New Economics Papers
on Financial Markets
Issue of 2010‒01‒16
twelve papers chosen by

  1. Firm volatility and banks: evidence from U.S. banking deregulation By Ricardo Correa; Gustavo A. Suarez
  2. Riding Bubbles By Günster, N.K.; Kole, H.J.W.G.; Jacobsen, B.
  3. Credit models and the crisis, or how I learned to stop worrying and love the CDOs By Damiano Brigo; Andrea Pallavicini; Roberto Torresetti
  4. Arbitrage Pricing Theory: Evidence from an Emerging Stock Market By Tho Dinh NGUYEN
  5. Estimating Demand for IMF Financing by Low-Income Countries in Response to Shocks By Yasemin Bal-Gunduz
  6. Modelling and forecasting volatility of East Asian Newly Industrialized Countries and Japan stock markets with non-linear models By Guidi, Francesco
  7. Structural Changes in India's Stock Markets' Efficiency By Sasidharan, Anand
  8. Bubble Diagnosis and Prediction of the 2005-2007 and 2008-2009 Chinese stock market bubbles By D. Sornette; Zhi-Qiang Jiang; Wei-Xing Zhou; Ryan Woodard; Ken Bastiaensen; Peter Cauwels
  9. Universal Behavior of Extreme Price Movements in Stock Markets By Miguel A. Fuentes; Austin Gerig; Javier Vicente
  10. Probabilities of Positive Returns and Values of Call Options By Guanghui Huang; Jianping Wan
  11. Forward equations for option prices in semimartingale models By Rama Cont; Amel Bentata
  12. Leverage Causes Fat Tails and Clustered Volatility By Stefan Thurner; J. Doyne Farmer; John Geanakoplos

  1. By: Ricardo Correa; Gustavo A. Suarez
    Abstract: This paper exploits the staggered timing of state-level banking deregulation in the United States during the 1980s to study the causal effect of banking integration on the volatility of non-financial corporations. We find that firm-level employment, production, sales, and cash flows are less volatile after interstate banking deregulation, particularly for firms that have limited access to external finance. This finding suggests that bank-dependent firms exploit wider access to finance after deregulation to smooth out idiosyncratic shocks. In fact, short-term credit becomes less pro-cyclical after out-of-state bank entry is permitted. Finally, lower volatility in real-side variables after deregulation translates into lower idiosyncratic risk in stock returns.
    Date: 2009
  2. By: Günster, N.K.; Kole, H.J.W.G.; Jacobsen, B. (Erasmus Research Institute of Management (ERIM), RSM Erasmus University)
    Abstract: We empirically analyze rational investors' optimal response to asset price bubbles. We define bubbles as a sudden acceleration of price growth beyond the growth in fundamental value given by an asset pricing model. Our new bubble detection method requires only a limited time-series of historical returns. We apply our method to US industries and find strong statistical and economic support for the riding bubbles hypothesis: when an investor detects a bubble, her optimal portfolio weight increases significantly. A dynamic riding bubble strategy that uses only real-time information earns abnormal annual returns of 3% to 8%.
    Keywords: bubbles;limits to arbitrage;market efficiency;structural breaks
    Date: 2009–12–10
  3. By: Damiano Brigo; Andrea Pallavicini; Roberto Torresetti
    Abstract: We follow a long path for Credit Derivatives and Collateralized Debt Obligations (CDOs) in particular, from the introduction of the Gaussian copula model and the related implied correlations to the introduction of arbitrage-free dynamic loss models capable of calibrating all the tranches for all the maturities at the same time. En passant, we also illustrate the implied copula, a method that can consistently account for CDOs with different attachment and detachment points but not for different maturities. The discussion is abundantly supported by market examples through history. The dangers and critics we present to the use of the Gaussian copula and of implied correlation had all been published by us, among others, in 2006, showing that the quantitative community was aware of the model limitations before the crisis. We also explain why the Gaussian copula model is still used in its base correlation formulation, although under some possible extensions such as random recovery. Overall we conclude that the modeling effort in this area of the derivatives market is unfinished, partly for the lack of an operationally attractive single-name consistent dynamic loss model, and partly because of the diminished investment in this research area.
    Date: 2009–12
  4. By: Tho Dinh NGUYEN (Faculty of Banking and Finance, Foreign Trade University, Vietnam)
    Abstract: <p>This paper examines the stock price behaviour of an emerging stock market, the Stock Exchange of Thailand (SET), by applying a new equilibrium stock price theory formulated by Ross (1976). The theory postulates stock market risks and returns are determined by fundamentals under a linear relationship established on the basis of a homogeneous multi-factor model return generating process and the assumptions of perfectly competitive and frictionless markets.</p> <p>Employing the data for the period before the Asian Financial Crisis 1997-1998, between Jan 1987 and Dec 1996 under the light of the methodology proposed by Fama and McBeth (1973), the research investigates the relationship between the stock returns in the Stock Exchange of Thailand and some economic fundamentals, namely returns on the SET-Index, changes in exchange rates, industrial production growth rates, unexpected changes in inflation, changes in the current account balance, differences between domestic interest rates and international interest rates, changes in domestic interest rate.</p><p>The test's results show that, within the scope of the methodology and data employed, the Arbitrage Pricing Theory (APT) does hold in the very emerging stock market of Thailand, while the CAPM (Capital Asset Pricing Model) fails to do so. While changes in exchange rates consistently explain the stock returns, there is one chance the exchange rates and the industrial growth rates together systematically affect the stock returns. The negative risk premiums associated with these factors shows investors in the SET are risk averse and tend to hedge against risks of changes in fundamentals.</p>
    Date: 2010
  5. By: Yasemin Bal-Gunduz
    Abstract: This paper estimates factors affecting demand for Fund financing by Low-Income Countries (LICs) in response to policy and exogenous shocks. Various economic variables including reserve coverage, current account balance to GDP, real GDP growth, macroeconomic stability, and terms of trade shocks are found to be significant determinants of Fund financing. Moreover, global conditions, including changes in real oil and non-oil commodity prices and world trade, are also significant. Therefore, the demand for Fund financing by LICs is likely to be cyclical in response to common shocks with its intensity depending on the severity and persistence of adverse shocks.
    Keywords: Access to Fund general resources , Balance of payments need , Business cycles , Compensatory and Contingency Financing Facility , Cooperation with Fund , Economic growth , External shocks , Foreign direct investment , Fund approval , Fund arrangements , Low-income developing countries , Members , Poverty Reduction and Growth Facility , Stand-by arrangement requests ,
    Date: 2009–12–02
  6. By: Guidi, Francesco
    Abstract: This paper explores the forecasting performances of several non-linear models, namely GARCH, EGARCH, APARCH used with three distributions, namely the Gaussian normal, the Student-t and Generalized Error Distribution (GED). In order to evaluate the performance of the competing models we used the standard loss functions that is the Root Mean Squared Error, Mean Absolute Error, Mean Absolute Percentage Error and the Theil Inequality Coefficient. Our result show that the asymmetric GARCH family models are generally the best for forecasting NICs indices. We also find that both Root Mean Squared Error and Mean Absolute Error forecast statistic measures tend to choose models that were estimated assuming the normal distribution, while the other two remaining forecast measures privilege models with t-student and GED distribution.
    Keywords: GARCH; Volatility forecasting; forecast evaluation.
    JEL: G15 C22
    Date: 2010–01
  7. By: Sasidharan, Anand
    Abstract: This paper finds evidence that the Indian stock market has become weak-form efficient, off-late. We proceed by, first, locating structural breaks in the index using Bai-Perron's method for endogenous multiple structural changes. Four structural breaks are identified for the period 1991 to 2008 for the S&P CNX Nifty series -- December 1994, July 1999, June 2003 and January 2006. For this period the behaviour of returns approximates a Stable Paretian distribution. This would mean that the market risk will be beyond that can be predicted by measures build on the assumption of normality of returns. The property of infinite population variance of a stable paretian distribution makes variance based estimators redundant. Therefore, using non-parametric methods the paper tests the efficiency of the market across the periods of structural breaks. It is found that the markets have become weak-form efficient only since the second half of 2003, corresponding to the period of the third structural break.
    Keywords: Efficient Markets Hypothesis; Indian Stock Market; Structural Break; Bai-Perron; Paretian Distribution; Runs test;
    JEL: G14 C16 C14
    Date: 2009–06
  8. By: D. Sornette; Zhi-Qiang Jiang; Wei-Xing Zhou; Ryan Woodard; Ken Bastiaensen; Peter Cauwels
    Abstract: By combining (i) the economic theory of rational expectation bubbles, (ii) behavioral finance on imitation and herding of investors and traders and (iii) the mathematical and statistical physics of bifurcations and phase transitions, the log-periodic power law (LPPL) model has been developed as a flexible tool to detect bubbles. The LPPL model considers the faster-than-exponential (power law with finite-time singularity) increase in asset prices decorated by accelerating oscillations as the main diagnostic of bubbles. It embodies a positive feedback loop of higher return anticipations competing with negative feedback spirals of crash expectations. We use the LPPL model in one of its incarnations to analyze two bubbles and subsequent market crashes in two important indexes in the Chinese stock markets between May 2005 and July 2009. Both the Shanghai Stock Exchange Composite index (US ticker symbol SSEC) and Shenzhen Stock Exchange Component index (SZSC) exhibited such behavior in two distinct time periods: 1) from mid-2005, bursting in October 2007 and 2) from November 2008, bursting in the beginning of August 2009. We successfully predicted time windows for both crashes in advance (Sornette, 2007; Bastiaensen et al., 2009) with the same methods used to successfully predict the peak in mid-2006 of the US housing bubble (Zhou and Sornette, 2006b) and the peak in July 2008 of the global oil bubble (Sornette et al., 2009). Themore recent bubble in the Chinese indexes was detected and its end or change of regime was predicted independently by two groups with similar results, showing that the model has been well-documented and can be replicated by industrial practitioners. Here we present more detailed analysis of the individual Chinese index predictions and of the methods used to make and test them. We complement the detection of log-periodic behavior with Lomb spectral analysis of detrended residuals and (H, q)- derivative of logarithmic indexes for both bubbles. We perform unit-root tests on the residuals from the log-periodic power law model to confirm the Ornstein-Uhlenbeck property of bounded residuals, in agreement with the consistent model of ‘explosive’ financial bubbles (Lin et al., 2009).
    Keywords: stock market crash, financial bubble, Chinese markets, rational expectation bubble, log-periodic power law
    JEL: O16
    Date: 2009–10–25
  9. By: Miguel A. Fuentes; Austin Gerig; Javier Vicente
    Abstract: Many studies assume stock prices follow a random process known as geometric Brownian motion. Although approximately correct, this model fails to explain the frequent occurrence of extreme price movements, such as stock market crashes. Using a large collection of data from three different stock markets, we present evidence that a modification to the random model -- adding a slow, but significant, fluctuation to the standard deviation of the process -- accurately explains the probability of different-sized price changes, including the relative high frequency of extreme movements. Furthermore, we show that this process is similar across stocks so that their price fluctuations can be characterized by a single curve. Because the behavior of price fluctuations is rooted in the characteristics of volatility, we expect our results to bring increased interest to stochastic volatility models, and especially to those that can produce the properties of volatility reported here.
    Date: 2009–12
  10. By: Guanghui Huang; Jianping Wan
    Abstract: The true probability of a European call option to achieve positive return is investigated under the Black-Scholes model. It is found that the probability is determined by those market factors appearing in the BS formula, besides the growth rate of stock price. Our numerical investigations indicate that the biases of BS formula is correlated with the growth rate of stock price. An alternative method to price European call option is proposed, which adopts an equilibrium argument to determine option price through the probability of positive return. It is found that the BS values are on average larger than the values of proposed method for out-of-the-money options, and smaller than the values of proposed method for in-the-money options. A typical smile shape of implied volatility is also observed in our numerical investigation. These theoretical observations are similar to the empirical anomalies of BS values, which indicates that the proposed valuation method may have some merit.
    Date: 2009–12
  11. By: Rama Cont; Amel Bentata
    Abstract: We derive a forward partial integro-differential equation for prices of call options in a model where the dynamics of the underlying asset under the pricing measure is described by a -possibly discontinuous- semimartingale. This result generalizes Dupire's forward equation to a large class of non-Markovian models with jumps.
    Date: 2010–01
  12. By: Stefan Thurner (Dept. of Mathematics, University of Vienna); J. Doyne Farmer (Sante Fe Institute); John Geanakoplos (Cowles Foundation, Yale University)
    Abstract: We build a simple model of leveraged asset purchases with margin calls. Investment funds use what is perhaps the most basic financial strategy, called "value investing," i.e. systematically attempting to buy underpriced assets. When funds do not borrow, the price fluctuations of the asset are normally distributed and uncorrelated across time. All this changes when the funds are allowed to leverage, i.e. borrow from a bank, to purchase more assets than their wealth would otherwise permit. During good times competition drives investors to funds that use more leverage, because they have higher profits. As leverage increases price fluctuations become heavy tailed and display clustered volatility, similar to what is observed in real markets. Previous explanations of fat tails and clustered volatility depended on "irrational behavior," such as trend fol­lowing. Here instead this comes from the fact that leverage limits cause funds to sell into a falling market: A prudent bank makes itself locally safer by putting a limit to leverage, so when a fund exceeds its leverage limit, it must partially repay its loan by selling the asset. Unfortunately this sometimes happens to all the funds simultaneously when the price is already falling. The resulting nonlinear feedback amplifies large downward price movements. At the extreme this causes crashes, but the effect is seen at every time scale, producing a power law of price disturbances. A standard (supposedly more sophisticated) risk control policy in which individual banks base leverage limits on volatility causes leverage to rise during periods of low volatility, and to contract more quickly when volatility gets high, making these extreme fluctuations even worse.
    Keywords: Systemic risk, Clustered volatility, Fat tails, Crash, Margin calls, Leverage
    JEL: E32 E37 G12 G14
    Date: 2010–01

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