In this book, the relevance of inflation and the impact of inflation variability on stock prices and returns are empirically investigated.
The statistical
evidence for the common statistically significant and negative bivariate
relationship between inflation and stock returns is presented at the beginning
of Chapter 1. This chapter makes the case and provides evidence that the
presence of a supplemental basic variable makes the ostensibly negative
relationship disappear. Actual, expected, unexpected, positive unexpected,
negative unexpected, unexpected positive inflation rate in periods of positive
growth, unexpected positive inflation rate in periods of negative growth,
unexpected negative inflation rate in periods of positive growth, and
unexpected negative inflation rate in periods of positive growth are all possible
values for the inflation variable. The Dow Jones Industrial Average's log
returns and the S&P 500 stock market index are used to compute stock
returns.
The impact of inflation
fluctuation on stock returns is examined in Chapter 2. Either absolute inflation
or the square of inflation can be used to determine inflation variability.
Bivariate regressions show a negative and statistically significant
relationship between inflation variability and stock returns. The coefficient
on the inflation variable becomes statistically negligible when both inflation
and inflation variability are included as regressors, but the coefficient on
inflation variability remains statistically significant and negative. As a
result, the impact of inflation's fluctuation rules the empirical landscape.
The coefficients of both inflation and inflation variability become
statistically irrelevant when a basic variable is included, namely the change
in the cost of equity. This provides more proof why inflation is irrelevant.
In Chapter 3, the
impact of inflation on stock returns is recalculated while accounting for an
endogenous calendar break and the existence of two Markov switching regimes.
The findings demonstrate the existence of two regimes and an endogenous
calendar break. The relationship between inflation and stock returns is
statistically significant and negative in one subsample and one regime, but
there is no statistical relationship in the other subsample or the other
regime. Further investigation reveals that conditional heteroscedasticity and a
non-stationary probability distribution of the inflation variable are what are
responsible for the statistically significant results. Strong evidence is shown
in favour of a straightforward theoretical stock market model that includes the
same fundamental variable as in Chapters 1 and 2.
The Gordon dividend
discount model, which is used in Chapter 4, is based on the same basic stock
model of dividend growth. According to this model, changes in the cost of
equity, the exchange rate of the US dollar, and changes in aggregate demand
account for all stock return explanations; inflation and inflation uncertainty
have no further explanatory power. The strategy is based on least squares
regressions, endogenous calendar breakpoints, and GARCH models. It is
discovered that both the inflation and inflation uncertainty variables are
statistically negligible. Only
one subsample—the most recent one—shows a statistically significant impact of
the US dollar; the break occurred in July 1998. After this date, it is
predicted that a 1% decline in the value of the US dollar will result in a 1.6%
rise in stock prices.
Estimating the impact
of inflation on nominal and real stock returns as well as the equity premium is
the aim of Chapter 5. The null hypothesis holds that there is no correlation
between inflation and stock returns and that inflation has a detrimental
unitary impact on both real stock returns and the equity premium. These final
two effects are a result of the inflation variable—or a substitute for
it—existing on both sides of the regression. The appendix, which addresses this
topic, is recommended to the reader. EGARCH models, Markov switching regimes,
resilient least squares, quantile regressions, and least squares regressions
with HAC robust standard errors form the foundation of the method in Chapter 5.
The variables for inflation and T-bills are
discovered to be statistically insignificant. With a unitary coefficient, the
equity premium is inversely correlated with inflation. This is as a result of
the T-bill rate's independence as a gauge of inflation.
By taking into account
the independent and combined relationships of the 30 stocks that make up the
Dow Jones Industrial Average market stock index, Chapter 6 seeks to re-estimate
the impact of inflation on stock returns. Both core inflation and consumer
price index inflation are looked at. According to the model, the change in the
cost of equity is the only fundamental variable that can adequately explain
stock returns; inflation has no remaining explanatory ability. The chapter uses
panel least squares, limited and unconstrained system regressions, and other
inflation index estimations in addition to linear regressions. The findings demonstrate that the inflation
irrelevance hypothesis is well-supported, particularly when core inflation is
included in the research. Inflation is proven to be unrelated to stock returns,
regardless of the stock or econometric method. There are at least three reasons
why this chapter is unique. The first is to examine the 30 Dow individual
stocks for relevance rather than using market indexes as is customary in the
literature. The second is to use multiple econometric techniques, including
cross equation dependencies in both unconstrained and limited system
regressions. The third step is to perform panel least squares analysis.
By taking into account
the independent and combined relationships of the following six US market stock
indices: AMEX, DJIA, NASDAQ, NYSE, RUSSEL, and S&P 500, Chapter 7 aims to
re-estimate the impact of inflation on stock returns. The null hypothesis
assumes that there is no correlation. The price variable is decided to be core
inflation. The chapter creates a market stock model that takes dividend growth
to be constant. The four basic variables that make up this model's prediction
that stock returns can be explained by changes in the cost of equity, There is no longer any ability to explain
inflation using domestic aggregate demand, the VIX volatility index, or the US
dollar. Panel least squares, limited and unconstrained system regressions, and
linear regressions are all used in this chapter. The findings demonstrate that
the inflation irrelevance hypothesis is well supported. Inflation is determined
to be unrelated to stock returns, regardless of stock index or econometric
method. At least three reasons—similar to those for Chapter 6—make this chapter
unique.
In Chapter 8, the 20
Fama-French stocks are used to re-estimate the impact of inflation on stock
returns by taking independent and combined relations into account. The chapter
provides a market stock model that accounts for dividend increase that is
constant across time. The model predicts that four basic variables—the change
in the cost of equity, domestic aggregate demand, the VIX volatility index, and
the US dollar—explain stock return trends, leaving inflation out of the
picture. The existence of outliers is properly taken into consideration.
Implemented are two outlier-resistant econometric techniques. The findings demonstrate that the inflation irrelevance
hypothesis is well supported. Inflation is determined to be unrelated to stock
returns regardless of the stock portfolio or the econometric method. The
application of two robust least squares and quantile regressions, which are
robust to the presence of outliers, makes the chapter unique for at least one
reason.
The stock indices of 17
different nations are tested in Chapter 9 for their relevance to inflation. On
17 distinct international stock indexes, the chapter creates a theoretical
stock model and uses correlation and least squares analysis, including robust
least squares and quantile regressions. Positive and negative inflation rates
both have a non-linear impact, which is also researched. The chapter
demonstrates that there are no non-linearities and that inflation irrelevance
has strong international backing. A panel of the 16 nations studied in Chapter 9 is used in Chapter 10 to
examine the relevance of inflation. Turkey, the 17th nation in Chapter 9, is
left out because it stands out as an obvious exception. On these 16 worldwide
market indexes, the chapter performs panel least squares analysis and creates a
theoretical stock model. Additional techniques include robust least squares,
quantile regressions, and full information maximum likelihood. The inflation
variable is specified in both a linear and a non-linear manner. The chapter
demonstrates that the inflation/stock return relationship is linear and that
inflation irrelevance is well supported. It should be noted that the change in
the inflation rate has a statistically significant negative coefficient.
But the duration effect, which is the same
duration effect as for the change in real interest rates, is captured by this
change variable. It brings to mind the Fisher hypothesis, which divides changes
in nominal interest rates into changes in real interest rates and changes in
the rate of inflation.
Estimating the
relationship between inflation variability and inflation is the aim of Chapter
11. The absence of an association is the null hypothesis, which is accepted and
put to the test. The main innovation involves developing a GARCH specification
and setting up a full-fledged macroeconomic model of the inflation rate (the
mean equation) rather than reverting to an ad hoc ARMA model (the conditional
variance equation). The variance equation contains the inflation rate, and the
mean equation contains the GARCH variable. The macro model has strong support,
and this strong support is further confirmed by using Monte Carlo
bootstrapping. Furthermore, a causal relationship between inflation variability
and inflation has a lot of evidence to back it up.
The purpose of Chapter
12 is to estimate the impact of inflation on stock returns while taking into
account the potential occurrence of calendar breakpoints brought on by
structural changes in a particular economy, varying political and policy
regimes, or even global events and spillovers from other countries. The five
emerging nations that the author did not test in Chapters 9 and 10 are included
in the analysis. Even when breaks are permitted, there is no relationship,
according to the null hypothesis. Standard linear and multiple regression
analyses are used in this chapter. It searches for statistical anomalies that,
if they exist, could cast doubt on both the sampling process and the
econometric findings. These fractures are determined endogenously. The chapter examines two regression analysis
models, one that is merely bound by inflation and the other that is
unconstrained and takes five independent variables taken from a theoretical
model into account. For Brazil, Indonesia, and Mexico, statistical breaks are
discovered in this chapter. 1988M05, 1998M11, 2000M08, and 2003M07 are the
relevant years. No break is present for Chile and Colombia. Only the Brazilian
sample and the small subsample prior to the break show a significant connection
with the breaks. For the other four emerging nations—Chile, Colombia,
Indonesia, and Mexico—inflation is supported as irrelevant. Despite the breaks,
the relationship is clearly null and void. One criticism of the data showing there is no correlation between stock
returns and inflation is that the sample study has calendar breaks due to
institutional differences between the various nations. This chapter attempts to
refute this claim and to add to the substantial body of scholarship on
inflation's lack of relevance.
The researcher is then
drawn to the appendix's discussion of the econometric error of using the same
variable on both sides of the regression equation. The work takes into account
both a theoretical and an empirical proof. The estimated outcomes in both
situations are consistent with a significant bias in the regression slope
estimate. Furthermore, statistical support can be obtained in cases where it is
not necessary. This econometric issue appears frequently throughout the text,
which is why it is appended.
Author (s) Details
Samih
Antoine Azar
Faculty of Business
Administration & Economics, Haigazian University, Lebanon.
View Book :- https://stm.bookpi.org/IIVSRTIIP/article/view/7756
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