Predominant government behavior is decomposed by frequency into sev- eral periodic components: updating cycles of infrastructure, Kuznets cy- cles, fiscal policy over business cycles, and election cycles. Little is known, however, about the theoretical impact of such cyclical behavior in public finance on output fluctuations. Based on a standard neoclassical growth model, this study intends to examine the frequency at which public in- vestment cycles are relevant to output fluctuations. We find an inverted U-shaped relationship between output volatility and length of cycle in public investment. Moreover, with a numerical setting, we show that periodic behavior in public investment at low frequencies—such as up- dating cycles of infrastructure and Kuznets cycles—can cause aggravated output resonance.
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decline in output volatility in the US. On the other hand, some researchers suggest that monetary policy is more effective than fiscal policy in reducing volatility (Romer, 1999; Clarida et al., 2000; Blanchard & Simon, 2001; Barrell & Gottschalk, 2004). Kent et al. (2005) suggest that a stricter monetary policy and less regulated market decrease output volatility. Romer (1999) also claims that fiscal policies such as income tax and unemployment compensations assist output stability. In addition, Dalsgaard et al. (2002) suggest that the shift from manufacturing to service sector could affect the reduced volatility in developed countries. However, Blanchard and Simon (2001) argue that this shift is not as critical as the goods sector. Other popular explanations for volatility include flawed economy management and degree of liberalization in a country. Hausmann and Gavin (1996) indicate that misconfiguration of macroeconomic policies such as misguided monetary policy cause volatility. Some researchers like Denizer et al. (2002) and Bekaert et al. (2006) advocate that financial openness and development decrease instability. Similarly, Almeida and Ferreria (2002) and Mobarak (2005) show the encouraging effect of democracy on stability.
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Business cycle fluctuations and long-run economic growth have been recently integrated in recent empirical studies. Most studies have drawn conclusion from both individual country and cross-country data. However, the positive relationship between output volatility and output growth is still controversial. The notion raised by Mirman (1971) posits that higher economic uncertainty raises precautionary savings and leads to higher output growth. Black (1987) states that real uncertainty positively impacts growth. In other words, there is a positive tradeoff between output growth and output uncertainty. The argument is that technology comes with expected returns associated with degree of specialization and varying level of risk or uncertainty. Therefore, investment occurs in specialized technologies only if expected returns can sufficiently compensate for the associated risk. This is the so-called ‘Black’s Hypothesis.’ This hypothesis is disproved by Bernanke (1983) and Pindyck (1991) who find that there exists a negative relationship between output volatility and output growth stemming from investment irreversibilities at firm level. Economic reasoning behind these two findings is that output volatility generates uncertainty about future demand that impedes investment and thus leads to a negative relationship. These are the notions of the effect of real (or output uncertainty) on growth that works through its impact on investment.
In this article, we provide new, novel evidence for a more recent structural break (in 2010) indicating a greater moderation of output volatility compared to the well-known break during the mid-1980s. The period of analysis runs from 1962Q2 to 2018Q3. It covers 26 OECD countries. In terms of methodology, it has mainly been used as the measures of conditional and unconditional volatility and procedures of structural break detection (Inclan–Tiao test and autoregressive conditional heteroscedasticity model). As a result, it has been found that output greatly stabilized following the structural break at 2010Q1 in the post-era of 2008/09 global financial crisis. Moreover, output stabilization is robustly evident for 24 (out of 26) OECD countries. From a political standpoint, it is implied that the Keynesian view may be influential in this moderation. Government expenditures and fiscal programs, regulations of financial markets against the sub-prime lending and limitations to trade of mortgage-backed securities might have been the main driver of stability. Rapid improvement of digitalization and techni- cal productivity may be regarded as another relevant reason that might have contrib- uted to the stabilization process.
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stabilizing, in this way, the economy. On the other hand, opening the capital account makes the country more dependent on credit, which, in turn, could make it more vulnerable. Easterly, Islam and Stiglitz (2000) show that ﬁnancial depth, measured by private credit to GDP, aﬀects output volatility in a non- monotonic way: initially it tends to decrease volatility but too much private credit ends up increasing output volatility. Also they do not ﬁnd any evidence for the stabilizing role of capital ﬂows. On the other hand, Svaleryd and Vlachos (2000) found a positive relationship between openness to trade and development of ﬁ nancial markets, measured by proxies like liquid liabilities and credit to pri- vate enterprises. So it is interesting to see how much of the eﬀect of openness on output volatility is attributed to the development of ﬁ nancial markets. To inves- tigate that, we introduced in our analysis some ﬁ nancial proxies such as black market premium, foreign debt, credit to private sector and liquid liabilities. Our results show that among these ﬁ nancial proxies only the black market premium plays a role in explaining output volatility. In all but the developed economies sample, the black market premium passed from being insigni ﬁ cant during the period 1950-1975 to being highly signiﬁcant over 1975-2000. Moreover a higher average level of black market premium seems to increase output volatility while a higher variability in the black market premium helps smoothing the volatility of output.
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In our ﬁ rst analysis we vary only the shock standard deviation while keeping cons- tant the rest of the parameters. That is, we ﬁrst simulate the model using lower limits for standard deviation of shocks, then we use upper limits, and ﬁnally we study com- binations of both limits. When we use only the lower limit of the shocks standard deviation, volatility for most of the variables falls compared to the baseline simula- tion, specially output volatility. Analogously, when we use only the upper limit of the shock’s standard deviation, volatility for most of the variables rises. Reductions in the volatility of shocks decrease the uncertainty regarding the states of nature. In fact, the economic agents can achieve smoother paths for the variables they control such as consumption and monetary balances. It follows that the economy, as a whole, exhibits smaller ﬂ uctuations. In tables 11 to 14 we summarize the statistics that we obtained.
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What have we learned about the possible causes of the Great Moderation? We showed that a decline in labor market turnover, through a decrease in hiring frictions and an endogenous decrease in wage rigidity, can lead to a decrease in output volatility. However, this e¤ect is small in our simulations, partly because the decrease in wage rigidity is relatively small (at least much smaller than in the data), but also because there is a direct e¤ect of the reduction in hiring frictions that counteracts the e¤ect of decreased wage rigidity: reduced frictions make labor more volatile, reducing the volatility of its marginal product. A further caveat against the conclusion that the Great Moderation is driven by the decline in labor market turnover is that this story only works if technology shocks are relatively important as a source of business cycle uctuations. The reason is that preference shocks act as labor supply shocks, so that wage rigidity dampens rather than ampli es uctuations in employment in response to these shocks. Finally, reduced wage rigidity can only lead to reduced output volatility if the wage is allocative, i.e. if the reduction in wage rigidity applies to the wage of newly hired workers. We documented an increase in the relative volatility of wages of new hires, see section 2.3.2, but that increase is substantially smaller than for the average wage in the economy, casting further doubt on the importance of this mechanism for the Great Moderation.
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We further demonstrate that the smooth probability of high growth regime drops to low levels between the third quarter of 1980 and third quarter of 1983 as well as in 2006 long before the recent financial crisis was imminent. These observations complement the information given in Figure 2 which plots the movements in our measure of inflation volatility. This figure demonstrates that volatility in the 70s was quite high and it falls to lower levels in mid nineties which then increases to a higher level following the turn of the century peaking in 2006-2009 period. It is generally well accepted that the high level of inflation of the 1970s and to some extent the high inflation volatility and output volatility were driven by passive monetary policies pursued by the FED. Similarly, many economists believe that after 1979 the FED pursue a monetary policy which satisfied the so called ‘Taylor principle’ and reacted to expected inflation more than proportionally. Hence, several economists including Clarida et al. (2000) and Benati and Surico (2009) argue that the great moderation was due to good policy. However, the visual information provided in Figures 1 and 2 that inflation and its volatility are increasing during the post-Volker period, a period when the Fed was pursuing active monetary policy, appears to provide support for the advocates of good-luck hypothesis. 17 However, this issue, although important and relevant, is beyond the scope of
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The regional volatility factors are intended to capture commonalities in output volatility across countries after accounting for global patterns. We restrict to a deﬁnition of groups based on geographic location of countries since it facilitates the interpretation of the regional factors, and therefore, the subsequent structural analysis. Figure 4 shows the regional volatility components along with their corresponding historical data decompositions. Chart A plots the volatility factor of the North American region, which exhibits three signiﬁcant increases. In 1984, all the economies of the region experienced a signiﬁcant boom leading to substantial magnitudes of real activity ﬂuctuations. Instead, in 1991, the opposite scenario occurred, when U.S. and Canada enter a recessionary phase. The third increase can be attributed to the so called “Tequila Crisis” originated in Mexico. Despite those speciﬁc periods, the volatility in North America has remained relatively stable over time, which is consistent with Gadea et al. (2018), who showed that since the Great Moderation, U.S. output growth has remained subdued despite the loss of the Great Recession. Also, the corresponding decomposition shows that North American volatility is almost no inﬂuence by the volatility of other regions.
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In 1979 when the Fed switched its policy from fixing interest rates to targeting monetary aggregates, it missed its inflation target. Many attributed this failure to the failure of monetarist model of quantity theory of money. Friedman (1984) then argued that failure to achieve the inflation target was not due to failure of quantity theory, but rather due to volatility of money supply. He argued that money supply volatility could affect velocity of money and disturb the quantity theory of money. The implication is that the volatility of money supply could be a determinant of velocity or the demand for money, since velocity represents the linear combination of quantity of money supplied (or demanded at equilibrium), price level, and the level of output. If monetary volatility or monetary uncertainty can affect the demand for money, why not output volatility or uncertainty. Indeed, Choi and Oh (2003) presented a theoretical model that shows, indeed, output volatility can also affect the demand for money. These two uncertainty measures are said to affect public’s
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Another notable difference between the developing and developed countries is the degree of international financial integration. Developed economies have access to world financial markets with fewer constraints and smaller costs, either because of more reliable financial systems or because of the large number of financial products available. Exten- sively discussed, the relationship between financial markets and macroeconomic volatili- ty is still ambiguous. Mendoza (1994) finds that changes in the volatility of consumption and output are negligible in response to changes of financial openness. Baxter and Cruci- ni (1995) find that financial integration increases the volatility of output while decreasing the volatility of consumption. Gavin et al. (1996) study the sources of macroeconomic volatility in developing countries over the period 1970 − 92, and find that there is a sig- nificant positive association between the volatility of capital flows and output volatility.
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widely used by the recent literature on optimal monetary policy. The optimal choice of price flexibility in our model is based on an approximation of individual utility functions derived directly from the microfoundations of the model. This contrasts with the Romer and Devereux and Yetman approach, which is based on largely ad hoc macro models and an ad hoc approximation of the profit function. Note also that the main issue examined by Romer (199) and Devereux and Yetman (2002) is the impact of a non-zero (but constant) rate of inflation on the equilibrium degree of price flexibility. They do not consider the impact of inflation volatility or output volatility on the equilibrium degree of price flexi- bility, nor do they analyse welfare maximising monetary policy in the face of stochastic shocks.
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However, for the impulse response analysis of cocoa output, only the volatility of exchange rate shows a significant impact. This finding reflects the view of Nwachuku et al. (2010) who revealed that world export volume, exchange rates and cocoa output were determinants of cocoa export in Nigeria. Overall, the results from this study concur with Essien et al. (n.d) and Adeyeye (2012) that the exchange rate and prices are very crucial to the export growth of cocoa in Nigeria. This is because the price of cocoa is still exogenously determined from the world market, hence, both forces of demand and supply greatly impact on cocoa output growth. The exchange rate has impacted positively on cocoa export in Nigeria; hence, as a policy recommendation, there should be a free market determination of exchange rate for export of cocoa in Nigeria. The repeated intervention by the International Monetary Fund (IMF) 2004 should be discouraged, as it will only increase poverty and reduce output in the country. A weaker exchange rate of the naira will lead to an increase in prices domestically, which can propagate to other sectors of the economy (especially on agricultural products). On the other hand, a stronger exchange rate of naira will reduce prices domestically, and later stabilize the exchange rates and increase cocoa output growth. Finally, as a policy guide, it is recommended that the forces of demand and supply should be allowed to fully determine the value of exchange rates in Nigeria.
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Consider for instance a productivity drop in the country of destination of foreign investments. This deteriorates the prospective revenues from overseas investments, thereby discouraging entry of new firms. The fall in produc- tivity, however, may well reduce the entry costs faced by multinational firms, for instance because of a strong depreciation of the foreign currency. The effect on entry is clearly reversed. In a similar vein, Russ, 2007 shows that an increase in interest rate volatility may in principle attract or deter foreign investments depending on whether it originates in the host or in the source country. Empirical research has recently addressed the question, finding encouraging results in favour of a role of macroeconomic uncertainty in shaping entry decisions by multinational firms. 4
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Volatility is an important variable in the financial market. We propose a model-free implied vola- tility method to measure the volatility and test the volatility risk premium. The model-free im- plied volatility does not depend on the option pricing model, and extracts information from all the option contracts. We provide empirical evidence from the S & P 500 index option that model-free implied volatility is more accurate to forecast the future volatility and the volatility risk premium does not exist.
EGARCH and GJR GARCH models are best known as asymmetric models. These models are based on the fact that negative shocks bring more fluctuation (decline in value) than the positive events (increase in value) in indices or in individual securities. It’s because the investors are more affected sentimentally due to the arrival of bad news to the market than that of the good news. Volatility calculation under ANN models since takes care of both sentimental factors as well as outliers. It is to be considered as a better model than the asymmetric models. Volatility obtained under ANN model is less than that of EGARCH and GJR GARCH models in most of the years as has been seen in Table 2 for Sensex.
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It is clear from Figure 1 that South Africa experienced low inflation volatility periods in the late 1980s with the magnitude of the decrease in inflation volatility accelerating during the inflation targeting (IT) period. This finding is supported by a number of studies ( see Bonga-Bonga and Lebese, 2016). However, high inflation volatility is the most prevailing regime over the pre-IT periods. Particularly, episodes from 1969 to 1987 appear to be the most unstable with the highest inflation volatility recorded in the second half of 1970s. This period coincides with the 1976 Soweto student uprising which had an adverse effect on expectations and economic performance. Following the increasing political pressure and the decrease in gold price, the resulting increase in interest rate translated into increasing inflation volatility observed between 1984 and 1986 in South Africa. The trend slowed down in 1990s with the advent of democracy, however, the contagion effect from the Asian crisis in 1996-1997 led to the depreciation of the rand with significant rise in interest rates (Simo-Kengne et al., 2013); and hence the resurgence of high inflation volatility towards the end of 1990s. On the other hand, with the introduction of IT policy in 2000, inflation volatility has been relatively low, except during the Latin America currency crisis in 2002 and the 2007-2008 global financial crisis, well identified in Figure 1.
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KY H 2 h Y ^ H + ^ Y H 2 io + O 3 (15) where ~ is the deviation of the level of welfare from the non-stochastic equilibrium. Hence- forth, a hat over a variable indicates a log deviation from the non-stochastic steady state and a bar indicates the value in the non-stochastic steady state. Notice that welfare expres- sion (15) includes the …rst moments of consumption and output and the second moments of output components. Welfare is increasing in the expected level of consumption and decreasing in the expected level and variance of the components of output. Second-order accurate solutions for variances can be obtained from …rst-order accurate solutions for the relationships between endogenous variables and the shock variable. The analysis of volatil- ity therefore involves working with a log-linearised (i.e. …rst-order approximated) version of the model. But a full second-order expression for welfare requires second-order accurate
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However, Cecchetti et al. (2000) raise several objections to Bernanke and Gertler's (1999) conclusion. Cecchetti et al. (2000) believe that one of the final goals of monetary policy is to maintain a stable financial system. Large fluctuations in the stock market can cause adverse shock to the real economy. Therefore, central banks should not only concentrate on inflation and real economic growth, but also set a goal to react to the stock market volatility. In addition, Gilchrist and Saito (2006) employs a general equilibrium model on the basis of the Real Business Cycle theory and shows that it is necessary for monetary policy to consider stock market volatility. However, leverage has no impact on asymmetric volatility at the daily frequency and, moreover, we observe asymmetric volatility for stocks with no leverage. Also, expected returns may vary with the business cycle, that is, at a lower than daily frequency. Trading activity of contrarian and herding investors has a robust effect on the relationship between daily volatility and lagged return. Consistent with the predictions of the rational expectation models, the non-informational liquidity-driven (herding) trades increase volatility following stock price declines, and the informed (contrarian) trades reduce volatility following stock price increases. The results are robust to different measures of volatility and trading activity.
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French et al. (1987) were also concerned with statistical approaches to investigate the relation between expected stock returns and volatility. They used daily returns to compute estimates of monthly volatility and reported that unexpected stock market returns are negatively related to the unexpected changes in volatility. It has been recognized for quite some time that uncertainty of speculative prices, as measured by the variances and covariances, are changing through time. One of the most prominent tools that has emerged for characterizing such changing variance is the ARCH model of Engle (1982). The importance of this model is that it enables one to quantify the variations in volatility across days of the week. As stated above, this is of interest because it is important to know if the higher return on a particular weekday is just a reward for higher risk on that day.
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