Anteprima
Vedrai una selezione di 10 pagine su 118
Market Microstructure Pag. 1 Market Microstructure Pag. 2
Anteprima di 10 pagg. su 118.
Scarica il documento per vederlo tutto.
Scarica
Market Microstructure Pag. 6
Anteprima di 10 pagg. su 118.
Scarica il documento per vederlo tutto.
Scarica
Market Microstructure Pag. 11
Anteprima di 10 pagg. su 118.
Scarica il documento per vederlo tutto.
Scarica
Market Microstructure Pag. 16
Anteprima di 10 pagg. su 118.
Scarica il documento per vederlo tutto.
Scarica
Market Microstructure Pag. 21
Anteprima di 10 pagg. su 118.
Scarica il documento per vederlo tutto.
Scarica
Market Microstructure Pag. 26
Anteprima di 10 pagg. su 118.
Scarica il documento per vederlo tutto.
Scarica
Market Microstructure Pag. 31
Anteprima di 10 pagg. su 118.
Scarica il documento per vederlo tutto.
Scarica
Market Microstructure Pag. 36
Anteprima di 10 pagg. su 118.
Scarica il documento per vederlo tutto.
Scarica
Market Microstructure Pag. 41
1 su 118
D/illustrazione/soddisfatti o rimborsati
Disdici quando
vuoi
Acquista con carta
o PayPal
Scarica i documenti
tutte le volte che vuoi
Estratto del documento

Formattazione del testo

N1 ( )100 ( )• If you use logreturns (that fit better withå=110 0.10 0.10 1.10 0.10 0.10Normal distribution), we need to adjust theCAAR d , d CAR d dCAR (i.e., the sum of the ARs) to get the100 -0.09 0.01 0.91 -0.10 0.001 2 i 1, 2return in the discretely compounded regimeN120 0.20 0.21 1.20 0.18 0.18N1 ( )( ) å=i 1Discrete Sum of Sum of=CAAR d , d CAR d dreturns returns logrets(1+r) LogreturnsProduct 1.20100110 0.10 0.10 1.10 0.10 0.101 2 i 1, 2N100 -0.09 0.01 0.91 -0.10 0.00• where N is the number of stocks in the sample120 0.20 0.21 1.20 0.18 0.18Final return --> 1-Product 0.20 Exp(Sum)-1 0.20=i 1Product 1.20Final return --> 1-Product 0.20 Exp(Sum)-1 0.20• where N is the number of stocks in the sampleThe above result applies to a sample of one event and must be extended for the usual caseMarket Microstructure – Fall 2020 180Market Microstructure – Fall 2020 180180180where a sample of many event observations is aggregated. To aggregate

Across securities and Market Microstructure Fall 2020 through time, we assume that there is not any correlation across the abnormal returns of 26 Market Microstructure - Fall 2020 different securities. This will generally be the case if there is not any clustering, that is, there is 181 not any overlap in the event windows of the included securities. The absence of any overlap Market Microstructure - Fall 2020 181 Cumulative average abnormal 26 181 return and the maintained distributional assumptions imply that the abnormal returns and the cumulative abnormal returns will be independent across securities.

For any time interval [d , d ], we estimate 1 2 We also estimate the cumulative average abnormal return CAAR cumulative average abnormal return (CAAR) Statistical significance N1 ( )( ) å Statistical significance = CAAR d , d CAR d d 1 2 i 1, 2 N = i 1

where N is the number of stocks in the sample where N is the number of stocks in the sample. The abnormal return (i.e.,

Il residuo del modello di mercato è distribuito come • Il rendimento anormale (cioè il residuo del modello di mercato) è distribuito come distribuito come Market Microstructure Fall 2020 Market Microstructure - Fall 2020 181 ( ) 181 2sN 0, 2s eN 0, e • Le statistiche di test per l'ipotesi nulla che • Le statistiche di test per l'ipotesi nulla che Significatività statistica AR = 0 è Le statistiche di test per l'ipotesi nulla che AR = 0 i, t AR = 0 è ARi, t i, t = t sAR • Il rendimento anormale (cioè il residuo del ˆ Significatività statistica AR ei, t = modello di mercato) è distribuito come t sAR ˆ ( ) • Questo è anche noto come AR standardizzato e2sN 0, e • Questo è anche noto come AR standardizzato • Le statistiche di test per l'ipotesi nulla che Questo è anche noto come AR standardizzato. AR = 0 è i, t • Per testare l'ipotesi nulla che il CAR = 0 ARi, t = Per testare l'ipotesi nulla che CAR = 0 t ˆ eMarket Microstructure – Fall 2020 182( )• This is also known as standardized d , d= i 1 2182 tCAR s- + × ˆd d 1 Market Microstructure – Fall 2020 182e2 1Market Microstructure – Fall 2020 182182 • That is, we need to adjust the volatility for the182number of days in the event windowThat is, we need to adjust the volatility for the number of days in the event window. • Critical values for the tests are 27– 2.58 for 99% two-tail confidence interval (i.e., 0.995 27on the standard normal distribution table)– 1.96 for 95% two-tail confidence interval (i.e., 0.975 Market Microstructure Fall 2020Market Microstructure Fall 2020Market Microstructure Fall 2020Limitations. Possible effects of the event under investigation might not be re>lected in Definitions of trading volumeMarket Microstructure – Fall 2020 185different from zero (+ve or –ve) because:

date is not precise

Definitions of trading volume

The announcement combines different events

  • Trading volume in absolute terms
  • effect of the event was already incorporated into the stock price prior to the beginning
  • The Number of shares traded
  • Trading volume in absolute terms
  • Abnormal trading volume
  • to the event window
  • Monetary value of the shares traded (preferable to compare different stocks)
  • Number of shares traded
  • Trading volume in absolute terms
  • Information leakage, insider trading or market anticipation
  • Number of shares traded
  • Number of trades (higher information content)
  • Monetary value of the shares traded (preferable to compare different stocks)
  • Trading volume in relative terms
  • The model to estimate the expected return is not correct (joint hypothesis problem)
  • compare different stocks)
  • Number of trades (higher information content)
  • Once you have

estimated the expected trading volume:

Turnover: shares traded divided by the number of Abnormal trading volume.

Once you have estimated the expected trading volume, you may:

  • Number of trades (higher information content)
  • Trading volume in relative terms
  • estimate the abnormal volume (AV) as the difference between expected and actual trading volume

Turnover: shares traded divided by the number of shares outstanding

Trading volume in relative terms

estimate the abnormal volume (AV) as the difference between expected and actual trading volume

Turnover: shares traded divided by the number of shares outstanding

In percentage of the overall market trading volume

Turnover: shares traded divided by the number of shares outstanding

In percentage of the overall market trading volume

estimated and actual trading volumes (monetary value of shares i / overall monetary value of shares traded in the market)

In percentage of the overall market trading volume

= -AV V E (V )

In percentage of the overall market trading volume

(monetary value of shares i / overall monetary valuei

ti, ti, t(monetary value of shares i / overall monetary value of shares traded in the market)of shares traded in the market)• where V is the actually observed volume fori,t Market Microstructure – Fall 2020 187stock i in time t and E(V ) is the expectedwhere V is the actually observed volume for stock i in time t and E(V) is the expected volume.i,t187 volume for stock i in time t

De>initions of trading volume. In absolute terms: -number of shared traded -monetary valueMarket Microstructure – Fall 2020Market Microstructure – Fall 2020 187187of the shares traded (preferable to compare different stocks) -number of trades (higher187187information content). Market Microstructure – Fall 2020 186Trading volume in relative terms: -turnover: shares traded divided by the number of sharesAbnormal mutual fund flows186outstanding -in percentage of the overall market trading volume (monetary value of sharesAbnormal mutual fund flowsi / overall monetary value of

shares traded in the market).

Abnormal mutual fund flows• We estimate the following relationship onAbnormal mutual fund Olows. We estimate the following relationship on historical data29historical data• We estimate the following relationship ona b b b b e= + + + + +i i i i iF CAT RET F AUM• We estimate the following relationship onhistorical data - - -i ,t 1 i ,t 2 i , t 1 3 i , t 1 4 i ,t 1 i ,thistorical data• We estimate the expected fund inflows asa b b b b e= + + + + +i i i i iF CAT RET F AUM- - -i ,t 1 i ,t 2 i , t 1 3 i , t 1 4 i ,t 1 i ,tWe estimate the expected fund in>lows asa b b b b e= + + + + +i i i i iF CAT RET F AUMˆ ˆ ˆ ˆa b b b b= + + + +i i i i iˆE ( F ) CAT RET F AUM- - -i ,t 1 i ,t 2 i , t 1 3 i , t 1 4 i ,t 1 i ,t• We estimate the expected fund inflows as+ +i , t 1 1 i , t 1 2 i , t 3 i , t 4 i , t• We estimate the expected fund inflows as• We estimate the abnormal fund inflows (e.g.,ˆ ˆ

ˆ ˆa b b b b= + + + +i i i i iˆE ( F ) CAT RET F AUM+ +i , t 1 1 i , t 1 2 i , t 3 i , t 4 i , tafter a specific event under investigation) asˆ ˆ ˆ ˆa b b b b= + + + +i i i i iˆE ( F ) CAT RET F AUM• We estimate the abnormal fund inflows (e.g.,+ +i , t 1 1 i , t 1 2 i , t 3 i , t 4 i , t= -AF F E ( F )after a specific event under investigation) as+ + +t 1 i , t 1 i , t 1• We estimate the abnormal fund inflows (e.g.,We estimate the abnormal fund in>lows (e.g., after a speci>ic event under investigation) as= -after a specific event under investigation) asMarket Microstructure – Fall 2020 188AF F E ( F )+ + +t 1 i , t 1 i , t 1188 = -Market Microstructure – Fall 2020 188AF F E ( F )+ + +t 1 i , t 1 i , t 1188 Market Microstructure – Fall 2020 188 30188 30 30Inferences with clusteringIn analyzing aggregated abnormal returns, we have thus far assumed that the abnormalreturns on individual securities are uncorrelated

In the cross section. This will generally be a reasonable assumption if the event windows of the included securities do not overlap in calendar time. The assumption allows us to calculate cumulative abnormal returns without concern about covariances between individual sample CARS, since they are zero.

When there is one event date in calendar time, clustering can be accommodated in two different ways. First, the abnormal returns can be aggregated into a portfolio dated using. A way to handle clustering is to analyze the abnormal returns without aggregation, using unaggregated security-by-security data. The basic approach is an application of a multivariate regression model with dummy

Dettagli
Publisher
mane15
A.A. 2020-2021
118 pagine
19 download
SSD Scienze economiche e statistiche SECS-P/08 Economia e gestione delle imprese
I contenuti di questa pagina costituiscono rielaborazioni personali del Publisher mane15 di informazioni apprese con la frequenza delle lezioni di Market microstructure e studio autonomo di eventuali libri di riferimento in preparazione dell'esame finale o della tesi. Non devono intendersi come materiale ufficiale dell'università Università Cattolica del "Sacro Cuore" o del prof Petrella Giovanni.