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The statistical distribution of financial returns plays a key role in evaluating Value-at-Risk using parametric methods. Traditionally, when evaluating parametric Value-at-Risk, the statistical distribution of the financial returns is assumed to be normally distributed. However, though simple to implement, the Normal distribution underestimates the kurtosis and skewness of the observed financial returns. This article focuses on the evaluation of the South African equity markets in a Value-at-Risk framework. Value-at-Risk is estimated on four equity stocks listed on the Johannesburg Stock Exchange, including the FTSE/JSE TOP40 index and the S &amp, P 500 index. The statistical distribution of the financial returns is modelled using the Normal Inverse Gaussian and is compared to the financial returns modelled using the Normal, Skew t-distribution and Student t-distribution. We then estimate Value-at-Risk under the assumption that financial returns follow the Normal Inverse Gaussian, Normal, Skew t-distribution and Student t-distribution and backtesting was performed under each distribution assumption. The results of these distributions are compared and discussed.

We develop networks of international stock market indices using information and correlation based measures. We use 83 stock market indices of a diversity of countries, as well as their single day lagged values, to probe the correlation and the flow of information from one stock index to another taking into account different operating hours. Additionally, we apply the formalism of partial correlations to build the dependency network of the data, and calculate the partial Transfer Entropy to quantify the indirect influence that indices have on one another. We find that Transfer Entropy is an effective way to quantify the flow of information between indices, and that a high degree of information flow between indices lagged by one day coincides to same day correlation between them.

Research Paper Number: 225 Abstract: This paper examines a simple basis risk model based on correlated geometric Brownian motions. We apply quadratic criteria to minimize basis risk and hedge in an optimal manner. Initially, we derive the Follmer-Schweizer decomposition of a European claim. This allows pricing and hedging under the minimal martingale measure, corresponding to the local risk-minimizing strategy. Furthermore, since the mean-variance tradeoff process is deterministic in our setup, the minimal martingale- and variance-optimal martingale measures coincide. Consequently, the mean-variance optimal strategy is easily constructed. Simple closed-form pricing and hedging formulae for put and call options are derived. Due to market incompleteness, these formulae depend on the drift parameters of the processes. By making a further equilibrium assumption, we derive an approximate hedging formula, which does not require knowledge of these parameters. The hedging strategies are tested using Monte Carlo experiments, and are compared with recent results achieved using a utility maximization approach.

The 2008 credit crisis changed the manner in which derivative trades are conducted. One of these changes is the posting of collateral in a trade to mitigate the counterparty credit risk. Another is the realization that banks are not risk-free and, as a result, cannot borrow at the risk-free rate any longer. The latter led banks to introduced the controversial adjustment to derivative prices, known as a funding value adjustment (FVA), which is interlinked with the posting of collateral. In this paper, we extend the Cox, Ross and Rubinstein (CRR) discrete-time model to include collateral and FVA. We prove that this derived model is a discrete analogue of Piterbarg's partial differential equation (PDE), which describes the price of a collateralized derivative. The fact that the two models coincide is also verified by numerical implementation of the results that we obtain. Full article

In this paper we formulate the Risk Management Control problem in the interest rate area as a constrained stochastic portfolio optimization problem. The utility that we use can be any continuous function and based on the viscosity theory, the unique solution of the problem is guaranteed. The numerical approximation scheme is presented and applied using a single factor interest rate model. It is shown how the whole methodology works in practice, with the implementation of the algorithm for a specific interest rate portfolio. The recent financial crisis showed that risk management of derivatives portfolios especially in the interest rate market is crucial for the stability of the financial system. Modern Value at Risk (VAR) and Conditional Value at Risk (CVAR) techniques, although very useful and easy to understand, fail to grasp the need for on-line controlling and monitoring of derivatives portfolio. The portfolios should be designed in a way that risk and return be quantified and controlled in every possible state of the world. We hope that this methodology contributes towards this direction.

The Capital Asset Pricing Model (CAPM) has been a key theory in financial economics since the 1960s. One of its main contributions is to attempt to identify how the risk of a particular stock is related to the risk of the overall stock market using the risk measure Beta. If the relationship between an individual stock’s returns and the returns of the market exhibit heteroskedasticity, then the estimates of Beta for different quantiles of the relationship can be quite different. The behavioral ideas first proposed by Kahneman and Tversky (1979), which they called prospect theory, postulate that: (i) people exhibit “loss-aversion” in a gain frame, and (ii) people exhibit “risk-seeking” in a loss frame. If this is true, people could prefer lower Beta stocks after they have experienced a gain and higher Beta stocks after they have experienced a loss. Stocks that exhibit converging heteroskedasticity (22.2% of our sample) should be preferred by investors, and stocks that exhibit diverging heteroskedasticity (12.6% of our sample) should not be preferred. Investors may be able to benefit by choosing portfolios that are more closely aligned with their preferences.

The nature and indicators of financial stability of the state are presented. An issue of innovative development of the state is considered, its main difficulties and contradictions are defined. Trends and economic conditions for financial stability of the state in supporting innovation are substantiated. Special attention was paid to the factors to accelerate economic dynamics based on innovative mechanism, with appropriate set of direct financial, fiscal, legal, infrastructural, economic and political measures. It is proved that all the above measures are effective in their balanced unity under conditions of government providing with single framework for further innovative development.

A comparative analysis of the existing model of financial market regulation and the model put forth by the Bank of Russia within the framework of the Draft Federal Law ‘On Self-Regulating Organizations in the Field of Financial Markets’ indicates that the Bank of Russia is creating a new system of regulating the activity of self-regulating organizations operating in Russia’s financial markets, which significantly increases the powers of the state regulator. According to the Draft Law, the state regulator is to become free to unilaterally determine the scope of its own participation in the regulatory process. The unfettered discretion given to the state regulator may significantly undermine the interests of self-regulating organizations and their members, because the Draft Law fails to put forth any formal guarantees that the Bank of Russia should indeed permit such organizations to adequately participate in the process of setting standards and guidelines, or in monitoring the compliance therewith.

This paper employs weighted least squares to examine the risk-return relation by applying high-frequency data from four major stock indexes in the US market and finds some evidence in favor of a positive relation between the mean of the excess returns and expected risk. However, by using quantile regressions, we find that the risk-return relation moves from negative to positive as the returns’ quantile increases. A positive risk-return relation is valid only in the upper quantiles. The evidence also suggests that intraday skewness plays a dominant role in explaining the variations of excess returns.

We study price formation in securities markets, using the sequential trade framework of Glosten and Milgrom (1985). This paper makes one basic methodological advance over previous research on sequential securities trading: we allow traders to choose from n trade sizes in a multi-period market, where n can be arbitrarily large. We examine how trade size multiplicity affects the intertemporal dynamics of trading strategies, bid-ask spreads, and information revelation. We show that price impact, as a function of trade size, is increasing and exhibits (discrete) concavity.

Recently, there has been a considerable interest in the Bayesian approach for explaining investors' behaviorial biases by incorporating conservative and representative heuristics when making financial decisions, (see, for example, Barberis, Shleifer and Vishny (1998)). To establish a quantitative link between some important market anomalies and investors' behaviorial biases, Lam, Liu, and Wong (2010) introduced a pseudo-Bayesian approach for developing properties of stock returns, where weights induced by investors' conservative and representative heuristics are assigned to observations of the earning shocks and stock prices. In response to the recent global financial crisis, we introduce a new pseudo-Bayesian model to incorporate the impact of a financial crisis. Properties of stock returns during the financial crisis and recovery from the crisis are established. The proposed model can be applied to investigate some important market anomalies including short-term underreaction, long-term overreaction, and excess volatility during financial crisis. We also explain in some detail the linkage between these market anomalies and investors' behavioral biases during financial crisis.

Building on Veronesi (2000), we investigate the relationship between the quality of information on the state of the economy and the equity risk premium. We analyze the driving forces of the premium in a regime-switching setup where agents have Epstein-Zin preferences, finding a remarkably rich relation between the required risk premium and the quality of information available to investors. In particular, relaxing the strict relationship between investors' elasticity of intertemporal substitution (EIS) and their degree of risk aversion (RA) embedded in a power utility function enables us to demonstrate how the required equity premium is determined by their interplay. As conjectured in the existing literature, we demonstrate that investors with a high EIS will require less excess returns for holding stocks if they are provided with better information on the state of the economy. More interestingly, and not predicted in the literature, we find that this will also hold for investors with a moderate EIS if they are sufficiently risk averse.

An argument for adjusting Black Scholes implied call deltas downwards for a gamma exposure in a left skewed market is presented. It is shown that when the objective for the hedge is the conservation of capital ignoring the gamma for the delta position is expensive. The gamma adjustment factor in the static case is just a function of the risk neutral distribution. In the dynamic case one may precompute at the date of trade initiation a matrix of delta levels as a function of the underlying for the life of the trade and subsequently one just has to look up the matrix for the hedge. Also constructed are matrices for the capital reserve, the pro¯t, leverage and rate of return remaining in the trade as a function of the spot at a future date in the life of the trade. The concepts of pro¯t, capital, leverage and return are as described in Carr, Madan and Vicente Alvarez (2010). The dynamic computations constitute an application of the theory of nonlinear expectations as described in Cohen and Elliott (2010).

Current integration and co-movement among international stock markets has been boosted by increased globalization of the world economy, and profit-chasing capital surfing across borders. With a reputation as the fastest growing economy in the world, China’s stock market has continued gaining momentum during recent years and incurred growing attention from academicians, as well as practitioners. Taking into account economic and geographical considerations, the US and Hong Kong are considerably the most comparable stock markets to China. The usual vector error correction model (VECM) could overlook the long memory feature of cointegration residual series, which can in turn exert bias on the resulting inferences. To overcome its limitations, we employ a fractionally integrated VECM (FIVECM) in this paper to investigate the long-term cointegration relations binding China’s stock market to the aforementioned stock markets. In addition, by augmenting the FIVECM with multivariate GARCH model, the return transmission and volatility spillover between market return series were revealed simultaneously. Our empirical results show that China’s stock market is fractionally cointegrated with the two markets, and it appears that China’s stock market has stronger ties with its neighboring Hong Kong market than with the world superpower, the US market.

This paper gives a complete characterization of the equilibria in Shleifer and Vishny's (1997) model of "Limits of Arbitrage". We show that expected wealth (the arbitrageurs' objective function) is a possibly non-concave function of investment and that the relation between investment and prices is not necessarily continuous or single-valued or well-defined. As a result, "anything is possible": non-existence or multiplicity of equilibria may arise, and sunspots may govern the equilibrium selection in the latter case. Es wird eine vollstandige Charakterisierung moglicher Gleichgewichte im Shleifer-Vishny-Modell zu Grenzen der Arbitrage vorgenommen. Zunachst wird gezeigt, dass die Zielfunktion der Arbitrageure - ihr erwartetes Vermogen - eine nicht-konkave Funktion ihrer Investitionen ist und dass die Abbildung, die die aggregierten Investitionen und die Vermogenspreise in Beziehung zueinander setzt, moglicherweise unstetig, mehrwertig oder gar nicht wohldefiniert ist. Als Konsequenz hieraus "ist alles moglich": Nicht-Existenz eines Gleichgewichts ebenso wie Multiplizitat. Im zweiten Fall konnen Sonnenflecken die Gleichgewichtsauswahl bestimmen.

This study investigates optimal hedge ratios in all base metal markets. Using recent hedging computation techniques, we find that 1) the short-run optimal hedging ratio is increasing in hedging horizon, 2) that the long-term horizon limit to the optimal hedging ratio is not converging to one but is slightly higher for most of these markets, and 3) that hedging effectiveness is also increasing in hedging horizon. When hedging with futures in these markets, one should hedge long-term at about 6 to 8 weeks with a slightly greater than one hedge ratio. These results are of interest to many purchasing departments and other commodity hedgers.

Globalization has precipitated movement of output and employment between regions. We examine factors related to corporate financial distress across three continents. Using a multidimensional definition of financial distress we test three hypotheses to explain financial distress using historical financial data. A null hypothesis of a single global model was rejected in favor of a fully relaxed model which created individual financial distress models for each region. This result suggests that despite other indications of worldwide convergence, international differences in accounting rules, lending practices, managements skill levels, and legal requirements among others has kept corporate decline from becoming commoditized.

A new simple model of financial market is proposed, based on the sequential and inter-temporal nature of trader-trader interaction, and on a new simple trading strategy space. In this pattern-based speculation model, the traders open and close their positions explicitly. Information ecology is strikingly similar to that of the Minority Game which suggest to reinterpret the latter as a model of synchronisation of predictability exploitation. Naive and sophisticated agents are shown to give rise to very different phenomenology, 31 pages, 7 figures. Final version, even clearer and better. New figure of information ecology

We build a discrete-time non-linear model for volatility forecasting purposes. This model belongs to the class of threshold-autoregressive models, where changes in regimes are governed by past returns. The ability to capture changes in volatility regimes and using more accurate volatility measures allow outperforming other benchmark models, such as linear heterogeneous autoregressive model and GARCH specifications. Finally, we show how to derive closed-form expression for multiple-step-ahead forecasting by exploiting information about the conditional distribution of returns.

It is generally held that derivative prices do not contain useful predictive information, that is, information relating to the distribution of future financial variables under the real-world measure. This is because the market’s implicit forecast of the future becomes entangled with market risk preferences during derivative price formation. A result derived by Ross [1], however, recovers the real-world distribution of an equity index, requiring only current prices and mild restrictions on risk preferences. In addition to being of great interest to the theorist, the potential practical value of the result is considerable. This paper addresses implementation of the Ross Recovery Theorem. The theorem is formalised, extended, proved and discussed. Obstacles to application are identified and a workable implementation methodology is developed.

Certain exotic options cannot be valued using closed-form solutions or even by numerical methods assuming constant volatility. Many exotics are priced in a local volatility framework. Pricing under local volatility has become a field of extensive research in finance, and various models are proposed in order to overcome the shortcomings of the Black-Scholes model that assumes a constant volatility. The Johannesburg Stock Exchange (JSE) lists exotic options on its Can-Do platform. Most exotic options listed on the JSE’s derivative exchanges are valued by local volatility models. These models needs a local volatility surface. Dupire derived a mapping from implied volatilities to local volatilities. The JSE uses this mapping in generating the relevant local volatility surfaces and further uses Monte Carlo and Finite Difference methods when pricing exotic options. In this document we discuss various practical issues that influence the successful construction of implied and local volatility surfaces such that pricing engines can be implemented successfully. We focus on arbitrage-free conditions and the choice of calibrating functionals. We illustrate our methodologies by studying the implied and local volatility surfaces of South African equity index and foreign exchange options.

Static time series models usually assume stationarity, normality, and independence for the increments of financial rates of return. This paper investigates the empirical characteristics of financial rates of return from Latin American stock and currency markets and documents that their empirical rates of return are non-normal, non-stationary and non-ergodic, and that they exhibit long-term dependence. This paper measures the degree of long-term dependence of these financial time series by calculating their global, or homogeneous, Hurst exponents from their wavelet multiresolution analyses (MRA), i.e. from the wavelet resonance coefficients. Visualizations of these resonance coefficients and their power spectra are provided by scalograms and scalegrams, respectively. These visualizations help to identify the long-term dependence characteristics, which cannot be identified by the classical time series analysis, which is based on the stationarity and independence assumptions. Our findings are consistent with some empirical findings from financial market data in the USA, in Europe and in Asia, but extend their domain of empirical investigation.

We consider the problem of constructing a portfolio of finitely many assets whose return rates are described by a discrete joint distribution. We propose a new portfolio optimization model involving stochastic dominance constraints on the portfolio return rate. We develop optimality and duality theory for these models. We construct equivalent optimization models with utility functions. Numerical illustration is provided.

This article proposes a stylized model that reconciles several seemingly conflicting findings on financial security returns and option prices. The model is based on a pure jump Levy process, wherein the jump arrival rate obeys a power law dampened by an exponential function. The model allows for different degrees of dampening for positive and negative jumps and also for different pricing for upside and downside market risks. Calibration of the model to the S&P 500 index shows that the market charges only a moderate premium on upward index movements but the maximally allowable premium on downward index movements.

International audience; We study a monetary version of the Keen model by merging two alternative extensions, namely the addition of a dynamic price level and the introduction of speculation. We recall and study old and new equilibria, together with their local stability analysis. This includes a state of recession associated with a deflationary regime and characterized by falling employment but constant wage shares, with or without an accompanying debt crisis. We also emphasize some new qualitative behavior of the extended model, in particular its ability to produce and describe repeated financial crises as a natural pace of the economy, and its suitability to describe the relationship between economic growth and financial activities.

In practice, all option strategies are decided in advance, given the investor's belief of the stock price. In this paper, instead of deciding in advance the most appropriate hedging option strategy, an LP problem is formulated, by considering all significant Greek parameters of the Black–Scholes formula, such as delta , gamma , theta , rho and kappa . The optimal strategy to select will be simply decided by the solution of that model. The LP model is applied to Ericsson's call and puts options.

We develop a model that explains why the manager of a firm may smooth reported earnings by reducing its variability through time. Greater earnings volatility leads to a bigger informational advantage for informed investors over uninformed investors. If a sufficient number of current shareholders are uninformed and face some likelihood of trading in the future for liquidity reasons, then an increase in the volatility of reported earnings will magnify the trading losses these uninformed shareholders perceive. They will, therefore, want their firm's manager to produce as smooth a reported earnings stream as possible. Interestingly, it is a concern with long-term stock price performance rather than a preoccupation with the short-term performance that causes smoothing. Empirical implications are drawn out that link earnings smoothing to managerial compensation contracts, uncertainty about the volatility of earnings and the ownership structure.

Bharath and Shumway (2008) provide evidence that shows that it is the functional form of Merton’s (1974) distance to default (DD) model that makes it useful and important for predicting defaults. In this paper, we investigate whether the default predictability of the Merton DD model would be affected by taking investors’ ambiguity aversion into consideration. The Cox proportional hazard model is used to compare the forecasting power of Bharath and Shumway’s naive model, which retains the functional form of the Merton DD model and computes the default probability in a naive way, with our new model, which treats investors’ ambiguity aversion as additional information. We provide evidence to show that our new model performs better than Bharath and Shumway’s naive model. In addition, our empirical results show that the statistical significance of Bharath and Shumway’s naive default probability is retained in the credit default swap (CDS) spread regressions, though the sign of the coefficient is changed. However, both the sign and the statistical significance of our model are retained in the CDS spread regressions.

In this paper, we document that realized variation measures constructed from high-frequency returns reveal a large degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting errors in realized volatility are substantive. Even though returns standardized by ex post quadratic variation measures are nearly Gaussian, this unpredictability brings considerably more uncertainty to the empirically relevant ex ante distribution of returns. Explicitly modeling this volatility risk is fundamental. We propose a dually asymmetric realized volatility model, which incorporates the fact that realized volatility series are systematically more volatile in high volatility periods. Returns in this framework display time varying volatility, skewness and kurtosis. We provide a detailed account of the empirical advantages of the model using data on the S&amp, P 500 index and eight other indexes and stocks.

Chong and Ng (2008) find that the Moving Average Convergence–Divergence ( MACD ) and Relative Strength Index ( RSI ) rules can generate excess return in the London Stock Exchange. This paper revisits the performance of the two trading rules in the stock markets of five other OECD countries. It is found that the MACD (12,26,0) and RSI (21,50) rules consistently generate significant abnormal returns in the Milan Comit General and the S&P/TSX Composite Index. In addition, the RSI (14,30/70) rule is also profitable in the Dow Jones Industrials Index. The results shed some light on investors’ belief in these two technical indicators in different developed markets.

The problem of investing y(0) dollars at time 0 to duplicate a contingent claim is formulated as a dynamic optimization problem and solved by the Lagrange method. As an example, the well-known formula of Black and Scholes on option pricing is derived. If the function defining dy(t) is concave in y(t), owing to costs of trading in incomplete markets, there is economy of scale in producing many claims simultaneously, thus explaining the profitability of institutions in providing such financial services.

This paper examines the convexity bias introduced by pricing interest rate swaps off the Eurocurrency futures curve and the market's adjustment of this bias in prices over time. The convexity bias arises because of the difference between a futures contract and a forward contract on interest rates, since the payoff to the latter is non-linear in interest rates. Using daily data from 1987-1996, the differences between market swap rates and the swap rates implied from Eurocurrency futures prices are studied for the four major interest rate swap markets - $, £, DM and ¥. The evidence suggests that swaps were being priced off the futures curve (i.e. by ignoring the convexity adjustment) during the earlier years of the study, after which the market swap rates drifted below the rates implied by futures prices. The empirical analysis shows that this spread between the market and futures-implied swap rates cannot be explained by default risk differences, liquidity differences or information asymmetries between the swap and the futures markets. Using alternative term structure models (one-factor Vasicek, Cox-Ingersoll and Ross, Hull and White, Black and Karasinski, and the two-factor Heath, Jarrow and Morton), the theoretical value of the convexity bias is found to be related to the empirically observed swap-futures differential. We interpret these results as evidence of mispricing of swap contracts during the earlier years of the study, with a gradual elimination of that mispricing by incorporation of a convexity adjustment in swap pricing over time.

Psychological studies establish that people are usually overconfident and that they systematically overweight some types of information while underweighting others. How overconfidence affects a financial market depends on who in the market is overconfident and on how information is distributed. This paper examines markets in which price-taking traders, a strategic-trading insider, and risk-averse market-makers are overconfident. It also analyzes the effects of overconfidence when information is costly. In all scenarios, overconfidence increases expected trading volume and market depth while lowering the expected utility of those who are overconfident. However, its effect on volatility and price quality depend on who is overconfident. Overconfident traders can cause markets to underreact to the information of rational traders. Markets also underreact to abstract, statistical, or highly relevant information, while they overreact to salient, anecdotal, or less relevant information.

This paper demonstrates that winning a takeover bidding contest can be `bad news' and, consequently, losing can be `good news.' This is true even when all bidders act rationally in their own best interests with perfect information on their valuations. Bidders with toeholds rationally bid above their valuations and possibly suffer losses in equilibrium. This equilibrium strategy of `bidding to lose' played by partial owners leads to the `owner's curse.' The paper provides an explanation for acquirer losses without recourse to managerial `hubris' and/or agency problems. The presence of partial owners also has strong implications for the choice of selling mechanisms: firms should not be sold using a first price sealed-bid auction. The presence of large blockholders also acts as a costless defensive measure and partially substitutes for other costly defensive measures. The model gives rise to predictions on (1) the type of acquirers more likely to make losses; (2) the choice of auction procedures; (3) the effect of managerial ownership on firm value; (4) the existence of initial bid premia; and (5) the incentives of firms to engage in share repurchase, private placement and debt-for-equity swaps in the face of a takeover threat.

This paper studies the effect of clustering of liquidity trades on intraday patterns of volatility and market depth when private information is long-lived. The assumption of long-lived information allows us to distinguish between the patterns of information arrival and information use. Our results are: (i) volatility follows the same pattern as liquidity trading, (ii) there are no systematic patterns in the price impacts of orders, and (iii) the timing of information arrival is unimportant. Result (i) is the same as that obtained by Admati and Pfleiderer (1988) in a model of short-lived private information, but (ii) and (iii) are different.

This paper features an analysis of the relationship between the S&P 500 Index and the VIX using daily data obtained from both the CBOE website and SIRCA (The Securities Industry Research Centre of the Asia Pacifc). We explore the relationship between the S&P 500 daily continuously compounded return series and a similar series for the VIX in terms of a long sample drawn from the CBOE running from 1990 to mid 2011 and a set of returns from SIRCA's TRTH datasets running from March 2005 to-date. We divide this shorter sample, which captures the behaviour of the new VIX, introduced in 2003, into four roughly equivalent sub-samples which permit the exploration of the impact of the Global Financial Crisis. We apply to our data sets a series of non-parametric based tests utilising entropy based metrics. These suggest that the PDFs and CDFs of these two return distributions change shape in various subsample periods. The entropy and MI statistics suggest that the degree of uncertainty attached to these distributions changes through time and using the S&P 500 return as the dependent variable, that the amount of information obtained from the VIX also changes with time and reaches a relative maximum in the most recent period from 2011 to 2012. The entropy based non-parametric tests of the equivalence of the two distributions and their symmetry all strongly reject their respective nulls. The results suggest that parametric techniques do not adequately capture the complexities displayed in the behaviour of these series. This has practical implications for hedging utilising derivatives written on the VIX, which will be the focus of a subsequent study.

This paper proposes the Lagrange multiplier test for the null hypothesis thatthe bivariate time series has only a single common stochastic volatility factor and noidiosyncratic volatility factor. The test statistic is derived by representing the model in alinear state-space form under the assumption that the log of squared measurement error isnormally distributed. The empirical size and power of the test are examined in Monte Carloexperiments. We apply the test to the Asian stock market indices.

In this paper, we treat output as a decision variable. Moreover, we employ a general form of basis risk. Furthermore, we relax the statistical-independence assumption between the spot price and basis risk.

Both academic and applied researchers studying financial markets and other economic series have become interested in the topic of chaotic dynamics. The possibility of chaos in financial markets opens important questions for both economic theorists as well as financial market participants. This paper will clarify the empirical evidence for chaos in financial markets and macroeconomic series emphasizing what exactly is known about these time series in terms of forecastability and chaos. We also compare these two concepts from a financial market perspective contrasting the objectives of the practitioner with those of the economic researchers. Finally, we will speculate on the impact of chaos and nonlinear modelling on future economic research.

This paper considers a setting in which managers have private information about the values of their firms and can communicate it to uninformed investors through the use of two signals: capital structure and inventory accounting method. We show conditions under which a separating equilibrium with debt alone does not exist. The two-signal equilibrium involves a partitioned separation in which the highest quality firms choose FIFO and the lower quality firms choose LIFO, and all firms then distinguish themselves within these two partitions through capital structure choices. The analysis helps to explain the many observed empirical regularities about firms' capital structure choices and LIFO/FIFO choices and, in addition, produces numerous testable predictions about the relation between capital structure and inventory accounting method.

We study fluctuations in stock prices using a framework derived from the present value model augmented with a macroeconomic factor. The fundamental value is derived as the expected present discounted value of broad dividends that include, in addition to traditional cash dividends, other payouts to shareholders. A stochastic discount factor motivated by the consumption-based asset pricing model is utilized. A single macroeconomic factor, namely the output gap determines the non-fundamental component of stock prices. A resulting trivariate Vector Autoregression (TVAR) model of stock prices, broad dividends, and the output gap shows evidence of cointegration in the DJIA and S&amp, P 500 index data. Nonetheless, a sup augmented Dickey-Fuller test reveals existence of periodically collapsing bubbles in S&amp, P 500 data during the late 1990s.

We develop a simple model where exporting firms are characterized by het- erogeneous productivity and may face a liquidity constraint, which in turn is affected by exchange rate changes. This setup is used to analyze exchange rate exposure, i.e. the sensitivity of profits to exchange rate changes, and to derive testable implications that we bring to the data. The key innovation of our setup is to assume that exchange rate changes can either boost or depress liquidity: this allows us to study exposure profits under different scenarios. We find that profits of more productive firms should be more sensitive to ex- change rate fluctuations. Moreover, an increase in the cost of external funds (relative to cash flow) makes profits less sensitive to exchange rate shocks for firms whose liquidity is positively affected by an appreciation of the exchange rate. We test these predictions derived from the model using a large dataset of French exporting firms. Results confirm that exposure tends to increase with productivity but in a non linear way. Furthermore, empirical results confirm that for firm whose cash flow is negatively correlated with exchange rate movements, an increase in financial costs lowers exposure.

This paper considers the multiperiod hedging decision in a framework of mean-reverting spot prices and unbiased futures markets. The task is to determine the optimal hedging path, i.e., the sequence of positions in futures contracts with the objective of minimizing the variance of an uncertain future cash flow. The model is used to illustrate both hedging using a matchedmaturity futures contract and hedging by rolling over a series of nearby futures contracts. In each case, the paper derives the conditions under which a single period (myopic) strategy would be optimal as opposed to a dynamic multiperiod strategy. The results suggest that greater the market power of the hedging entity, closer the optimal strategy is to a myopic hedge. The paper also highlights the difference in the optimal hedging path when hedging is based on matched-maturity as opposed to nearby contracts.

The valuation of options and many other derivative instruments requires an estimation of exante or forward looking volatility. This paper adopts a Bayesian approach to estimate stock price volatility. We find evidence that overall Bayesian volatility estimates more closely approximate the implied volatility of stocks derived from traded call and put options prices compared to historical volatility estimates sourced from IVolatility.com (“IVolatility”). Our evidence suggests use of the Bayesian approach to estimate volatility can provide a more accurate measure of ex-ante stock price volatility and will be useful in the pricing of derivative securities where the implied stock price volatility cannot be observed.

We explore optimal hedge ratios and hedging effectiveness for the German electricity market. Given the increasing attention that wavelets received in the financial market, we concentrate on the investigation of the relationship, covariance/coherence evolution and hedge ratio analysis, on a time-frequency-scale approach (discrete and continuous), between electricity spot and futures. Simpler approaches are also used for comparison purposes like the näive, OLS and the dynamic multivariate GARCH model in order to account for risk reduction through hedging. Results allow us to conclude that: dynamic hedging strategies provide higher variance reductions in terms of hedging effectiveness; there is poor correlation among spot and futures, not being homogeneous across scales, which condition the effectiveness of the hedging strategy; the long-horizon hedge ratio does not converge to its long run equilibrium of one. Wavelets poor fit in variance reduction is attributed to low coherence and to statistical relationships between spot and futures electricity series. The instability found in various aspects of market comovements may imply serious limitations to the investor's ability to exploit potential benefits from hedging with futures contracts in electricity markets. Moreover, much variation in the contemporaneous relationship among spot and futures may highlight inadequacy in assuming (short-term) relationships in both markets, which might account for the difficulty in achieving profitable active trading.

We examine the use of second-order stochastic dominance as both a technique for constructing portfolios and also as a way to measure performance. As a preference-free technique second-order stochastic dominance will suit any risk-averse investor, and it does not require normally distributed returns. Using in-sample data, we construct portfolios such that their second-order stochastic dominance over a benchmark is most probable. The empirical results based on 21 years of daily data suggest that this portfolio choice technique significantly outperforms the benchmark out-of-sample. Moreover, its performance is typically better - and frequently much better - than several alternative portfolio-choice approaches using equal weights, mean-variance optimization, or minimum-variance methods.

Berle and Means’ analysis of the corporation, and in particular their view that those in control are not the owners of the corporation, raises questions about actions that corporations take to counter concerns regarding management’s influence. What mechanisms, if any, do corporations implement to balance the distribution of power in the corporation? To address this question, we analyze the board of directors’ propensity to voluntarily adopt recommended corporate governance practices that are designed to enhance their oversight capabilities. Since board independence has been advocated as a way to enhance shareholders’ ability to monitor management, we ask whether firms with an independent board of directors are more likely than firms without an independent board to adopt these practices. Using hand-collected data from Canadian firms listed on both US and Canadian stock exchanges, we find that firms with both types of boards voluntarily adopt governance practices designed to enhance their monitoring capabilities and that independent boards are no more likely to adopt these practices than their non-independent counterparts. One exception to this statement is the formation of board committees. When boards are independent, the audit and compensation committees are far more likely to be staffed exclusively with independent directors. For other voluntary governance practices, the board's propensity to adopt recommended practices is sensitive to the presence of a controlling shareholder.

We analyze risk-sensitive, incentive-compatible deposit insurance in the presence of private information and moral hazard. Without deposit-linked subsidies it is impossible to implement risk-sensitive, incentive-compatible deposit insurance pricing in a competitive, deregulated environment, except when the deposit insurer is the least risk averse agent in the economy. We establish this formally in the context of an insurance scheme in which privately informed depository institutions are offered deposit insurance premia contingent on reported capital; the result holds for alternative sorting instruments as well. This suggests a contradiction between deregulation and fairly priced, risk-sensitive deposit insurance. THIS PAPER EXPOSES A conflict between a deregulated, competitive financial services industry on the one hand and fairly priced, risk-sensitive deposit insurance on the other. We show that if depository institutions (DIs) are perfectly competitive, i.e., each makes zero profits on average, then it is impossible to implement incentive-compatible, risk-sensitive deposit insurance pricing.' Recent distress among DIs has fueled debate about reform of the existing deposit insurance system. Many believe that the prevailing risk-insensitive premium structure encourages DIs to choose excessively risky assets, and that it should be replaced by deposit insurance premia linked to the DIs' choices of risk.2 Risk-sensitive deposit insurance pricing, however, imposes greater informational demands on the deposit insurer, and runs afoul of both

A central challenge to the practice of enterprise risk measurement and management faced by diversified financial institutions (e.g., an internationally active bank or insurance company) is developing a coherent approach to aggregating different risk types. This has been motivated by rapid financial innovation, developments in supervisory standards (Basel 2) and recent financial turmoil. The main risks faced - market, credit and operational – have distinct distributional properties, and historically have been modeled in differing frameworks. We contribute to the modeling effort along four dimensions, providing tools and insights to practitioners and regulators. First, we extend the scope of the analysis for these three risks that have been the focus of the literature, analyzing proxies for liquidity and interest rate risk, which has implications for Pillar II of Basel IRB framework. Second, we utilize actual data representative of major banking institutions’ loss experience, extracted from call reports, submitted by banks to supervisory agencies. This allows us to explore the impact of business mix and inter-risk correlations on total risk. Third, we estimate alternative copula models, an established framework for capturing realistic distributional features of different risk types (e.g., non-normality) and cohesively combining such, on the same data-set. We then compare our models to several conventional approaches to computing risk amongst practitioners. Finally, we apply recently developed goodness-of-fit (GOF) tests to the various copula models, which has been a largely neglected in theory and practice. Overall, our results constitute a sensitivity analysis that is evidence for practitioners to consider implementing a simple non-parametric methodology in order to quantify integrated risk. We observe a wide divergence in measured VaR, diversification benefits as well as the sampling variation across different risk aggregation methodologies and types of institutions (the five largest banks by book value of assets as of 4Q08), We observe in our sample that, contrary to asymptotic theory, empirical copula simulation (ECS) tends to produce the highest absolute magnitudes of VaR relative to standard copula formulations (e.g. Gaussian copula simulation - GCS) on the order of about 20% to 30%; while the variance-covariance approximation (VCA) tends to under-state risk. The proportional diversification benefits, as measured by the relative VaR reduction vis a vis the assumption of perfect correlation, exhibit wide variation across banks and aggregation techniques. The ECS generally yields the highest values relative the other methodologies (127% to 243%), while the Archimadean Gumbel copula (AGCS) is the lowest (10-21%). We conclude that while the ECS may over-state absolute risk relative, proportional diversification benefits may be understated by standard methodologies on the order of about 15% to 30%. Through differences observed across the five largest banks, we fail to find the effect of business mix to exert a directionally consistent an impact on total integrated diversification benefits. In the GOF tests, we find mixed results, that in many cases most of the copula methods exhibit poor fit to the data relative to the ECS, with the Archimadean copulas fitting worse than the Gaussian or Student-T copulas. In a bootstrapping experiment, in which we are able to measure the inherent variability in the VaR integrated risk and proportional diversification benefit measures, we find the variability of the VaR to be significantly lowest (highest) for the ECS (VCA) as compared to other copula formulations. We also find that the contribution of the sampling error in the parameters of the marginal distributions to be an order or magnitude greater than that of the correlation matrices. Given the conservatism and stability of the ECS methodology, the poor performance of the standard methodologies in GOF analysis, and the lack of consensus upon the best copula to use, we believe that we have an argument for consideration of the ECS method.

This paper re-examines the performance of REITs, stocks, and fixed-income assets based on the preferences of risk-averse and risk-seeking investors using mean-variance and stochastic dominance approaches. Our findings indicate no first-order stochastic dominance and no arbitrage opportunity among these assets. However, our stochastic dominance results reveal that in order to maximize their expected utility, the risk-averse prefer fixed-income assets over real estate, which, in turn, is preferable to stocks. On the other hand, to maximize their expected utility, all risk-seeking investors would prefer to invest in stocks than in real estate, but real estate, in turn, is preferable to fixed-income assets.