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Review of Quantitative Finance and Accounting

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01-08-2014 | Original Research

Investor sentiment and interest rate volatility smile: evidence from Eurodollar options markets

Authors: Cathy Yi-Hsuan Chen, I-Doun Kuo

Published in: Review of Quantitative Finance and Accounting | Issue 2/2014

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Abstract

This paper studies the extent to which investor sentiment affects the Eurodollar option smile and finds that there is the dynamic interplay between sentiment-driven investors and arbitrageurs. The results reveal a significant relation between investor sentiment and interest rate volatility smile. The significant relations are stronger for put options, for short-maturity options, and for periods with higher uncertainty. The results are robust when considering controlling variables, net buying pressure, different interest rate option models, model-free method, or excluding rational components from the sentiment measures. Our findings favor the limits to arbitrage hypothesis against the positive feedback hypothesis, suggesting that the sentiment effect is transitory. Change in investor sentiment explains the time-varying smile that can be explained neither by rational interest rate models nor by net buying pressure.

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We highlight that sentiment affects asset prices and smile shapes not only because of the speculative nature of the market, but also because there are greater obstacles for arbitrage that impede asset prices returning to fundamental values.

If arbitrageurs perceive OTM calls that are overpriced, alternatively they could sell these options and simultaneous buy delta ratio of Eurodollar futures, and then liquidate their position until price convergence. But under this strategy, arbitrageurs face model risk, infrequent rebalancing, and other unhedged variables (see the discussion in Green and Figleswi 1999), and required capital for margins. Hence, this arbitrage strategy entails greater risk or impediments than the strategy discussed above.

These recommendations are collected from the following sources: 1. Reading current market letters from these advisers. 2. Calling hotlines provided by advisers. 3. Contacting major brokerage houses to learn what the house analysts are recommending for different markets. 4. Reading faxes and e-mails sent from advisers. The buy/sell recommendations from each adviser are tracked during the day to verify the entry and exit of each trading position. The bullish consensus is compiled at the end of the day to reflect the open positions of the advisers.

There are many types of put call ratios and different ways to interpret them. The equity put call ratio measures the equity-based options, such as stock options, whereas the index put call ratio measures the index-based options, such as the SPX. The equity put call ratio includes the "Individual Equity put call Ratio," which measures the put call ratio of an individual stock, and the "Total Equities put call Ratio," which measures all equities in the market. The most authoritative total equities put call ratio in the United States is the CBOE Total Equities put call Ratio.

Under the one-factor HJM model, the volatility function σ (t, T) is set to be σ ₀, where t is the starting time of forward rate and T is the terminating time.

When the stock market is strongly affected by uncertainty, the need for OTM options will significantly increase since these options are an economical and convenient way to hedge interest rate risk.

For brevity, the results of the relationship between sentiment and call smile after including control variable is not reported here, though they can be provided on request.

We appreciate this suggestion from the reviewer.

Since the Baker sentiment data is monthly frequency, it will be better to calculate the smiles in the basis of monthly frequency to match their data frequency. This additional test takes care this consideration.

Recent literature shows that multifactor models outperform single-factor models (see Zeto 2002; Driessen et al. 2003; Gupta and Subrahmanyam 2005; Kuo and Wang 2009).

For the specification of volatility function, we use Models 1, 5, and 7 from Kuo and Wang (2009) for representing one-, two-, and three-factor models, respectively.

The mathematical expressions of model-free skewness and their computation procedure can be found in Bakshi et al. (2003) and Han (2008) so that they are not shown here.

Under the constraint on the daily option prices without any available information regarding bid ask price quotes, measuring the net buying pressure as Bollen and Whaley (2004) do is somewhat restrictive in this circumstance. However, our approach depicts the pattern that OTM options are traded more than ATM options during the economic downturn since OTM options are inexpensive means to hedge against the decline in the underlying asset. Either the approach of Bollen and Whaley, or ours will have similar effects. Our approach can be the reasonable compromise for considering data availability.

We choose the appropriate number of lags for the multivariate VAR estimation in each SS, RR and BS, using the Akaike information criterion (AIC). In general, this estimation results in two lags.

The associated tables are not included due to space considerations and will be provided upon request.

We eliminate the observations at the turning points from one regime to the other.

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Title: Investor sentiment and interest rate volatility smile: evidence from Eurodollar options markets
Authors: Cathy Yi-Hsuan Chen
I-Doun Kuo
Publication date: 01-08-2014
Publisher: Springer US
Published in: Review of Quantitative Finance and Accounting / Issue 2/2014
Print ISSN: 0924-865X
Electronic ISSN: 1573-7179
DOI: https://doi.org/10.1007/s11156-013-0376-6

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