Análisis de Encuestas aplicadas para determinar el nivel de servicio ofertado por la

In this section we will report the results from our asymmetric tests. In the following regressions we have decomposed the program balances into positive and negative changes in order to assess the impact of the policy programs on market liquidity in even greater detail.

First we report the results of model (6.8+_{), which is otherwise identical to}

model (6.8), but now accounts for the positive changes in the Federal Re- serve’s program balances. Correspondingly, we then proceed to assessing the relationship between the negative changes in the Federal Reserve’s program balances and market liquidity from the results of model (6.8−). Please note however, that the variable of the QE2 large-scale asset purchase program is not available for the assessment of negative changes in our sample period due to the lack of observations.

Results from model (6.8+_{) suggest that positive changes in the QE1, QE2, and}

QE3 large-scale asset purchase programs are negatively associated with the market liquidity measure. These results are consistent with previous findings as reported earlier. Equally well, increases in the LFI are also associated with a negative relationship with market liquidity, and the LCM witnesses a positive association with the market liquidity measure, both of which are in line with our earlier results. Furthermore, we find that all results remain statistically insignificant as a predictor of the market liquidity measure with the exception of the QE1 large-scale asset purchase program and the liquidity to key credit markets program. The results for the positive changes in the QE1 program are statistically significant at the 0.10 level, while the LCM program is statistically significant at the 0.05 level.

On the other hand, results from model (6.8−) indicate that negative changes in the QE1 large-scale asset purchase program are negatively associated with market liquidity, while QE3 shows a positive association. These results suggest that the while tapering in the QE1 large-scale asset purchase program had a negative impact on the market liquidity measure, this effect was no longer observable in the QE3 large-scale asset purchase program. The existence of a binding relationship on the downside between the market liquidity measure and the QE1 program could be a reflection of the exceptionally fragile market conditions between November 2008 and August 2010. Similarly, the negative changes in the LFI program are also positively associated with market liquidity. But the negative changes in the LCM program are negatively associated with market liquidity. However, we find that the QE1 and QE3 large-scale asset purchase programs, as well as the LFI and LCM programs were all statistically not significant predictors of market liquidity.

Overall, our results are roughly in line with the findings of Bayoumi and Bui [2011] and Fratzscher, Lo Duca and Straub [2013] on the impact of unconven- tional monetary policies on equity markets. Although our baseline regressions show no clear evidence of an impact on market liquidity, our asymmetric tests suggest that the impact of unconventional monetary policy was statistically significant with the QE1 large-scale asset purchases and liquidity to key credit

markets programs. Positive changes in the QE1 program are negatively associ- ated with the market liquidity measure, while positive changes in the liquidity to key credit markets are associated with an increase in the market liquidity measure. Judging from these results, the LCM program seems to have been the most efficient stabilizing policy in terms of direct effect on market liquidity.

While our results indicate a statistically significant relationship between the positive changes in the QE1 and LCM programs and market liquidity mea- sure, our results are not particularly encouraging in terms of finding statistical evidence supporting the relationship between market liquidity and the other market operations by the Federal Reserve. More specifically, we were not able to confirm the positive tone associated to economic literature studying the lend- ing to financial institutions programs such as TAF [see for example McAndrews et al., 2008, Christensen et al., 2009, Wu, 2008, Sarkar and Shrader, 2010]. But this can potentially be due to other programs such as the TSLF averaging out the statistical significance of programs such as TAF. Nevertheless, these results can also be attributed to methodology and the parameters in the measure of market liquidity adopted in our study, or other specifications in our model, rather than to a more fundamental difference in interpretation.

However, the results are still potentially positive news. Because perhaps more importantly, our study suggests that the tapering of large-scale asset purchases or the reductions in the LFI and LCM programs have not been a particularly negative shock to market liquidity and risk to the systemic stability of the financial system.

Conclusions and comments

The objective of this study was to assess whether the Federal Reserve’s uncon- ventional monetary policy has had a statistically observable impact on market liquidity. We have described the background of the financial crisis, and have elaborated on the trends leading to the crisis and systemic bank runs. We have accounted for the fundamental concept of liquidity in economic literature, and have provided a summary of the models of financial contagion relating to liq- uidity. In this study, we also have explained central bank balance sheet policies during the crisis, and provided empirical evidence on the impact of unconven- tional monetary policies on market liquidity.

Our study used a liquidity proxy based on Pastor and Stambaugh’s [2003] specifications complemented with data from the CRSP database and the Fed- eral Reserve economic data program. We have used this setup to evaluate the impact of the Federal Reserve’s monetary programs on market liquidity in the NYSE, NYSE MKT, and NASDAQ stock exchanges.

Our empirical tests included baseline regressions and asymmetric impact tests, where the program balances were decomposed into positive and negative changes. In general, from baseline regressions we were able to conclude that the large- scale asset purchase programs (QE1, QE2, and QE3), lending to financial insti- tutions, and liquidity to key credit markets programs didn’t have a statistically significant impact on market liquidity.

In asymmetric tests, our results suggested that only the positive changes in the QE1 large-scale asset purchase program and the liquidity to key credit markets program had a statistically significant impact on market liquidity. Positive changes in the QE1 program were associated with contractions in the market liquidity measure, while positive changes in the liquidity to key credit markets tended to coincide with an increase in the market liquidity measure. These results suggest that the LCM program was the most efficient stabilizing policy if measured by direct impact on market liquidity. Conversely, our results did not support the notion that the tapering in either the large-scale asset purchases, lending to financial institutions, or liquidity to key credit markets programs had any statistically significant impact on the market liquidity measure.

In conclusion, these results are roughly in line with earlier studies assessing the impact of the Federal Reserve’s monetary programs on equity markets [see for example Bayoumi and Bui, 2011, Fratzscher et al., 2013]. We are confident on the asymmetric impact of the positive changes in the QE1 large-scale asset purchase program and the liquidity to key credit markets programs on market liquidity, but are less convinced with the effects of other programs. Essentially, we are not able to replicate the positive tone of results in studies assessing the impact of lending to financial institutions to the context of equity markets [see for example McAndrews et al., 2008, Christensen et al., 2009, Wu, 2008, Sarkar and Shrader, 2010]. Still, these minor differences might be attributed to the specifications of our model and concept of liquidity, and thus might change if parameters or assumptions were to be modified and retested.

Our results raise some questions about the exact channel of impact of un- conventional monetary policies and the specifications for our liquidity proxy. While the direct effects from market operations to market liquidity could be negative, Bayoumi and Bui [2011] suggest that the indirect impact via for ex- ample Treasury yields can be notable. This begs the question on how would it then be possible to make a more sound assessment of liquidity from the available data. Also, there are a few more important caveats that relate to the data that is available. For example, it is not obvious that the Federal Reserve’s monetary operations are purely exogenous. Fratzscher, Lo Duca and Straub [2013] note that the decisions by the Federal Reserve might of been to

a certain extent endogenous to market developments, suggesting that tighter market conditions during certain weeks could have influenced the Federal Re- serve to provide more liquidity. While our study has focused on the effective weekly market operations of the Federal Reserve, our paper does not take into account the announcement effect of such policies. Studies by Fratzscher, Lo Duca and Straub [2013], Ait-Sahalia, Adnritzky, Jobst, Nowak, and Tamirisa [2012] emphasize that the announcement effect is one of the most efficient pol- icy tools of the Federal Reserve. Although asset prices tend to react instantly to policy announcements, it is not evident how quickly these announcements are discounted in portfolio flows and how they might impact on market con- ditions. Also, another question is how well do financial markets foresee and anticipate the announcements and operations by the Federal Reserve. If the policy moves have been correctly anticipated by markets, it would be expected that portfolios have been already adjusted to reflect these conditions.

While it is not always evident how we should address these concerns, one po- tential solution would be in extending our study using event studies or different liquidity proxies based on more detailed microstructure transaction data such as TAQ data complemented with a more specified decomposition of the Federal Reserve’s monetary programs (perhaps on daily data). Alternatively, one could shift the focus to assessing the aggregate macrostructure of liquidity shocks in financial markets. One particularly interesting perspective could be in study- ing the exact conditions that push brokerages/dealers to increase or decrease margins and/or haircuts, and whether aggregate cash inflows/outflows into eq- uity funds or increased macroprudential regulation serve as any indicator of such activities. Any of such modifications to our study could further highlight interesting contributing factors as far as market liquidity is concerned.

Frozen. The Economist, April(18), 2015.

V. V. Acharya and L. H. Pedersen. Asset pricing with liquidity risk. Journal of Financial Economics, 77(2):375–410, 2005.

Y. Ait-Sahalia and J. Yu. High frequency market microstructure noise esti- mates and liquidity measures. Annals of Applied Statistics, 3(1):422–457, 2009.

Y. Ait-Sahalia, J. Andritzky, A. Jobst, S. Nowak, and N. Tamirisa. Market response to policy initiatives during the global financial crisis. Journal of International Economics, 87(1):162–177, 2012.

F. Allen and E. Carletti. The role of liquidity in crises. Federal Reserve Bank of Kansas City’s 2008 Jackson Hole Conference Proceedings, pages 379–412, 2008.

Y. Amihud. Illiquidity and stock returns: cross-section and time-series effects. Journal of Financial Markets, 5(1):31–56, 2002.

Y. Amihud, H. Mendelson, and L. H. Pedersen. Liquidity and asset prices. Foundations and Trends in Finance, 1(4):269–364, 2005.

T. Bayoumi and T. Bui. Unforeseen events wait lurking: estimating policy spillovers from u.s. to foreign asset prices. IMF Working Paper, 11(183), 2011.

B. S. Bernanke. The crisis and the policy response. Stamp Lecture at the London School of Economics, 2009.

B. S. Bernanke. Some reflections on the crisis and the policy response. The Rus- sel Sage Foundation and the Century Foundation Conference on Rethinking Finance, 2012.

B. S. Bernanke and M. Gertler. Monetary policy and asset price volatility. Economic Review - Federal Reserve Bank of Kansas City, pages 17–51, 1999. B. S. Bernanke and K. N. Kuttner. What explains the stock markets reaction to federal reserve policy? Federal Reserve Bank of New York Staff Reports, 174, 2003.

Financial Stability Board. Shadow banking: strengthening oversight and reg- ulation. Global Shadow Banking Monitoring Report, 2011.

K. Boudt, E. C. S. Paulus, and D. W. R. Rosenthal. Funding liquidity, market liquidity and ted spread: a two-regime model. National Bank of Belgium Working Paper Research, 244, 2013.

S. Brave and A. Butters. Diagnosing the financial system: financial conditions and financial stress. International Journal of Central Banking, 8(2):191–240, 2012.

T. Breusch and A. Pagan. A simple test for heteroscedasticity and random coefficient variation. Econometrica, 47(5):1287–1294, 1979.

M. K. Brunnermeier. Deciphering the liquidity and credit crunch 2007-2008. Journal of Economic Perspectives, 23(1):77–100, 2009.

M. K. Brunnermeier and M. Oehmke. Predatory short selling. Review of Finance, 18(6):2153–2195, 2014.

M. K. Brunnermeier and L. H. Pedersen. Market liquidity and funding liquidity. Review of Financial Studies, 22(6):2201–2238, 2009.

M. K. Brunnermeier, A. Crocket, C. Goodhart, A. D. Persaud, and H. S. Shin. The fundamental principles of financial regulation. Geneva Reports on the World Economy, 11, 2009.

G. A. Calvo and E. Mendoza. Rational contagion and the globalization of securities markets. Journal of International Economics, 51(1):79–113, 2000.

G. Cespa and T. Foucault. Illiquidity contagion and liquidity crashes. Review of Financial Studies, 27(6):1615–1660, 2014.

J. S. Chadha, L. Corrado, and J. Meaning. Reserves, liquidity and money: an assessment of balance sheet policies. BIS Papers, 66, 2012.

Q. Chen, A. Filardo, D. He, and F. Zhu. International spillovers of central bank balance sheet policies. BIS Papers, 66, 2011.

T. Chordia, R. Roll, and A. Subrahmanyam. Commonality in liquidity. Journal of Financial Economics, 56(1):3–28, 2000.

T. Chordia, R. Roll, and A. Subrahmanyam. Market liquidity and trading activity. The Journal of Finance, 56(2):501–530, 2001.

T. Chordia, S. Huh, and A. Subrahmanyam. Theory-based illiquidity and asset pricing. The Review of Financial Studies, 22(9):3629–3668, 2009.

J. H. E. Christensen and J. M. Gillan. Does quantitative easing affect market liquidity? Federal Reserve Bank of San Francisco Working Paper, 26, 2013.

J. H. E. Christensen, J. A. Lopez, and G. D. Rudebusch. Do central bank liquidity facilities affect interbank lending rates? Federal Reserve Bank of San Francisco Working Paper, 13, 2009.

J. H. Cochrane. Asset pricing: liquidity, trading, and asset prices. NBER Reporter, 5:1–12, 2004.

D. W. Diamond and P. H. Dybvig. Bank runs, deposit insurance, and liquidity. Journal of Political Economy, 91(3):401–419, 1983.

D. W. Diamond and R. G. Rajan. Liquidity shortages and banking crises. Journal of Finance, 60(2):615–647, 2005.

D. W. Diamond and R. G. Rajan. The credit crisis: conjectures about causes and remedies. American Economic Review, 99(2):606–610, 2009.

T. Doh. The efficacy of large-scale asset purchases at the zero lower bound. The Federal Reserve Bank of Kansas City Economic Review, 2:5–34, 2010.

R. Dornbusch, Y. C. Park, and S. Claessens. Contagion: understanding how it spreads. The World Bank Research Observer, 15(2):177–197, 2000.

S. DAmico and T. B. King. Flow and stock effects of large-scale treasury purchases. Federal Reserve Working Paper, 52, 2010.

L. H. Ederington and J. H. Lee. How markets process information: news releases and volatility. The Journal of Finance, 48(4):1161–1191, 1993.

M. J. Fleming and E. M. Remolona. The term structure of announcement effects. BIS Working Papers, 71, 1999.

K. Fong, C. W. Holden, and C. A. Trzcinka. What are the best liquidity proxies for global research? Working Paper, 2014.

J. S. Fontaine, R. Garcia, and S. Gungor. Funding liquidity, market liquidity and the cross-section of stock returns. Bank of Canada Working Paper, 12, 2015.

M. Fratzscher, M. Lo Duca, and R. Straub. On the international spillovers of us quantitative easing. ECB Working Paper Series, June(1557), 2013.

International Monetary Fund. Containing systemic risks and restoring financial soundness. Global Financial Stability Report, 2008.

J. Gagnon, M. Raskin, J. Remache, and B. Sack. The financial market effects of the federal reserves large-scale asset purchases. International Journal of Central Banking, 7(1):3–43, 2011.

D. Gale and T. Yorulmazer. Liquidity hoarding. Theoretical Economics, 8(2): 291–324, 2013.

N. Garleanu and L. H. Pedersen. Margin-based asset pricing and deviations from the law of one price. The Review of Financial Studies, 24(6):1980–2022, 2011.

L. Glosten and L. Harris. Estimating the components of the bid/ask spread. Journal of Financial Economics, 21(1):123–142, 1988.

G. Gorton. The subprime panic. European Financial Management, 15(1): 10–46, 2009.

G. Gorton and A. Metrick. Securitized banking and the run on repo. Journal of Financial Economics, 104(3):425–451, 2012.

R. Y. Goyenko, C. W. Holden, and C. A. Trzcinka. Do liquidity measures measure liquidity? Journal of Financial Economics, 92(2):153–181, 2009.

D. Gromb and D. Vayanos. Equilibrium and welfare in markets with financially constrained arbitrageurs. Journal of Financial Economics, 66(2-3):361–407, 2002.

J. Hasbrouck and D. J. Seppi. Common factors in prices, order flows, and liquidity. Journal of Financial Economics, 59(3):383–411, 2001.

G. Huberman and D. Halka. Systematic liquidity. Journal of Financial Re- search, 24(2):161–178, 2001.

N. Kiyotaki and J. Moore. Credit cycles. Journal of Political Economy, 105 (2):211–248, 1997.

A. Krishnamurthy. Amplification mechanisms in liquidity crises. American Economic Journal: Macroeconomics, 2(3):1–30, 2010.

M. Kutner, J. Neter, and C. Nachtsheim. Applied linear regression models. McGraw-Hill, Irwin, 4th edition edition, 2004.

K. N. Kuttner. Comments on price stability and japanese monetary policy. Monetary and Economic Studies, 22(3):37–46, 2004.

A. S. Kyle and W. Xiong. Contagion as a wealth effect. The Journal of Finance, 56(4):1401–1440, 2001.

M. Labonte. Federal reserve: unconventional monetary policy options. CRS Report for Congress, R42962, 2014.

M. Lewis. Flash boys: a Wall Street revolt. W. W. Norton Company, New York, 1st edition edition, 2014.

J. Lintner. The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. The Review of Economics and Statis- tics, 47(1):13–37, 1965.

A. W. Lo and J. W. Wang. Trading volume: definitions, data analysis, and implications of portfolio theory. Review of Financial Studies, 13(2):257–300, 2000.

F. A. Longstaff. Financial claustrophobia: asset pricing in illiquid markets. NBER Working Paper, 10411, 2004.

X. Lou and T. Shu. Why is the amihud illiquidity measure priced? Working Paper, 2014.

B. F. Madigan. Bagehot’s dictum in practice: formulating and implementing policies to combat the financial crisis. The Federal Reserve Bank of Kansas Citys 2009 Jackson Hole Symposium, 2009.

H. Markowitz. Portfolio selection. Journal of Finance, 7(1):77–91, 1952.

J. McAndrews, A. Sarkar, and Z. Wang. The effect of the term auction facility on the london interbank offered rate. Federal Reserve Bank of New York Staff Reports, 335, 2008.

F. S. Mishkin. Over the cliff: from the subprime to the global financial crisis. Journal of Economic Perspectives, 25(1):49–70, 2011.

S. Morris and H. S. Shin. Liquidity black holes. Review of Finance, 8(1):1–18, 2004.

J. Mossin. Equilibrium in a capital asset market. Econometrica, 34(4):768–783, 1966.

C. J. Neely. The federal reserve responds to crises: September 11th was not the first. Federal Reserve Bank of St. Louis Review, 86:27–42, 2004.

W. Newey and K. West. A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3):703– 708, 1987.

W. Newey and K. West. Automatic lag selection in covariance matrix estima- tion. Review of Economic Studies, 61(4):631–653, 1994.

B. J. Noeth and R. Sengupta. Is shadow banking really banking? The Regional Economist by the Federal Reserve Bank of St. Louis, 19:8–13, 2011.

Board of Governors of the Federal Reserve System. The federal reserve system: