Change in Volatility in the Won/U.S. Dollar Daily Exchange Rate:Stochastic Volatility Model

This study estimates the changes in volatility of the won/U.S. dollar dailyexchange rates before and after the Korean currency crisis, using the stochastic volatility model with the ARMAregression error term. We find that the persistence of volatility increased after the Koreancurrency crisis.     

分项收入不平等效应与收入结构的优化

从分项收入角度考察城乡收入不平等,不仅能判断产生收入不平等的原因,还能判断什么分项收入有利于增加收入总量;不仅能量化收入不平等效应,还能根据收入不平等效应来优化收入结构。研究发现,分项收入具有性质不同、大小不等的收入不平等效应,其中工资性收入扩大收入不平等的效应最强、转移性收入次之、财产性收入最小;经营性收入是唯一具有缩小收入不平等效应的分项收入。据此,我们认为,城乡居民收入结构优化的方向应该是,稳定维持工资性收入、适当调整转移性收入、适度控制财产性收入并努力增加经营性收入。     

Determinants of stock market volatility and risk premia

Summary. We show the dynamics of diverse beliefs is the primary propagation mechanism of volatility in asset markets. Hence, we treat the characteristics of the market beliefs as a primary, primitive, explanation of market volatility. We study an economy with stock and riskless bond markets and formulate a financial equilibrium model with diverse and time varying beliefs. Agents states of belief play a key role in the market, requiring an endogenous expansion of the state space. To forecast prices agents must forecast market states of belief which are beliefs of others hence our equilibrium embodies the Keynes Beauty Contest. A market state of belief is a vector which uniquely identifies the distribution of conditional probabilities of agents. Restricting beliefs to satisfy the rationality principle of Rational Belief (see Kurz, 1994, 1997) our economy replicates well the empirical record of the (i) moments of the price/dividend ratio, risky stock return, riskless interest rate and the equity premium; (ii) Sharpe ratio and the correlation between risky returns and consumption growth; (iii) predictability of stock returns and price/dividend ratio as expressed by: (I) Variance Ratio statistic for long lags, (II) autocorrelation of these variables, and (III) mean reversion of the risky returns and the predictive power of the price/dividend ratio. Also, our model explains the presence of stochastic volatility in asset prices and returns. Two properties of beliefs drive market volatility: (i) rationalizable over confidence implying belief densities with fat tails, and (ii) rationalizable asymmetry in frequencies of bull or bear states.This research was supported by a grant of the Smith Richardson Foundation to the Stanford Institute for Economic Policy Research (SIEPR). We thank Kenneth Judd for constant advice which was crucial at several points in the development of this work. We also thank Kenneth Arrow, Min Fan, Michael Magill, Carsten Nielsen, Manuel Santos, Nicholas Yannelis, Ho-Mou Wu and Woody Brock for comments on earlier drafts. The RBE model developed in this paper and the associated programs used to compute it are available to the public on Mordecai Kurzs web page at http://www.stanford.edu/ mordecai.This revised version was published online in January 2005 with corrections to the Cover date.     

New development: Evaluating state revenue portfolio stability—a case of commercial casino taxes

State governments in the USA are searching for new ways to diversify their tax structure’s stability without raising existing taxes. Taxing commercial casinos on non-American Indian reservations has received significant attention to increase state revenue capacities. This article explains the impact of commercial casino tax revenue on state revenue stability.     

On the Predictability of the Stock Market Volatility: Does History Matter?

This study compares the performance of the ISD, the GARCH (1,1) , the historical volatility estimates and of two lagged trading volume measures for predicting the Swiss Stock Market Index's (SMI) volatility. The ISD has a superior daily informational content than the GARCH (1,1) estimate and retains unbiased but decreasing explanatory power over up to 20 days ahead horizons. Mean and spread daily volume measures play a significant correcting role when forecasting stock market volatility over daily and longer intervals respectively and clearly dominate the GARCH (1,1) forecasts. Their significance emphasises heterogeneous horizon traders' influence on the SMI volatility time series properties     

Leverage vs. feedback: Which Effect drives the oil market?

This article brings new insights on the role played by (implied) volatility on the WTI crude oil price. An increase in the volatility subsequent to an increase in the oil price (i.e. inverse leverage effect) remains the dominant effect as it might reflect the fear of oil consumers to face rising oil prices. However, this effect is amplified by an increase in the oil price subsequent to an increase in the volatility (i.e. inverse feedback effect) with a two-day delayed effect. This lead-lag relation between the oil price and its volatility is central to any type of trading strategy based on futures and options on the OVX implied volatility index. It is of interest to traders, risk- and fund-managers.