Alexandre Ziegler

State-price densities under heterogeneous beliefs, the smile effect, and implied risk aversion

Abstract It has been widely noted in the empirical literature that state-price densities implicit in financial asset prices are not log-normal. This paper shows that this phenomenon can be caused by heterogeneity in investors’ beliefs. It derives the state-price density under heterogeneous beliefs in closed form and demonstrates that heterogeneous beliefs can give rise to multimodal state-price densities. Consequences for the “smile effect” in implied option volatility and for measures of risk aversion inferred from empirical state-price densities are discussed.

How Index Futures and ETFs Affect Stock Return Correlations

We examine both theoretically and empirically whether increased trading activity in index futures and exchange traded funds (ETFs) is associated with higher equity return correlations. Our model predicts that demand shocks to ETFs and futures lead to stronger price comovement for index stocks and non-index stocks. Moreover, demand shocks to ETFs have a higher impact on stock return correlations than shocks to futures. We confirm the model predictions by studying the correlation of U.S. stocks after the inception of S&P 500 futures and ETFs. Furthermore, our empirical results suggest that the return comovement induced by index trading is excessive.

High-Frequency Trading in Limit Order Markets: Equilibrium Impact and Regulation

We investigate the impact of high-frequency trading (HFT) on market quality and investor welfare using a general limit order book model. We find that while the presence of HFT always improves market quality under symmetric information, under asymmetric information this is the case only if competition between high-frequency traders is sufficiently strong. While HFT does not negatively impact investor welfare, it reduces the welfare of slow speculators. The flexibility of the model allows investigating the effect of the main recent regulatory initiatives designed to curb HFT on market quality and investor welfare. We consider time-in-force rules, cancellation fees, transaction taxes, rebate fee structures, and speed bumps. While some of these regulations lead to improvements in a number of market quality measures, this generally does not translate into higher welfare for long-term investors. Rather, the main effect of such regulations is to generate wealth transfers from high-frequency traders to slow speculators. These regulations therefore appear inadequate to enhance investor welfare in the presence of HFTs. Of the different measures, transaction taxes are the least harmful; while they reduce welfare roughly by the amount of the tax, they do not significantly worsen market quality. The common practice by exchanges of granting rebates to limit orders is detrimental to market quality and investor welfare, causing both higher effective spreads and longer execution times.

Negotiating over Banking Secrecy: The Case of Switzerland and the European Union

Over the period 2002 to 2003,Switzerland and the European Union (EU) were engaged in negotiations regarding banking secrecy. The EUs stated goal was for Switzerland to abolish banking secrecy. Switzerland refused and offered to impose a withholding tax on interest income instead. The two parties eventually agreed on the latter solution. We examine the effect of these negotiations on the share prices of four Swiss banks: UBS, Credit Suisse Group (CSG), Julius Baer (Baer), and Vontobel. Overall, investors believe that bank profitability will not be impacted by the imposition of the withholding tax. The event-by-event response of the share prices differs across banks. Whereas the two universal banks (UBS and CSG) primarily react to the threat of sanctions on their EU-based operations, the private banks (Baer and Vontobel) react strongly to events suggesting that banking secrecy might be abolished.

Sell in May and go away: the evidence in the international equity index futures markets

There are teeth in the old saw

The Timing of Option Returns

We document empirically that the returns from shorting out-of-the-money S&P 500 put options are concentrated in the few days preceding their expiration. Back-month options generate almost no returns, and front-month options do so only towards the end of the option cycle. The concentration of the option premium at the end of the cycle reflects changes in options’ risk characteristics. Specifically, options’ convexity risk increases sharply close to maturity, making them more sensitive to jumps in the underlying price. By contrast, volatility risk plays a smaller role close to maturity. Our results imply that speculators wishing to harvest the put option premium should short front-month options only during the last days of the cycle, while investors wishing to protect against downside risk should use back-month options to reduce hedging costs.

Company Stock Reactions to the 2016 Election Shock: Trump, Taxes and Trade

Donald Trump’s surprise election shifted expectations: corporate taxes would be lower and trade policies more restrictive. Relative stock prices responded appropriately. High-tax firms and those with large deferred tax liabilities (DTLs) gained; those with significant deferred tax assets from net operating loss carryforwards (NOL DTAs) lost. Domestically focused companies fared better than internationally oriented firms. A price contribution analysis shows that easily assessed consequences (DTLs, NOL DTAs, tax rates) were priced faster than more complex issues (net DTLs, foreign exposure). In sum, the analysis demonstrates that expectations about tax rates greatly impact firm values.

Risk and Return Around the Clock

We investigate price discovery over the 24-hour trading day for equities, currencies, bonds, and commodities. Sizable price discovery occurs around the clock for most assets. For a given asset, intraday risk and return distributions are fairly similar, indicating a broadly constant risk-return-relationship during the day. Although the amount of price discovery varies significantly during the day and differs across assets, price discovery is generally efficient around the clock. Most assets do not exhibit the U-shaped intraday volatility pattern that has been documented for US equities, even if only main trading hours are considered. Intraday spikes in volatility are driven by the open or close of the market for the respective asset or other assets and by macroeconomic announcements. Both diffusion and jump risk are important drivers of intraday volatility patterns, and US macroeconomic news account for a sizable fraction of jump-driven volatility. For some -- but not all -- assets, the relationship between volume and volatility that can generally be observed during the trading day does not hold at the time of jumps, suggesting that traders anticipate large price moves at the time of scheduled announcements and market depth falls accordingly.

Costly Information, Imperfect Learning, and Information Aggregation

Underlying the emergence of the incomplete-information literature is the insight that real-world investors do not know assets’ expected returns. Modelling the information acquisition process explicitly, this literature aims at making models of dynamic portfolio choice and asset pricing more realistic and more accurate. However, all of the papers reviewed in Chap. 1, as well as the previous chapters of this study, assume — at least implicitly — that parameter estimation can be performed costlessly and therefore perfectly, and use optimal filtering theory to model the economic agents’ inference process.

The Impact of Incomplete Information on Utility, Prices, and Interest Rates

The literature overview presented in Chap. 1 demonstrates that there is a very close mathematical correspondence between a complete-information and an incomplete-information economy. Although this insight clearly specifies a methodology by which an incomplete information economy can be analyzed, Chap. 1 left the question of the actual impact of incomplete information on the economy open. This chapter is concerned with this issue and addresses the following questions: How does the quality of agents’ information influence their utility? Does better information always mean higher expected lifetime utility? How does the quality of information influence share prices and interest rates? Is it the case that better information leads to higher asset prices?