The London health inequalities forecast: Technical document

This paper describes the data and methods used in the London health inequalities forecast: A briefing on inequalities in life expectancy and deaths from cancers, heart disease and stroke in London. Links to relevant data sources and further information are also provided where possible.

The London health inequalities forecast: A briefing on inequalities in life expectancy and deaths from cancers, heart disease and stroke in London

This briefing considers the national health inequalities targets which must be met by 2010. The targets include those set for heart disease and stroke, cancers and life expectancy.

Intronic variants in the NFKB1 gene may influence hearing forecast in patients with unilateral sensorineural hearing loss in Meniere's disease.

Meniere's disease is an episodic vestibular syndrome associated with sensorineural hearing loss (SNHL) and tinnitus. Patients with MD have an elevated prevalence of several autoimmune diseases (rheumatoid arthritis, systemic lupus erythematosus, ankylosing spondylitis and psoriasis), which suggests a shared autoimmune background. Functional variants of several genes involved in the NF-κB pathway, such as REL, TNFAIP3, NFKB1 and TNIP1, have been associated with two or more immune-mediated diseases and allelic variations in the TLR10 gene may influence bilateral affectation and clinical course in MD. We have genotyped 716 cases of MD and 1628 controls by using the ImmunoChip, a high-density genotyping array containing 186 autoimmune loci, to explore the association of immune system related-loci with sporadic MD. Although no single nucleotide polymorphism (SNP) reached a genome-wide significant association (p<10(-8)), we selected allelic variants in the NF-kB pathway for further analyses to evaluate the impact of these SNPs in the clinical outcome of MD in our cohort. None of the selected SNPs increased susceptibility for MD in patients with uni or bilateral SNHL. However, two potential regulatory variants in the NFKB1 gene (rs3774937 and rs4648011) were associated with a faster hearing loss progression in patients with unilateral SNHL. So, individuals with unilateral MD carrying the C allele in rs3774937 or G allele in rs4648011 had a shorter mean time to reach hearing stage 3 (>40 dB HL) (log-rank test, corrected p values were p = 0.009 for rs3774937 and p = 0.003 for rs4648011, respectively). No variants influenced hearing in bilateral MD. Our data support that the allelic variants rs3774937 and rs4648011 can modify hearing outcome in patients with MD and unilateral SNHL.

Fiscal foresight, forecast revisions and the effects of government spending in the open economy

This paper investigates the effects of government spending on the real exchange rate and the trade balance in the US using a new VAR identification procedure based on spending forecast revisions. I find that the real exchange rate appreciates and the trade balance deteriorates after a government spending shock, although the effects are quantitatively small. The findings broadly match the theoretical predictions of the standard Mundell-Fleming model and differ substantially from those existing in literature. Differences are attributable to the fact that, because of fiscal foresight, the government spending is non-fundamental for the variables typically used in open economy VARs. Here, on the contrary, the estimated shock is fundamental.

Volatility smile extrapolation with an artificial neural network

I use a multi-layer feedforward perceptron, with backpropagation learning implemented via stochastic gradient descent, to extrapolate the volatility smile of Euribor derivatives over low-strikes by training the network on parametric prices.

Iowa Prison Population Forecast FY 2003-2013

CJJP takes a look at the forecast of inmates population in the state of Iowa in a ten year period. Information was produced by Division of Criminal and Juvenile Justice Planning. This report was made possible partially through funding from the U.S. Department of Justice, Bureau of Justice Statistics and its program for State Statistical Analysis Centers. Points of view or opinions expressed in this report are those of the Division of Criminal and Juvenile Justice Planning (CJJP), and do not necessarily reflect official positions of the U.S. Department of Justice.

Iowa Prison Population Forecast FY 2002-2012

CJJP takes a look at the forecast of inmates population in the state of Iowa in a ten year period. Information was produced by Division of Criminal and Juvenile Justice Planning. This report was made possible partially through funding from the U.S. Department of Justice, Bureau of Justice Statistics and its program for State Statistical Analysis Centers. Points of view or opinions expressed in this report are those of the Division of Criminal and Juvenile Justice Planning (CJJP), and do not necessarily reflect official positions of the U.S. Department of Justice.

Iowa Prison Population Forecast, FY 2002-2011

CJJP takes a look at the forecast of inmates population in the state of Iowa in a ten year period. Information was produced by Division of Criminal and Juvenile Justice Planning. This report was made possible partially through funding from the U.S. Department of Justice, Bureau of Justice Statistics and its program for State Statistical Analysis Centers. Points of view or opinions expressed in this report are those of the Division of Criminal and Juvenile Justice Planning (CJJP), and do not necessarily reflect official positions of the U.S. Department of Justice.

Iowa Prison Population Forecast FY 2001-2010

CJJP takes a look at the forecast of inmates population in the state of Iowa in a ten year period. Information was produced by Division of Criminal and Juvenile Justice Planning. This report was made possible partially through funding from the U.S. Department of Justice, Bureau of Justice Statistics and its program for State Statistical Analysis Centers. Points of view or opinions expressed in this report are those of the Division of Criminal and Juvenile Justice Planning (CJJP), and do not necessarily reflect official positions of the U.S. Department of Justice.

Iowa Prison Population Forecast FY 2000-2009

CJJP takes a look at the forecast of inmates population in the state of Iowa in a ten year period. Information was produced by Division of Criminal and Juvenile Justice Planning. This report was made possible partially through funding from the U.S. Department of Justice, Bureau of Justice Statistics and its program for State Statistical Analysis Centers. Points of view or opinions expressed in this report are those of the Division of Criminal and Juvenile Justice Planning (CJJP), and do not necessarily reflect official positions of the U.S. Department of Justice.

Calibration of stochastic volatility models via second order approximation: the Heston model case

Using a suitable Hull and White type formula we develop a methodology to obtain asecond order approximation to the implied volatility for very short maturities. Using thisapproximation we accurately calibrate the full set of parameters of the Heston model. Oneof the reasons that makes our calibration for short maturities so accurate is that we alsotake into account the term-structure for large maturities. We may say that calibration isnot "memoryless", in the sense that the option's behavior far away from maturity doesinfluence calibration when the option gets close to expiration. Our results provide a wayto perform a quick calibration of a closed-form approximation to vanilla options that canthen be used to price exotic derivatives. The methodology is simple, accurate, fast, andit requires a minimal computational cost.

Option pricing under stochastic volatility and stochastic interest rate in the Spanish case

Among the underlying assumptions of the Black-Scholes option pricingmodel, those of a fixed volatility of the underlying asset and of aconstantshort-term riskless interest rate, cause the largest empirical biases. Onlyrecently has attention been paid to the simultaneous effects of thestochasticnature of both variables on the pricing of options. This paper has tried toestimate the effects of a stochastic volatility and a stochastic interestrate inthe Spanish option market. A discrete approach was used. Symmetricand asymmetricGARCH models were tried. The presence of in-the-mean and seasonalityeffectswas allowed. The stochastic processes of the MIBOR90, a Spanishshort-terminterest rate, from March 19, 1990 to May 31, 1994 and of the volatilityofthe returns of the most important Spanish stock index (IBEX-35) fromOctober1, 1987 to January 20, 1994, were estimated. These estimators wereused onpricing Call options on the stock index, from November 30, 1993 to May30, 1994.Hull-White and Amin-Ng pricing formulas were used. These prices werecomparedwith actual prices and with those derived from the Black-Scholesformula,trying to detect the biases reported previously in the literature. Whereasthe conditional variance of the MIBOR90 interest rate seemed to be freeofARCH effects, an asymmetric GARCH with in-the-mean and seasonalityeffectsand some evidence of persistence in variance (IEGARCH(1,2)-M-S) wasfoundto be the model that best represent the behavior of the stochasticvolatilityof the IBEX-35 stock returns. All the biases reported previously in theliterature were found. All the formulas overpriced the options inNear-the-Moneycase and underpriced the options otherwise. Furthermore, in most optiontrading, Black-Scholes overpriced the options and, because of thetime-to-maturityeffect, implied volatility computed from the Black-Scholes formula,underestimatedthe actual volatility.

Inflation, political instability and stockmarket volatility in interwar Germany

What determined the volatility of asset prices in Germany between thewars? This paper argues that the influence of political factors has beenoverstated. The majority of events increasing political uncertainty hadlittle or no effect on the value of German assets and the volatility ofreturns on them. Instead, it was inflation (and the fear of it) that islargely responsible for most of the variability in asset returns.

A general decomposition formula for derivative prices in stochastic volatility models

We see that the price of an european call option in a stochastic volatilityframework can be decomposed in the sum of four terms, which identifythe main features of the market that affect to option prices: the expectedfuture volatility, the correlation between the volatility and the noisedriving the stock prices, the market price of volatility risk and thedifference of the expected future volatility at different times. We alsostudy some applications of this decomposition.