Bid-Ask Spreads In The Retail Currency Exchange Offices

Individual Project

And Their Relationship

With Interbank Forex Market

Introduction

• The goal of the project is to analyze the USD/RUB bid-ask spreads on the retail exchange market in Moscow and its relationship with MICEX results

• I found that retail spreads significantly correlate with MICEX trading results. However, this correlation is complex and indirect.

• I found some evidence that two-factor model proposed by Kyle-Obizhaeva study (2014) are relevant to the retail exchange market and displays some prediction power

Introduction

• As the most of retail factors are usually unobservable, using common market data could be valuable for studies.

• Even assuming additional retail transaction costs (Yuanchen Chang 1996) I believe, the two-factor model could predict retail market spreads to a considerable degree.

Data• I collected a panel data of around 800 Moscow retail currency exchange offices in period

of January,14 2015 to May,15 2015 by on-line quotation resource quote.rbc.ru.• I built panel data of 10-minute snapshots of a citywide retail currency exchange market.• For each snapshot I calculated geometric mean for the smallest 10 bids (2,5% percentile)

as a sample for of current market risk (Bollerslev, 1994)

Methods

I used different frameworks to explore each of the following:• correlation among MICEX trade size, volatility

and retail spreads• possible seasonality in retail spreads• hysteresis of retail spreads

Methods I examined four different functional forms: linear, squared, square rooted

and logarithmic:

I tested seasonality in three different ways:1. Estimation of the distribution of extreme values 2. the exploration of possible cyclic component in time-series3. I assessed the distribution of standard deviations

I estimated both equations forms to test the Kyle-Obizhaeva findings on the dependence among volume, volatility and spreads on the market.First, “the short-form”:

(1)The second, “the long form”:

(2)Where,Retail spread bp — retail as a base points of weighted average exchange rate Ϭ — sigma — standard deviation of the rate for the day/last hourTrade volume — (rate · USD trading volume) or volume in rubles of trade for the day/10-minutes

Results 1/3The logarithmic form is the best estimation for the retail spread elasticity over MICEX’s volatility

Results 2/31. interday hysteresis - NO,

intraday - YES.

Intraday seasonality:

Results 3/31. Kyle-Obizhaeva factor estimations:

Positive Spread-Volume correlation:

DAILY STATISTICS INTRADAY STAT.

Conclusions

• The retail market is more elastic for low volatility while higher levels of price deviations made spreads less elastic

• There is no hysteresis of retail spreads between trading days, while intraday statistics shows 30-40 minute trade volume lagging in spreads

• The retail dealers tend to perceive morning operations with higher risk of potential losses and keep their spread wide until 11:00. Just after 11:00, spreads abruptly fall and gradually declining to the day’s minimums about 13:00-15:30.

Conclusions• My study supports the Kyle-Obizhaeva findings and show significant

evidence of strong correlation between retail spreads and interbank trade volumes and volatility. I found that such interconnection persists even between different markets of the same asset.

• I revealed positive correlation of retail spreads with volatility on MICEX.• My estimations disclosed positive correlation within average retail spread

and MICEX trading volume:– increase in trade volume often leads to significant shift in exchange rate. Giving

the positive correlation between volumes and MICEX returns. Retail dealers apprehend higher risk of exchange rate shift and boost their spreads, because of one-hour rate hold-up period.

– Another explanation: trade volume on interbank currency exchange mirrored in retail trade volumes on a limited basis due to cash/non-cash transactions costs. Thus, retail exchange offices may not actively hedge their currency positions on MICEX

Modeling• I tried to apply revealed estimation coefficients of Kyle-

Obizhaeva equation to predict spreads in the last week:

Corr. coefficient:

~0,36

R2=0,32

Thank you!

Constantine KurbatovMif’2015

Acknowledgments:Ruben Enikolopov who supervised me in this study

Anna Obizhaeva who helped me find the right method to explore data

Gennady Piftankin who gave me volatility data for MICEX