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.

Consumption, aggregate wealth and expected stock returns: a quantile cointegration approach

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ricardo Quineche
Keyword(s):  
Stock Returns ◽  
The United States ◽  
Labor Income ◽  
Left Tail ◽  
Long Run ◽  
Short Run ◽  
Excess Stock Returns ◽  
Aggregate Wealth ◽  
Risk Free Rate ◽  
The Right

Abstract This paper empirically examines the long-run relationship between consumption, asset wealth and labor income (i.e., cay) in the United States through the lens of a quantile cointegration approach. The advantage of using this approach is that it allows for a nonlinear relationship between these variables depending on the level of consumption. We estimate the coefficients using a Phillips–Hansen type fully modified quantile estimator to correct for the presence of endogeneity in the cointegrating relationship. To test for the null of cointegration at each quantile, we apply a quantile CUSUM test. Results show that: (i) consumption is more sensitive to changes in labor income than to changes in asset wealth for the entire distribution of consumption, (ii) the elasticity of consumption with respect to labor income (asset wealth) is larger at the right (left) tail of the consumption distribution than at the left (right) tail, (iii) the series are cointegrated around the median, but not in the tails of the distribution of consumption, (iv) using the estimated cay obtained for the right (left) tail of the distribution of consumption improves the long-run (short-run) forecast ability on real excess stock returns over a risk-free rate.

PREDICTABILITY OF MOVING AVERAGE RULES AND NONLINEAR PROPERTIES OF STOCK RETURNS: EVIDENCE FROM THE CHINA STOCK MARKET

2011 ◽  
Vol 07 (02) ◽  
pp. 267-279 ◽  
Author(s):  
Zhigang Wang ◽  
Yong Zeng ◽  
Heping Pan ◽  
Ping Li
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Dynamic Process ◽  
Linear Models ◽  
Moving Average ◽  
Ann Model ◽  
Nonlinear Properties ◽  
China Stock Market ◽  
Artificial Neural Network Ann ◽  
Predictability Of Returns

This paper investigates the predictability of moving average rules for the China stock market. We find that buy signals generate higher returns and less volatility, while returns following sell signals are negative and more volatile. Moreover, the bootstrapping results indicate that the asymmetrical patterns of return and volatility between buy and sell signals cannot be explained by four popular linear models of returns, especially the phenomenon of negative sell returns. We then test the nonlinear dynamic process of returns. Although the existing artificial neural network (ANN) model can replicate the negative sell returns, it fails to capture the volatility patterns of buy and sell returns. Furthermore, we introduce the conditional heteroskedasticity structure into the ANN model and find that the revised ANN model cannot only explain the predictability of returns, but can also capture the patterns of buy and sell volatility, which are never achieved by any linear model of returns tested in the related literature. Therefore, we conclude that the moving average trading rules can pick up some of the hidden nonlinear patterns in the dynamic process of stock returns, which may be the reason why they can be used to predict price changes.

Explaining Puzzles

2017 ◽  
Author(s):  
Kerry E. Back
Keyword(s):  
Equity Premium ◽  
Labor Income ◽  
Income Risk ◽  
Labor Income Risk ◽  
Catching Up ◽  
Long Run ◽  
Rare Disasters ◽  
Risk Free Rate ◽  
Long Run Risks ◽  
Free Rate

Various models proposed to explain the equity premium or risk‐free rate puzzle are explained: external habits (Abel’s “catching up with the Joneses” model and the Campbell‐Cochrane model), rare disasters, Epstein‐Zin‐Weil utility, long run risks, and idiosyncratic uninsurable labor income risk. External habits allow the SDF to be variable without requiring high variability of consumption. The SDF for a representative investor with Epstein‐Zin‐Weil utility depends on consumption and the market return. It is most useful when the world is not IID, as in the long‐run risks model. With uninsurable labor income risk, there is no representative investor even if investors all have the same CRRA utility, and there is additional exibility to explain asset returns.

scholarly journals The CCAPM with Varying Preferences

2017 ◽  
Vol 12 (2) ◽  
pp. 199
Author(s):  
Samih Antoine Azar
Keyword(s):  
Risk Aversion ◽  
Euler Equation ◽  
Risk Premium ◽  
Capital Asset Pricing ◽  
Long Run ◽  
Short Run ◽  
The Difference ◽  
Risk Free Rate ◽  
Long Run Risk ◽  
Free Rate

The Consumption Capital Asset Pricing Model (CCAPM) is by now a paradigm in financial economics. Applied to the risk-free rate, the CCAPM implies an Euler equation which depends on expected marginal utilities. The paper uses a widespread functional form to specify the utility. However the paper introduces varying preferences into the Euler equation. This enables us to find a relation between the current risk-free rate and the current level of real per capita consumption. Empirically this relation finds that risk aversion is lower for the short run and higher for the long run. The difference between the two is economically small but it is still statistically significant. The paper calculates the differential risk premium required to compensate for the higher long run risk aversion. This premium is also economically small. The paper concludes that the evidence supports that, in the long run, risk is either the same or higher than the short run risk.

scholarly journals Moving Average Market Timing in European Energy Markets: Production Versus Emissions

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3281
Author(s):  
Chia-Lin Chang ◽  
Jukka Ilomäki ◽  
Hannu Laurila ◽  
Michael McAleer
Keyword(s):  
Renewable Energy ◽  
Carbon Emission ◽  
Oil And Gas ◽  
Moving Average ◽  
Market Timing ◽  
Asset Markets ◽  
Price Series ◽  
Stochastic Trends ◽  
Long Run ◽  
Returns Predictability

This paper searches for stochastic trends and returns predictability in key energy asset markets in Europe over the last decade. The financial assets include Intercontinental Exchange Futures Europe (ICE-ECX) carbon emission allowances (the main driver of interest), European Energy Exchange (EEX) Coal ARA futures and ICE Brent oil futures (reflecting the two largest energy sources in Europe), Stoxx600 Europe Oil and Gas Index (the main energy stock index in Europe), EEX Power Futures (representing electricity), and Stoxx600 Europe Renewable Energy index (representing the sunrise energy industry). This paper finds that the Moving Average (MA) technique beats random timing for carbon emission allowances, coal, and renewable energy. In these asset markets, there seems to be significant returns predictability of stochastic trends in prices. The results are mixed for Brent oil, and there are no predictable trends for the Oil and Gas index. Stochastic trends are also missing in the electricity market as there is an ARFIMA-FIGARCH process in the day-ahead power prices. The empirical results are interesting for several reasons. We identified the data generating process in EU electricity prices as fractionally integrated (0.5), with a fractionally integrated Generalized AutoRegressive Conditional Heteroscedasticity (GARCH) process in the residual. This is a novel finding. The order of integration of order 0.5 implies that the process is not stationary but less non-stationary than the non-stationary I(1) process, and that the process has long memory. This is probably because electricity cannot be stored. Returns predictability with MA rules requires stochastic trends in price series, indicating that the asset prices should obey the I(1) process, that is, to facilitate long run returns predictability. However, all the other price series tested in the paper are I(1)-processes, so that their returns series are stationary. The empirical results are important because they give a simple answer to the following question: When are MA rules useful? The answer is that, if significant stochastic trends develop in prices, long run returns are predictable, and market timing performs better than does random timing.

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