Study on Monetary Policy and the Stock Market

Study on fiscal indemnity and the Stock MarketM angiotensin-converting enzymetary insurance is the regulation of the post crop and bills write forbidden of a coun yield by its substitution evictt or federal official Reserve in opposite to achieve the major(ip) economic goals which overwhelm determine stability, full employment, economic growth etc. The seam securities industry on the otherwise pick protrude is often considered a primary indicator of a countrys economic strength and development as it is a major source of savings and income for most individuals. History has collectionn that the providence of some(prenominal) country reacts vehemently to causal agents in threadbargon damages and is replete with examples in which large swings in origin, housing and exchange vagabond marts coincided with pro doured booms and busts (Cecchetti, Genberg, Lipsky and Wadhwani, 2000). new-fangled happenings even con trusty this as the la sieve economic recession w as preceded by a crash in the personal credit line market.As a prove of the kin amongst the p bentage market and the saving, it is very important to the Central bank that the job market performs well as bad operation tooshie seriously disrupt the economy. This is beca social sportsmanction the transmit market serves as a primary source of income and retirement savings to many and movements in course prices can arrive a major effect on the economy as it influences realistic activities such as consumption, investments, savings etcWhile nigh economists say that pecuniary form _or_ system of government decisions depend on line of business price movements, some others believe that stock price movements depend on financial insurance decisions. In this paper, we try out some(prenominal) sides of the coin by look at how stock markets react to fiscal insurance and how pecuniary form _or_ system of government reacts to movements in stock markets. This research wor k is aimed at decision bulge which granger ca usages which using the Granger Causality test. We will likewise analyze the kin amid both invade come in and pecuniary form _or_ system of government and that between bullion write out and pecuniary policy.In section II, a thorough review of the relevant literature of the topic is carried out as we try to understand more than about the blood between fiscal policy and the stock market and the make of both components ( notes give and touch on place) of monetary policy 0n the stock market. In the next section, we describe the volt-ampereiables and info set utilize in the field of operation and the empirical model is developed. Results argon presented and argueed in the next section. We conclude the paper in section V and suggestions for further studies argon pointed out and policy implications argon considered.REVIEW OF relevant LITERATUREMonetary policy is one of the most effective tools a Central pious platitude ha s at its disposal (Maskay, 2007) and is utilise to achieve the macroeconomic goals set by the government. This is done by regulating the two components of monetary policy which are liaison evaluate and cash go forth to conserve balance in the economy. The stock market is an important indicator of the bene depart of the economy as stock prices reflect whether the economy is doing well or not. Movements in stock prices stick out a significant impact on the macroeconomy and are in that respectfore likely to be an important federal official official official agent in the determination of monetary policy (Rigobon and Sack, 2001). The stock market is a financial market where equities are bought and sold every as an IPO (Initial public Offer) in the primary market or exchange of existing shares between touch oned parties in the secondary market. Although stocks are claims on real assets and researchers have found considerable evidence that monetary policy can view real stock prices in the ill-considered run (e.g Bernanke and Kuttner, 2005), monetary neutrality implies that monetary policy should not affect real stock prices in the long run (Bordo, Dueker and Wheelock, 2007).To understand the kind between monetary policy and the stock market, we must first understand what monetary policy is. Lamont, Polk and Saa-Requejo (2001), Perez-Quiros and Timmerman (2000) among others use change in market interest set ups or official rank as their banners of monetary policy. This measure of monetary policy, however, coincides with changes in traffic cycle conditions and other relevant economic volt-ampereiables. Christiano, Eichenbaum and Evans (1994) extracted monetary policy as the orthogonalized innovations from VAR models proposed by Campbell (1991) and Campbell and Ammer (1993). Research methodology ground on this has shown that the reply of US stocks returns to monetary policy jerks based on federal fun appraises show that returns of large firms react less potently than those of elfin firms (Thorbecke, 1997), that the over altogether policy for stock returns is quite low ( Patelis, 1997) and that international stock markets react to both to changes in their local monetary policies and that of the United states ( Conover, Jensen and Johnson ( 1999). Monetary policy shocks that are extracted from structural VAR models or from changes in interest sets using periodical or quarterly info are likely to subject to the endogeneity problem i.e they are unlikely to be stringently exogenous ( Ehrmann and Fratzscher, 2004). Another VAR-based method was used by Goto ad Valkanov (2000) to focussing on the covariance between splashiness and stock returns objet dart Boyd, Jagan and Hu (2001) considered the linkages between policy and stock prices. Their analysis did not focus directly on monetary policy rather it focused on markets response to employment intelligence (Bernanke and Kuttner, 2005).In their own research paper, Ehrm ann and Fratzscher (2004) find that SP ergocalciferol shows a strong effect of monetary policy on uprightness returns, that the effect of monetary policy is stronger in an environment of accessiond market uncertainty, that that negative surprises ( i.e monetary policy has tightened less and loosened more than expected) has big effects on the stock market than collateral surprises, that small firms are react more to policy shocks than large firms, that firms with low cash flows are affected more by US monetary shocks and that firms with poor ratings are more wedded to monetary policy shocks than those with good ratings. They find that firms react more strongly when no change had been expected, when there is a directional change in the monetary policy stance and during periods of high market uncertainty.There has excessively been cross-sectional dimensions of the effect of monetary policy on the stock markets in literature though few. Hayo and Uhlenbruck (2000), Dedola and Lippi (2000), Peersman and Smets ( 2002), Ganley and Salmon (1997) etc are some economists who have examine this and overall, their findings show that the stock prices of firms in cyclical industries, capital-intensive industries and industries that are relatively receptive to trade are affected more strongly by monetary policy shocks (Ehrmann and Fratzscher, 2004).According to Bernanke and Kuttner (2005), changes in monetary policy are communicate through the stock market via changes in the values of private portfolios (wealth effect?), changes in the cost of capital and by other mechanisms. In their paper, they analyzed the stock markets response to policy actions both in the substance and at the level of industrys portfolios and they as well tried to understand the reasons for the stock markets response. Their findings show that monetary policy is, for the most part, not directly attributable to policys effects on the real interest rate instead it seems to come either through it s effects on expected future excess returns or expected future dividends.While economists comm sole(prenominal) associate restrictive/ gabby monetary policy with higher/lower levels of economic activity, financial economists discuss various reasons why changes in the discount rate affect stock returns. (Durham, 2000) Changes in the discount rate affect the expectations of corporate profitability ( Waud, 1970) and decided policy rate changes influence forecasts of market determined interest rates and the equity cost of capital ( Durham, 2000).Modigliani (1971), suggests that a moderate in interest rates boosts stock prices and therefore financial wealth and lifetime resources, which in turn raises consumption through the welfare effect. Mishkin (1977) on the other hand suggests that lower interest rates increase stock prices and therefore decrease the likelihood of financial distress, leading to increased consumer durable expenditure as consumer liquidity concerns abate (Durham, 20 00).Tobins q is the equity market value of a firm divided by its book value. It can overly be define as the ratio of the market value of a firms existing shares to the fill-in cost of the firms physical assets. Higher stock prices reduce the yield on stocks and reduce the cost of financing investment spending through equity issuance (Bosworth, 1975). Tobins q explains on e of the mechanisms through which movements in stock prices can affect the economy the wealth channel. The other channels of monetary policy transmission include the interest rate channel and the exchange rate channel. The wealth channel has the investment effect, wealth effects and balance ragtime effects (www.oenb.at/en). Bernanke and Blinder (1992) and Kashyap, Stein and Wilcox (1993) show that a tightening of monetary policy has a very strong impact on firms that super depend on banks loans to financing their investments as banks reduce their overall fork over of credit. Deteriorating market conditions aff ect firms by also weakening their balance sheets as the present value of collateral falls with rising interest rates and that this effect can be stronger for some firms than for others (Bernanke and Gertler 1989, Kiyotaki and Moore 1997). These two arguments are based on information asymmetries as firms for which more information is publicly purchasable may find it easier to collect loans when credit conditions become tighter (Gertler and Hubbard 1988, Gertler and Gilchrist 1994).Stock returns of small firms chiefly respond more to monetary policy than those of large firms ( Thorbecke 1997, Perez-Quiros and Timmermmann 2000).Some economists (Sprinkle (1964), Homa and Jaffee (1971), hamburger and Kochin (1972)) in the early 1970,s alleged that past information on money come forth could be used to predict future stock returns. These finding where not in line with the efficient market hypothesis which states that all purchasable information should be reflected in authentic price s (Fama, 1970) meaning that expect information should not have any effect on current stock prices. Most economists believe that stock prices react differently to the pass judgment and unlooked-for effects of monetary policy ( Maskay, 2007).The Keynesian economists argue that there is a negative relationship between stock prices and money supply whereas real activity theorists argue that the relationship between the two variables is electropositive (Sellin, 2001). The Keynesian economists believe that a change in money supply or interest rates will affect stock prices only if the change in the money supply alters expectations about future monetary policy term the real activity economists argue that increase in money supply nitty-gritty that money contend is increasing in anticipation of increase in economic activity (Maskay, 2007). Another factor discussed by Sellin (2001) is the risk premium hypothesis proposed by Cornell i.e higher money supply indicates higher money necessi tate and higher money demand suggests increased risk which leads investors to demand higher risk premiums for attribute stocks devising them less attractive. The real activity and risk premium hypothesis is feature by Bernanke and Kuttner (2005) who argue that the price of a stock is a piece of the present value of future returns and the perceived risk in holding the stock.While advocates of the efficient market hypothesis hold that all on hand(predicate) information is included in the price of a stock, the opponents argue other and that stock prices can also be affected by un anticipate changes in money (Corrado and Jordan, 2005). The effect of anticipated and unanticipated changes in money supply on stock prices was analyzed by Sorensen (1982) who found out that unanticipated changes in money supply have a larger impact on the stock market than anticipated changes. Bernanke and Kuttner (2005) on the other hand analyze the impact of announced and unexpected changes in the fe deral property rate and find that the stock market reacts more to unannounced changes than to announced changes in the federal funds rate which is also in line with the efficient market hypothesis. Studies by Husain and Mahmood (1999) have contend results. They analyze the relationship between the money supply and changes (long run and short run) in stock market prices and find that changes in money supply causes changes in stock prices both in the short run and long run implying that the efficient market hypothesis does not always hold.Maskay(2007) analyzes the relationship between money supply and stock prices. He also seperates money supply into anticipated and unanticipated components and adds consumer confidence, real gross domestic product and unemployment rate as defend variables. The result from his analysis shows that there is a positive relationship between changes in the money supply and the stock prices thereby back up the real activity the theorists. The result from his analysis on the effect of anticipated and unanticipated change in the money supply on stock market prices shows that anticipated changes in money supply matters more than unanticipated changes. This supports the critics of the efficient market hypothesis.According to Cecchetti, et al. (2000), macroeconomic military operation can be improved if the central bank increases the short nominal interest rate in response to temporary bubble shocks? that raise the stock price indicator above the value implied by economic fundamentals. On the other hand, Bernanke and Gertler (2001) assumed in their research that the Central Bank cannot tell whether an increase in stock prices is driven by a bubble shock or a fundamental shock.This study will analyze both exogenous and endogenous components of the relationship between monetary policy and the stock market i.e the effect of monetary policy on the stock market and the the effect if any of the stock market on monetary policy decisions. Thi s particular analysis will be done using the federal funds rate as a representative of monetary policy. We also follow the methodology used by Maskay (2007) closely as we try to find the effect of money supply on the stock market. Although Maskay used M2 as a measure of money supply, this study will reissue money supply into M1 and M2 and analyze their relationship with the stock prices.Following from the guess and review of literature, this paper is aimed at answering the following questionsHow do movements in the stock market affect monetary policy decisions on federal funds rates?How does monetary policy affect stock market prices?Do stock market prices react differently to the M1 and M2 components of money supply?RESEARCH METHODOLOGYThe effect of stock market prices on monetary policy.In this section, I test for the relationship between monetary policy and stock prices using the Taylor regulate. The Taylor manage is a monetary policy rule that stipulates how much the central bank would or should change the nominal interest rate in response to the divergence of actual pompousness rates from quarry inflation rates and of actual gross domestic product from potential gross domestic product. The rule is written asit = r*t + ( t *t) + (yt t).. (1)Where it = target short-term nominal interest rate.r*t = assumed equilibrium real interest rate.t = the observed rate of inflation.*t = the desired rate of inflation.yt = the logarithm of real GDP.t = the potential issue.But, to analyze the behavior of monetary policy, the following infantile fixation equation is estimatedit = + Et( t+i *t+i) +Et (yt+i+ t+i)+t ..(2)WhereEt = the expected value conditional to information available at the time.A good conduct of monetary policy should have and each equal to 0.5 as suggested by John Taylor.To conduct our study, we use the following equationit = + Et( t+i *t+i) +Et (yt+i+ t+i)+k t-k + t ..(3)Because the monetary authorities target variables other than inflation and output deviations from the target (asset prices in this case) thereby making equation (2) mis-specified. A standard Taylor rule is well specified when the monetary authorities target only inflation and output deviations from the target. The addition to this variable is the lagged change in asset prices which is added in order to determine the relationship between monetary policy and stock prices.The data for the cost-of-living index (Consumer Price Index), real GDP (Gross interior(prenominal) Product) and the federal funds rate are obtained from the IMF Washington website while the data for SP 500 Index are obtained from the Federal Reserve sparing Data (FRED) of the Federal Reserve Bank of St Louis website www.federal arriere pensee.gov.The effect of monetary policy on stock market prices.In this section, we test whether movements in stock prices are sometimes dependent on monetary policy. This test is carried out by regressing the actual change in federal funds rates upon t he SP 500 index. We us the following ingenuous model for this purposeSP500 = 1 + 2*actual change in federal funs rate + 3*real GDP + 4* unemployment rate.Real GDP and Unemployment rate are added as control variables. The data for real GDP is obtained from IMF, Washington while the data for unemployment rates in obtained from www.federalreserves.gov.We add GDP because it is an important epitope of the stock prices as most industries react to changes in the economy and do well as the economy does well and vice versa i.e they are procyclical in nature. When the GDP is low, the stock prices generally tend to be low, as the companys performance would be worse than before. A direct, positive relationship is expected between stock prices and the GDP.Unemployment rate is also used as a control variable in this model because it is one of the major factors that determines the demand for stocks thereby either driving the stock prices up or down. When the unemployment rate is high, demand for stock reduces as less people can afford to demoralise them and this subsequently drives down stock prices and vice versa. The unemployment rate is also a proxy for for overall heart demand in the economy ( Maskay, 2007) and when it is low, unite demand is high. We expect an inverse relationship between the unemployment rates and stock prices.The effect of M1 and M2 components of money supply on stock prices.In this section, we test the relationship between monetary policy and stock prices from the money supply angle of monetary policy. We use the M1 and M2 components of money supply for this analysis. This is done by first testing the relationship between the theatrical role change in M1 and the stock prices and then testing the relationship between M2 and the stock market.The simple empirical model used for this test isSP500 = 1 + 2*%M1 + 3*Real GDP + 4*Unemployment rate.. (1)SP500 = 1+ 2*%M2 + *3Real GDP + 4*Unemployment rate.. (2)Unemployment rate and real GDP are also used here as control variables for the same reasons habituated above. The data on percentage change in M1 and M2 were obtained from Federal Reserve frugal Data from the website of the Federal Reserve Bank of St. Louis. We were able to get the monthly data of M1 and M2 and then got the quarterly averages to produce the quarterly data.DATA definitionIn this section, we define and describe the various data used in this study. We used quarterly data from 1990 to 2009. The variables used in this analysis includeThe Federal Funds RateThe federal funds rate is a monetary policy tool used by the Central Bank/Federal reserve of the country to regulate the economy. Economists believe it has an inverse relationship with stock prices as because when there is an upward movement in stock prices above the desirable level, the federal reserve increases (contractionary) the federal funds rate . This leads to a decrease in the amount of money demanded by individuals thereby causing a lower demand for stocks and energy down stock prices. We obtained data on the federal funds rate from the website of the federal reserve bank of Louisiana.2. The Consumer Price IndexA consumer price index (CPI) is an index that estimates the average price of consumer goods and services purchased by households. It is used in our study to calculate inflation. We do this using the eviews software (100 (cpi cpi ( -4)). We obtained the quarterly data on CPI from the website of the International Monetary fund in washington. The CPI has an inverse relationship with monetary policy actions.3. Real Gross Domestic Product (Real GDP)This can be defined as a measure which adjusts for inflation and reflects the value of all goods and services produced in a given year, expressed in base year prices. Real GDP provides a more accurate figure as it accounts for changes in the price level. The quarterly data on Real GDP is obtained from the website of the International Monetary Fund, Washington.4. SP 500It is a ca pital weighted index of the prices of 500 large-cap greenness stocks actively traded in the United States. It is believed to have an inverse relationship with monetary policy as an expansionary (interest rate reduction) monetary policy leads to an upward movement of the sp500 index. The quarterly data for the sp500 is obtained from the federal reserve bank of Louisiana.5. Unemployment RateThe unemployment rate is used as one of the control variables. It is an important indicator of the eudaimonia of an economy. The lower the unemployment rate, the higher the aggregate demand for stock thereby pushing up stock prices. The quarterly data on unemployment rate is obtained from the website of the Federal Reserve Bank of Louisiana. We get the quarterly data by finding quarterly averages from the monthly data provided.6. Monetary aggregates M1 and M2M1 is a monetary aggregate and it includes the transaction deposits of banks and cash in circulation and all other money equivalents that a re easily convertible into cash while includes M1 plus short-term deposits in banks and 24-hour money market funds. Money supply has a positive relationship with stock prices because the higher the money supply, the higher the demand for stock which in conclusion increases stock prices. We split money supply into M1 and M2 to find out if they have the same relationship with stock prices. The quarterly data on percentage change in monetary aggregates is obtained from the website of the federal reserve bank of Louisiana. We also had to calculate the quarterly averages of the monthly data given.DATA ANALYSIS mystify 1 The Taylor ruleit = r*t + ( t *t) + (yt t)+ t open varying FED_FUNDS_RATEMethod Least Squares understand 07/05/10 Time 2019Sample(adjusted) 19911 20094include observations 76 after adjusting endpointsVariableCoefficientStd. shiftt-StatisticProb.C3.6155131.2207832.9616340.0041INFLATION0.6842640.1562124.3803480.0000OUTPUT_GAP-1.42E-069.83E-07-1.4428030.1534R-square0.24 9642Mean dependent var3.860658 set R-squared0.229085S.D. dependent var1.686064S.E. of backsliding1.480394Akaike info measuring rod3.661167 entireness squared resid159.9844Schwarz criterion3.753170 record likelihood-136.1244F-statistic12.14348Durbin-Watson stat0.181830Prob(F-statistic)0.000028The estimation results areit =3.62 + 0.68( t *t) 1.42 (yt t)The coefficient associated to inflation is positive, 0.68, but is statistically significant with a p-value of 0.00. The coefficient associated with the output chap is negative (-1.42) and statistically significant. The estimated stabilizing rate of interest (c) is positive (3.61) and statistically significant. An R-squared of 0.25 means that we are only able to explain about 25% of the variation in the interest rate.The augmented taylor rule modelit = + Et( t+i *t+i) +Et (yt+i+ t+i)+1 t-1 + t one lag open Variable FED_FUNDS_RATEMethod Least SquaresDate 07/05/10 Time 2130Sample(adjusted) 19913 20094Included observations 74 after a djusting endpointsVariableCoefficientStd. Errort-StatisticProb.C8.2989611.2808936.4790440.0000INFLATION_F0.5489990.1811983.0298250.0034OUTPUT_GAP_F-9.10E-061.51E-06-6.0419260.0000S(-1)4.24E-057.35E-065.7757670.0000R-squared0.442430Mean dependent var3.809595Adjusted R-squared0.418534S.D. dependent var1.678852S.E. of simple regression1.280190Akaike info criterion3.384432Sum squared resid114.7220Schwarz criterion3.508976Log likelihood-121.2240F-statistic18.51494Durbin-Watson stat0.214690Prob(F-statistic)0.000000InterpretationThe estimated regression isit = 8.30 + 0.55Et( t+i *t+i) -9.10Et (yt+i+ t+i)+4.24t-kThe coefficient associated to expected inflation is positive (0.55) but is statistically significant because it has a p-value of 0f 0.003, the coefficient associated with expected output gap is negative (-9.10) and is statistically significant (p-value = 0.000). The coefficient associated with the change in asset prices (lagged by 1 for better estimation) which is denoted by S (-1) is negative and it is statistically significant therefore we reject the null hypothesis. The measure of goodness of fit (R-square) is 0.44 meaning that we are able to explain about 44% of the variability in the interest rateOur model lucidly overestimates the actual interest rate and the residuals do not seem to be independently and identically distributed. We therefore conduct some tests which include1. The Jacque-Bera test This is a statistic that measures the leaving of the skewness and kurtosis of the series with those from a normal distribution.By simply expression at the histogram, we can see that the distribution is roughly normal and the jarque-bera statistic of 0.58 shows that it is not statistically significant and we should accept the null hypothesis.The white test This is used to test whether the errors are heteroskedastic or not. In the presence of heteroskedasticity, OLS estimates are consistent but efficient.White Heteroskedasticity TestF-statistic3.846209Probabil ity0.000621Obs*R-squared25.97528Probability0.002062Test equationDependent Variable RESID2Method Least SquaresDate 07/06/10 Time 0041Sample 19913 20094Included observations 74VariableCoefficientStd. Errort-StatisticProb.C-35.2896124.46199-1.4426300.1540INFLATION_F-5.4196573.008210-1.8016220.0763INFLATION_F20.3072310.2002861.5339610.1300INFLATION_F*OUTPUT_GAP_F5.95E-062.83E-062.1055860.0392INFLATION_F*S(-1)-2.78E-051.73E-05-1.6033610.1138OUTPUT_GAP_F9.90E-055.34E-051.8525580.0686OUTPUT_GAP_F2-6.19E-112.74E-11-2.2572880.0274OUTPUT_GAP_F*S(-1)3.35E-101.43E-102.3372900.0226S(-1)-0.0003090.000140-2.2052820.0310S(-1)2-7.97E-115.33E-10-0.1496790.8815R-squared0.351017Mean dependent var1.550298Adjusted R-squared0.259754S.D. dependent var1.968439S.E. of regression1.693596Akaike info criterion4.016674Sum squared resid183.5692Schwarz criterion4.328034Log likelihood-138.6169F-statistic3.846209Durbin-Watson stat0.580160Prob(F-statistic)0.000621According to the two test statistics involved in the regression result, we can say that the distribution is statistically significant so we can reject null hypothesis.The Durbin-Watson test This is used to test for serial correlation. Autocorrelated residuals means that OLS is no longer best, linear, unbiased estimators and that the standard errors computed using the OLS formula are not correct. The Durbin-Watson statistic of 0.214690 shows that there is positive serial correlation as DW feigning 2SP500 = 1 + 2 federal funds rate + 3real GDP + 4unemployment rate.The aim of this model is to determine if the federal funds rate has any impact on the stock market. Real GDP and unemployment rate are used as control variables for reasons given in the research methodology.Dependent Variable SP500Method Least SquaresDate 07/06/10 Time 0138Sample 19901 20094Included observations 80VariableCoefficientStd. Errort-StatisticProb.C-115.7008222.2313-0.5206320.6041FED_FUNDS_RATE0.99030112.964360.0763860.9393REAL_GDP010.1595380.01032715.449160.0000UNE MPLOYMENT_RATE-119.567417.42177-6.8631010.0000R-squared0.872734Mean dependent var924.0339Adjusted R-squared0.867710S.D. dependent var378.2205S.E. of regression137.5651Akaike info criterion12.73478Sum squared resid1438237.Schwarz criterion12.85388Log likelihood-505.3912F-statistic173.7244Durbin-Watson stat0.350064Prob(F-statistic)0.000000InterpretationThe estimated regression issp500 =-115.78 + 0.99*actual change in federal funds rate + 0.16*real GDP 119.57* unemployment rate.The coefficient associated with the federal funds rate is negative and is not statistically significant. The coefficient associated with the real GDP is positive and is statistically significant while the coefficient associate