
Author(s):  
Arnaud Dufays ◽  
Elysee Aristide Houndetoungan ◽  
Alain Coën
Keyword(s):  
Time Series ◽  
Hedge Fund ◽  
Expectation Maximization Algorithm ◽  
Model Parameters ◽  
Relevant Parameter ◽  
Time Varying ◽  
Long Time ◽  
Model Selection Uncertainty ◽  
Penalized Likelihood Approach ◽  
Changes Over Time

Abstract Change-point (CP) processes are one flexible approach to model long time series. We propose a method to uncover which model parameters truly vary when a CP is detected. Given a set of breakpoints, we use a penalized likelihood approach to select the best set of parameters that changes over time and we prove that the penalty function leads to a consistent selection of the true model. Estimation is carried out via the deterministic annealing expectation-maximization algorithm. Our method accounts for model selection uncertainty and associates a probability to all the possible time-varying parameter specifications. Monte Carlo simulations highlight that the method works well for many time series models including heteroskedastic processes. For a sample of fourteen hedge fund (HF) strategies, using an asset-based style pricing model, we shed light on the promising ability of our method to detect the time-varying dynamics of risk exposures as well as to forecast HF returns.

Market reaction to scheduled and unscheduled news announcements under varying economic conditions

2020 ◽  
Author(s):  
◽  
Parveshsingh Seeballack
Keyword(s):  
Stock Returns ◽  
Risk Premium ◽  
Implied Volatility ◽  
Equity Market ◽  
Economic Conditions ◽  
Time Varying ◽  
Equity Risk Premium ◽  
Macroeconomic News ◽  
Equity Risk ◽  
News Announcements

The unifying theme of this dissertation is the study of the role of macroeconomic news announcements in the context of the equity market. We focus on two important areas of the asset pricing theory, namely price discovery and equity risk premium forecasting. Chapter 2 investigates the time-varying sensitivity of stock returns to scheduled macroeconomic news announcements (MNAs) using high-frequency data. We present new insights into how efficiently stock returns incorporate the informational content of MNAs. We further provide evidence that the stock market response to MNAs is cyclical, and finally we conclude Chapter 2 with an investigation into the factors driving the time-varying sensitivity of stock return to MNAs. Chapter 3 investigates the time-varying sensitivity of stock returns in the context of unscheduled macroeconomic news announcements using high-frequency data. We investigate the speed and persistence in stock returns’ response to unscheduled macro-news announcements, and whether the reactions are dependent on the state of the economy, or general investor sentiment level. Combined, Chapters 2 and 3 provide interesting insights into how equity market participants react to the arrival of scheduled and unscheduled macro-announcements, under varying economic conditions. Chapter 4 focuses on equity risk premium forecasting. We investigate the predictive ability of option-implied volatility variables at monthly horizon, under varying economic conditions. We innovate by constructing monthly announcement and non-announcement option-implied volatility predictors and assess whether the monthly announcement option-implied volatility predictors contain additional information for better out-of-sample predictions of the monthly equity risk premium. Each of the three empirical chapters explores a unique aspect of the asset pricing theory in the context of the U.S. equity market.

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