Yingzi Zhu

An E-ARCH Model for the Term Structure of Implied Volatility of FX Options

We construct a statistical model for term-structure of implied volatilities of currency options based on daily historical data for 13 currency pairs in a 19-month period. We examine the joint evolution of 1 month, 2 month, 3 month, 6 month and 1 year 50-delta options in all the currency pairs. We show that there exist three uncorrelated state variables (principal components) which account for the parallel movement, slope oscillation, and curvature of the term structure and which explain, on average, the movements of the term-structure of volatility to more than 95% in all cases. We test and construct an exponential ARCH, or E-ARCH, model for each state variable. One of the applications of this model is to produce confidence bands for the term- structure of volatility.

Macroeconomic Volatilities and Long-Run Risks of Asset Prices

In this paper, motivated by existing and growing evidence on multiple macroeconomic volatilities, we extend the long-run risks model by allowing both a long-and a short-run volatility components in the evolution of economic fundamentals. With this extension, the new model not only is consistent with the volatility literature that the stock market is driven by two, rather than one, volatility factors, but also provides significant improvements in fitting various patterns, such as the size of market risk premium, the level of interest rate, degree of dividend yield predictability, and the term structure of variance risk premiums, of both the equity and option data. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2014.1962. This paper was accepted by Jerome Detemple, finance.

A Trend Factor: Any Economic Gains from Using Information over Investment Horizons?

In this paper, we provide a trend factor that captures simultaneously all three stock price trends: the short-, intermediate-, and long-term, by exploiting information in moving average prices of various time lengths whose predictive power is justified by a proposed general equilibrium model. It outperforms substantially the well-known short-term reversal, momentum, and long-term reversal factors, which are based on the three price trends separately, by more than doubling their Sharpe ratios. During the recent financial crisis, the trend factor earns 0.75% per month, while the market loses −2 . 03% per month, the shortterm reversal factor loses −0 . 82% , the momentum factor loses −3 . 88% , and the long-term reversal factor barely gains 0.03%. The performance of the trend factor is robust to alternative formations and to a variety of control variables. From an asset pricing perspective, it also performs well in explaining cross-section stock returns.

Volatility Trading: What is the Role of the Long-Run Volatility Component?

We study an investor’s asset allocation problem with a recursive utility and with tradable volatility that follows a 2-factor stochastic volatility model. Consistent with previous findings under the additive utility, we show that the investor can benefit substantially from volatility trading due to hedging demand. Unlike existing studies, we find that the impact of elasticity of intertemporal substitution (EIS) on investment decisions is of 1st-order importance. Moreover, the investor can incur significant economic losses due to model and/or parameter misspecifications where the EIS better captures the investor’s attitude toward risk than the risk aversion parameter.

Asset allocation: can technical analysis add value?

In this paper, we propose a simple approach to for exploiting optimally the information provided by technical analysis. Our optimal asset allocation strategy is easy to apply in practice and is quite robust to model misspecifications. Empirically, we apply the strategy to the US stock market from January 1926 to March 2011. In addition, we also examine strategy’s performances during the recent financial crisis as well as over all the bear markets of the past 85 years. We find that the proposed strategy outperforms the usual fixed asset allocation strategy substantially, and does extremely well during the recent financial crisis.

Bitcoin: Learning, Predictability and Profitability via Technical Analysis

We document that 1to 20-week moving averages (MAs) of daily prices predict Bitcoin returns inand out-of-sample. Trading strategies based on MAs generate substantial alpha, utility and Sharpe ratios gains, and significantly reduce the severity of drawdowns relative to a buy-and-hold position in Bitcoin. We explain these facts with a novel equilibrium model that demonstrates, with uncertainty about growth in fundamentals, rational learning by investors with different priors yields predictability of returns by MAs. We further validate our model by showing the MA strategies are profitable for tech stocks during the dotcom era when fundamentals were hard to interpret. JEL classification: G11, G12, G14